From 41806cd1488e87364fa47885744a7825f4e4ee8c Mon Sep 17 00:00:00 2001
From: kioxli <162790769+kioxli@users.noreply.github.com>
Date: Mon, 12 Oct 2026 23:09:55 -0400
Subject: [PATCH 01/19] feat(demo): add Franka volume-map DFSPH pouring demo

---
 .../embodiments/franka_panda_firm_grip.yaml   |  88 +++
 .../franka_panda_firm_grip_newton.yaml        |  91 +++
 .../trajectory_open_loop_gentle.yaml          |  29 +
 .../trajectory_open_loop_pour_cup.yaml        |  29 +
 .../manipulation/tableware/pour_cup/README.md | 190 ++++++
 .../tableware/pour_cup/assets/cup.glb         | Bin 0 -> 23244 bytes
 .../tableware/pour_cup/env.newton.yaml        | 116 ++++
 .../manipulation/tableware/pour_cup/env.yaml  |  90 +++
 .../pour_cup/task.franka.newton.yaml          |  26 +
 .../tableware/pour_cup/task.franka.yaml       |  16 +
 .../task_program/integration.newton.yaml      |  67 ++
 .../pour_cup/task_program/integration.yaml    |  67 ++
 .../pour_cup/task_program/program.newton.yaml |  40 ++
 .../pour_cup/task_program/program.yaml        |  40 ++
 .../sim/assets/franka_dfsph_pour/.gitignore   |   6 +
 .../sim/assets/franka_dfsph_pour/README.md    |  41 ++
 .../sim/assets/franka_dfsph_pour/close_23.png | Bin 0 -> 421745 bytes
 .../assets/franka_dfsph_pour/environment.json |  30 +
 .../sim/assets/franka_dfsph_pour/frame_15.png | Bin 0 -> 750331 bytes
 .../assets/franka_dfsph_pour/manifest.json    |  54 ++
 .../franka_dfsph_pour/media_validation.json   | 439 ++++++++++++++
 .../newton_smooth_validation.json             |  97 +++
 .../assets/franka_dfsph_pour/particles_30.npz | Bin 0 -> 1655413 bytes
 .../pr_smoke_validation.json                  |  73 +++
 .../settling_comparison.json                  |  44 ++
 .../assets/franka_dfsph_pour/trajectory.pt    | Bin 0 -> 301077 bytes
 .../assets/franka_dfsph_pour/validation.json  | 446 ++++++++++++++
 .../water_settling_closeup.mp4                | Bin 0 -> 1414937 bytes
 .../tutorials/sim/franka_dfsph_pour_demo.py   | 571 ++++++++++++++++++
 tests/gym/envs/test_official_task_layout.py   |   1 +
 30 files changed, 2691 insertions(+)
 create mode 100644 embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip.yaml
 create mode 100644 embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip_newton.yaml
 create mode 100644 embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_gentle.yaml
 create mode 100644 embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_pour_cup.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/cup.glb
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.newton.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.newton.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.yaml
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/.gitignore
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/README.md
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/close_23.png
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/environment.json
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/frame_15.png
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/manifest.json
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/media_validation.json
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/newton_smooth_validation.json
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/particles_30.npz
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/pr_smoke_validation.json
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/settling_comparison.json
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/trajectory.pt
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/validation.json
 create mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/water_settling_closeup.mp4
 create mode 100644 scripts/tutorials/sim/franka_dfsph_pour_demo.py

diff --git a/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip.yaml b/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip.yaml
new file mode 100644
index 000000000..90bacd060
--- /dev/null
+++ b/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip.yaml
@@ -0,0 +1,88 @@
+embodiment_id: franka_panda_firm_grip
+simulation:
+  class_type: FrankaPanda
+  robot_type: panda
+  uid: FrankaPanda
+  joint_drive_props:
+    drive_type: force
+    stiffness:
+      fr3_joint[1-7]: 10000.0
+      fr3_finger_joint[1-2]: 10000.0
+    damping:
+      fr3_joint[1-7]: 1000.0
+      fr3_finger_joint[1-2]: 100.0
+    max_effort:
+      fr3_joint[1-7]: 100000.0
+      fr3_finger_joint[1-2]: 80.0
+  link_attrs:
+    # Override the existing finger selector with Default-backend contact properties.
+    newton_gripper_contacts:
+      link_names_expr: [fr3_leftfinger, fr3_rightfinger]
+      attrs:
+        collision_props:
+          backend: default
+          torsional_patch_radius: 0.009
+          min_torsional_patch_radius: 0.008
+        material_props:
+          static_friction: 1.0
+          dynamic_friction: 1.0
+sensor:
+- sensor_type: Camera
+  uid: cam_high
+  width: 640
+  height: 480
+  intrinsics:
+  - 488.1665
+  - 488.1665
+  - 320.0
+  - 240.0
+  extrinsics:
+    eye:
+    - -0.5
+    - -0.9
+    - 0.88
+    target:
+    - -0.5
+    - 0.1
+    - 0.2
+    up:
+    - 0.0
+    - 0.0
+    - 1.0
+skill_profile:
+  contract_id: single_arm_parallel_gripper
+  profile_id: franka_panda_firm_grip
+  resources:
+  - resource_id: primary_manipulator
+    endpoints:
+    - endpoint_id: motion
+      control_part: arm
+      capabilities:
+      - kinematics.batch_inverse
+      - motion.cartesian_pose
+      - kinematics.forward
+    - endpoint_id: grasp
+      control_part: eef
+      capabilities:
+      - interaction.grasp
+      command_preset: parallel_jaw_commands
+  command_presets:
+  - preset_id: parallel_jaw_commands
+    control_part: eef
+    commands:
+      open:
+      - 0.04
+      - 0.04
+      grasp:
+      - 0.0
+      - 0.0
+  runtime_services:
+    grasp_pose_generators:
+      eef:
+        kind: antipodal_parallel_jaw
+        sample_count: 1000
+        opening_margin: 0.002
+        point_sample_density: 0.012
+        filter_ground_collision: false
+        model:
+          model_id: franka_panda_hand
diff --git a/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip_newton.yaml b/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip_newton.yaml
new file mode 100644
index 000000000..216e95d37
--- /dev/null
+++ b/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip_newton.yaml
@@ -0,0 +1,91 @@
+embodiment_id: franka_panda_firm_grip_newton
+simulation:
+  class_type: FrankaPanda
+  robot_type: panda
+  uid: FrankaPanda
+  joint_drive_props:
+    drive_type: force
+    stiffness:
+      fr3_joint[1-7]: 10000.0
+      fr3_finger_joint[1-2]: 8000.0
+    damping:
+      fr3_joint[1-7]: 1000.0
+      fr3_finger_joint[1-2]: 100.0
+    max_effort:
+      fr3_joint[1-7]: 100000.0
+      fr3_finger_joint[1-2]: 80.0
+  link_attrs:
+    # Configure the existing finger selector for MuJoCo torsional and rolling contacts.
+    newton_gripper_contacts:
+      link_names_expr: [fr3_leftfinger, fr3_rightfinger]
+      attrs:
+        collision_props:
+          backend: newton
+          condim: 6
+        material_props:
+          backend: newton
+          dynamic_friction: 1.0
+          ke: 400000.0
+          kd: 1265.0
+          torsional_friction: 0.1
+          rolling_friction: 0.03
+sensor:
+- sensor_type: Camera
+  uid: cam_high
+  width: 640
+  height: 480
+  intrinsics:
+  - 488.1665
+  - 488.1665
+  - 320.0
+  - 240.0
+  extrinsics:
+    eye:
+    - -0.5
+    - -0.9
+    - 0.88
+    target:
+    - -0.5
+    - 0.1
+    - 0.2
+    up:
+    - 0.0
+    - 0.0
+    - 1.0
+skill_profile:
+  contract_id: single_arm_parallel_gripper
+  profile_id: franka_panda_firm_grip_newton
+  resources:
+  - resource_id: primary_manipulator
+    endpoints:
+    - endpoint_id: motion
+      control_part: arm
+      capabilities:
+      - kinematics.batch_inverse
+      - motion.cartesian_pose
+      - kinematics.forward
+    - endpoint_id: grasp
+      control_part: eef
+      capabilities:
+      - interaction.grasp
+      command_preset: parallel_jaw_commands
+  command_presets:
+  - preset_id: parallel_jaw_commands
+    control_part: eef
+    commands:
+      open:
+      - 0.04
+      - 0.04
+      grasp:
+      - 0.0
+      - 0.0
+  runtime_services:
+    grasp_pose_generators:
+      eef:
+        kind: antipodal_parallel_jaw
+        sample_count: 1000
+        opening_margin: 0.002
+        point_sample_density: 0.012
+        filter_ground_collision: false
+        model:
+          model_id: franka_panda_hand
diff --git a/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_gentle.yaml b/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_gentle.yaml
new file mode 100644
index 000000000..56835899e
--- /dev/null
+++ b/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_gentle.yaml
@@ -0,0 +1,29 @@
+policy_id: trajectory_open_loop_gentle_v1
+preset_id: trajectory
+
+requires:
+  embodiment_contract: single_arm_parallel_gripper
+
+motion:
+  strategy: ik_interp
+  sample_count: 360
+  dynamic_collision_mode: "off"
+
+tracking:
+  kind: timed
+  settle_duration: 0.4
+
+recovery:
+  max_replans: 0
+  max_action_retries: 0
+  goal_rotation_threshold: 3.141592653589793
+
+workflow_recovery:
+  max_recovery_attempts: 0
+
+runner:
+  minimum_cycle_time: 0.0
+  hold_on_completion: true
+  hold_during_effect_verification: false
+
+effect_assurance: projected
diff --git a/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_pour_cup.yaml b/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_pour_cup.yaml
new file mode 100644
index 000000000..2ee9b3f6f
--- /dev/null
+++ b/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_pour_cup.yaml
@@ -0,0 +1,29 @@
+policy_id: trajectory_open_loop_pour_cup_v1
+preset_id: trajectory
+
+requires:
+  embodiment_contract: single_arm_parallel_gripper
+
+motion:
+  strategy: ik_interp
+  sample_count: 240
+  dynamic_collision_mode: "off"
+
+tracking:
+  kind: timed
+  settle_duration: 0.4
+
+recovery:
+  max_replans: 0
+  max_action_retries: 0
+  goal_rotation_threshold: 3.141592653589793
+
+workflow_recovery:
+  max_recovery_attempts: 0
+
+runner:
+  minimum_cycle_time: 0.0
+  hold_on_completion: true
+  hold_during_effect_verification: false
+
+effect_assurance: projected
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
new file mode 100644
index 000000000..71c0a9ed4
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
@@ -0,0 +1,190 @@
+# Franka 单臂马克杯倒水 demo
+
+使用真实 Franka Panda 机型的仿真模型，从侧面夹住正常大小的马克杯杯把，
+抬杯、向碗里倒水、回正并放回。这里包含两个入口：
+
+- **空杯 Task Program**：只用 YAML 组合现有 `Pick → simulation.pour → Place`，
+  分别提供 Default 和 Newton + MuJoCo-Warp 部署。
+- **DFSPH 带水演示**：教程脚本回放已验证的 MuJoCo 机械臂和杯子轨迹，
+  同时通过 EmbodiChain 的 Newton 后端模拟液体。
+
+这是临时演示，未修改 `embodichain/` 生产代码。液体采用单向耦合：移动杯壁
+影响水，水不反作用于机械臂；DFSPH 场景不重新求解夹持接触。
+
+## 直接运行带水演示
+
+从源码仓库根目录，在已安装 EmbodiChain 和兼容 DexSim 的环境中运行：
+
+```bash
+python scripts/tutorials/sim/franka_dfsph_pour_demo.py --headless
+```
+
+脚本默认读取随本分支提交的
+[`trajectory.pt`](../../../../../../scripts/tutorials/sim/assets/franka_dfsph_pour/trajectory.pt)，
+不需要先生成轨迹。去掉 `--headless` 可打开原生窗口。
+短时检查或指定自行生成的轨迹：
+
+```bash
+python scripts/tutorials/sim/franka_dfsph_pour_demo.py \
+  --headless --seconds 2 --output outputs/franka_pour_cup/smoke
+
+python scripts/tutorials/sim/franka_dfsph_pour_demo.py --headless \
+  --trajectory outputs/franka_pour_cup/newton_smooth_mug_trajectories/traj_env0_000000.pt
+```
+
+本机验证使用 Python 3.10、DexSim 0.5.1 本地 wheel、Newton 1.6.0、
+MuJoCo / MuJoCo-Warp 3.12.0、Warp 1.17.0 和 NVIDIA RTX 5090 D v2。
+还需要 Torch、TensorDict、NumPy、SciPy、Trimesh、OpenCV、ImageIO 和
+ImageIO-FFmpeg。完整版本记录见
+[`environment.json`](../../../../../../scripts/tutorials/sim/assets/franka_dfsph_pour/environment.json)。
+DexSim 必须包含原生 SPH、volume map、液体网格重建和 OptiX 支持；
+本地 wheel 和虚拟环境不随分支提交，也未更改项目的全局依赖。
+本演示针对源码 checkout，不承诺随 wheel 打包二进制资产。
+
+Franka 模型沿用 EC 现有资产解析；杯子网格随任务提交，接水碗由脚本生成。
+默认输出目录是 `outputs/franka_pour_cup/dfsph_stable_bowl/`，包括全景/近景
+MP4、每秒关键帧、粒子位置和速度 NPZ、参数及统计 `validation.json`。
+已录制的最终结果见
+[演示资料目录](../../../../../../scripts/tutorials/sim/assets/franka_dfsph_pour/README.md)，
+包含三段视频、原始轨迹、最终粒子快照和验证报告。
+
+## 场景和求解器
+
+| 项目 | 配置 |
+| --- | --- |
+| 马克杯 | 杯口外径 8.5 cm、高 10 cm、壁厚 4 mm、底厚 6 mm |
+| 杯子容量和重量 | 几何满口容量约 362 mL，空杯 300 g |
+| 杯把 | 平侧面截面 12 × 12 mm，总宽约 11.33 cm |
+| 接水碗 | 弧形内壁、平底，口径约 18.9 cm、高 6.6 cm |
+| 碗中心 | `[-0.755, -0.137, 0.50]` m |
+| 液体 | 76,538 粒、间距 1.5 mm、初始名义水量约 258 mL |
+| 边界 | volume map，最大网格分辨率 192，额外 SDF 惩罚力关闭 |
+| 物理更新 | 1440 Hz，每步 1 个子步 |
+| DFSPH | 散度迭代上限 10、密度迭代上限 20、压力松弛 0.2 |
+| 液体参数 | 密度 1000 kg/m³、黏性系数 0.0006、离散表面张力 0.002 |
+| 录制 | 24 fps，全景 1280 × 960，近景 960 × 720 |
+
+体积按粒子初始体积估算。液体参数为演示调参，未做材料物性标定。
+`--spacing`、`--volume-map-resolution`、`--viscosity` 和 `--surface-tension`
+可调整对应参数。默认轨迹两倍速，首尾各静置 1 秒，共 30.975 秒仿真。
+
+杯子位姿、线速度和角速度每个物理步更新。机器人关节与杯子位置用 PCHIP
+插值，杯子姿态用 SLERP。volume map 从容器网格 SDF 烘焙，保留表面投影；
+`volume_map_boundary_enabled=True`，`sdf_boundary_enabled=False`。
+为适配 Newton 1.6，使用显式预烘焙 `force_sdf=False` 和 `approximation=SDF`。
+
+脚本参考 DexSim 的
+`examples/python/physics/particle_fluid/demo_newton_sph_dam_break_optix.py`，
+通过 `SimulationManager` / `NewtonPhysicsCfg` 运行
+`SPHSolverCfg(sph_method="dfsph")`。临时接入直接访问
+`sim._spawn_scene.builder` 和 Newton 后端 `render_sync.sync()`；
+这不是正式的公共液体 API。
+
+## 碗内稳定性处理
+
+旧版在碗内有持续的高频运动，散落粒子还会把重建范围拉大，使水面变粗或消失。
+本版增加黏性耗散、减小离散表面张力、提高 volume map 分辨率，并把碗朝水流
+落点平移 3.5 cm。重建使用 `aabb_trim_fraction=0.0`；每帧单独复制渲染位置，
+把 `z < 0.49 m` 的点标为非有限值，由原生重建器过滤。
+
+此过滤只作用于渲染副本，不回写求解器。全部粒子仍参与物理、保存和统计，
+没有冻结水面或删除散落粒子。水流和碗内液面使用 OptiX 光追及表面网格重建。
+
+最终完整运行结果：
+
+| 最终位置 | 粒子数 | 名义水量 |
+| --- | ---: | ---: |
+| 碗内 | 41,594 | 140.38 mL |
+| 杯内 | 34,698 | 117.11 mL |
+| 容器外 | 246 | 0.83 mL |
+
+散落比例为 0.32%，不是无泄漏仿真。第 19 秒至结束的每秒及最终快照中，
+碗内速度均方根平均 **5.05 mm/s**、最大 5.87 mm/s；99 分位水面高度极差
+约 **0.074 mm**。静水对照在相同旧版快照、相同零初速度及固定容器下各运行
+5 秒，速度均方根从 0.13404 m/s 降到 0.004749 m/s（降低 96.46%）；
+这是特定对照结果，不是完整倒水全过程的统一降幅。
+
+两份完整视频各 744 帧、31 秒，18 秒静置近景片段为 432 帧，均逐帧解码通过。
+31 份粒子快照的数量均为 76,538，位置和速度均有限。
+这些是一次完整运行和离散快照的结果，不代表批量成功率。
+已提交最终快照和全部统计，其他每秒快照可由运行脚本重新生成。
+
+## 空杯任务及轨迹生成
+
+首次运行 YAML 部署前，把杯子放入本机资产缓存；若设置了
+`EMBODICHAIN_DATA_ROOT`，改用该目录：
+
+```bash
+mkdir -p ~/.cache/embodichain_data/PourCupDemo
+cp embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/cup.glb \
+  ~/.cache/embodichain_data/PourCupDemo/cup.glb
+```
+
+Newton + MuJoCo-Warp：
+
+```bash
+embodichain run-env \
+  --gym-config embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml \
+  --headless --renderer hybrid --record_trajectory \
+  --trajectory_save_dir outputs/franka_pour_cup/newton_smooth_mug_trajectories
+```
+
+Default：
+
+```bash
+embodichain run-env \
+  --gym-config embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml \
+  --headless --renderer hybrid --record_trajectory \
+  --trajectory_save_dir outputs/franka_pour_cup/mug_trajectories
+```
+
+YAML 部署均为空杯任务，不直接加载 DFSPH 或碗。
+Newton 版杯心 `[-0.75, 0, 0.552]` m，机械臂基座在原点、朝向 180°。
+50 cm 高的支撑台与侧抓预备姿态减少了腕部折叠；夹爪沿世界 -X 方向接近，
+两指沿 Y 方向夹住杯把，TCP 距杯心朝机械臂方向 6.5 cm，抬升约 12 cm。
+Default 版保留地面布局和约 20 cm 抬升。
+
+Newton 配置使用 `mujoco_warp`、`solver: newton`、`implicitfast`，
+5 ms 物理步分成 50 个子步；杯子和指腹使用 `condim: 6` 的抗扭/滚动摩擦。
+运动学继续使用 FEP IK 和 `ik_interp`，没有新增 IK 或动作实现。
+两种执行策略均为 `projected`，程序完成须结合实际杯子位姿和录像判断。
+
+随分支提交的 MuJoCo 轨迹来自固定种子 0 的一次完整成功运行，1160 帧、20 Hz，
+包含侧抓、抬升、倾倒、回正、放回和松爪。相对优化前布局，累计关节转动量
+从 19.23 rad 降到 6.03 rad（减少 68.7%），最大关节速度从 0.473 rad/s
+降到 0.163 rad/s。杯子最大抬升 11.98 cm、实际最大倾角 66.09°，
+最终倾斜 0.28°、放回误差 2.09 mm。实际杯子倾角小于 80° 指令，
+夹持中仍有相对转动。详细记录见资料目录的 `newton_smooth_validation.json`。
+
+## YAML 分工
+
+两种后端各使用 6 份 YAML，共 12 份：
+
+| 配置 | 内容 |
+| --- | --- |
+| `task.franka.yaml` / `task.franka.newton.yaml` | 运行入口、种子、环境/机器人/程序/策略选择、初始姿态 |
+| `env.yaml` / `env.newton.yaml` | 物理后端、杯子和台面、接触参数、灯光、录制和复位 |
+| `task_program/program*.yaml` | Pick → Pour → Place、放回目标、稳定等待与终点验证 |
+| `task_program/integration*.yaml` | 杯子语义绑定、侧抓位姿、抬升距离、80° 倾倒指令 |
+| `components/embodiments/franka_panda_firm_grip*.yaml` | 原 Franka 模型、传感器、夹爪力度、接触参数和技能声明 |
+| `components/execution_policies/trajectory_open_loop_pour_cup.yaml` / `trajectory_open_loop_gentle.yaml` | IK 插值，分别 240 / 360 点，动作后稳定 0.4 秒 |
+
+组件路径以 `embodichain_tasks/configs/` 为根。为适配当前主分支，夹爪控制组
+使用 `eef`；Default 策略单独命名，避免覆盖现有共享慢速策略。
+机器人 link / joint 资产名保持原值。
+
+## 验证和范围
+
+静态检查两个部署：
+
+```bash
+python .agents/skills/add-task-program/scripts/inspect_deployment.py \
+  embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml \
+  embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml
+```
+
+本分支新增任务配置、教程、资产和演示证据，EC 生产代码与公共 API 未变。
+没有新建教程单元测试；使用部署检查、现有配置/资源测试、语法检查和 GPU
+短测验证。完整演示最初在提交 `3bb2a03c5` 上录制，分支基于 `453675d8a` 整理；
+当前分支的检查结果单独记录在 PR，不能把原录像当作新基线的完整复跑。
+现有上下文已覆盖配置和模拟器接入边界，无需增加架构文档。
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/cup.glb b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/cup.glb
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diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
new file mode 100644
index 000000000..8a083eabf
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
@@ -0,0 +1,116 @@
+environment_id: pour_cup
+physics: newton
+max_episodes: 1
+max_episode_steps: 1400
+num_envs: 1
+arena_space: 2.5
+
+physics_config:
+  physics_dt: 0.005
+  gravity: [0.0, 0.0, -9.81]
+  # Each 5 ms physics step contains fifty 0.1 ms MuJoCo solver steps.
+  num_substeps: 50
+  collision_cfg: null
+  solver_cfg:
+    solver_type: mujoco_warp
+    solver: newton
+    integrator: implicitfast
+    iterations: 50
+    ls_iterations: 100
+    cone: elliptic
+    impratio: 1000.0
+    use_mujoco_contacts: true
+    enable_multiccd: true
+
+simulation:
+  light:
+    direct:
+      - uid: main_light
+        light_type: sun
+        color: [1.0, 1.0, 1.0]
+        intensity: 4.0
+        direction: [-0.3, 0.2, -1.0]
+      - uid: fill_light
+        light_type: rect
+        color: [1.0, 1.0, 1.0]
+        intensity: 6.0
+        init_pos: [-0.5, -0.4, 1.2]
+        direction: [0.0, 0.0, -1.0]
+        rect_width: 0.7
+        rect_height: 0.7
+  background:
+    - uid: cup_platform
+      shape:
+        shape_type: Cube
+        size: [0.4, 0.5, 0.5]
+      body_type: static
+      init_pos: [-0.9, 0.0, 0.25]
+      attrs:
+        collision_props:
+          backend: newton
+          condim: 6
+        material_props:
+          backend: newton
+          dynamic_friction: 1.0
+          ke: 400000.0
+          kd: 1265.0
+          torsional_friction: 0.1
+          rolling_friction: 0.03
+  rigid_object:
+    - uid: cup
+      shape:
+        shape_type: Mesh
+        fpath: PourCupDemo/cup.glb
+        compute_uv: true
+        collision:
+          approximation: convex_decomposition
+          max_hulls: 32
+      body_type: dynamic
+      init_pos: [-0.75, 0.0, 0.552]
+      body_scale: [1.0, 1.0, 1.0]
+      attrs:
+        mass_props:
+          mass: 0.30
+        collision_props:
+          backend: newton
+          contact_offset: 0.0005
+          rest_offset: 0.0001
+          condim: 6
+        material_props:
+          backend: newton
+          dynamic_friction: 1.2
+          ke: 400000.0
+          kd: 1265.0
+          torsional_friction: 0.1
+          rolling_friction: 0.03
+  rigid_object_group: []
+  articulation: []
+
+env:
+  sim_steps_per_control: 10
+  control_parts: [arm, eef]
+  events:
+    settle_cup:
+      func: wait_for_dynamic_objects_to_settle
+      mode: reset
+      params:
+        entity_cfgs:
+          - uid: cup
+        min_steps: 10
+        max_steps: 120
+        check_interval_steps: 2
+        required_stable_checks: 3
+        timeout_behavior: raise
+    record_demo:
+      func: record_camera_data
+      mode: interval
+      interval_step: 1
+      params:
+        name: franka_newton_smooth_pour_mug
+        resolution: [1280, 960]
+        intrinsics: [1000.0, 1000.0, 640.0, 480.0]
+        eye: [-1.4, -1.5, 1.15]
+        target: [-0.4, 0.0, 0.45]
+        up: [0.0, 0.0, 1.0]
+        save_path: outputs/franka_pour_cup/videos
+  dataset: {}
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
new file mode 100644
index 000000000..d3991e278
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
@@ -0,0 +1,90 @@
+environment_id: pour_cup
+physics: default
+max_episodes: 1
+max_episode_steps: 1200
+num_envs: 1
+arena_space: 2.5
+
+physics_config:
+  physics_dt: 0.005
+  gravity: [0.0, 0.0, -9.81]
+  enable_ccd: true
+
+simulation:
+  light:
+    direct:
+      - uid: main_light
+        light_type: sun
+        color: [1.0, 1.0, 1.0]
+        intensity: 4.0
+        direction: [-0.3, 0.2, -1.0]
+      - uid: fill_light
+        light_type: rect
+        color: [1.0, 1.0, 1.0]
+        intensity: 6.0
+        init_pos: [-0.5, -0.4, 1.2]
+        direction: [0.0, 0.0, -1.0]
+        rect_width: 0.7
+        rect_height: 0.7
+  background: []
+  rigid_object:
+    - uid: cup
+      shape:
+        shape_type: Mesh
+        fpath: PourCupDemo/cup.glb
+        compute_uv: true
+        collision:
+          approximation: convex_decomposition
+          max_hulls: 32
+      body_type: dynamic
+      init_pos: [-0.485, -0.08, 0.052]
+      body_scale: [1.0, 1.0, 1.0]
+      attrs:
+        mass_props:
+          mass: 0.30
+        rigid_props:
+          linear_damping: 0.2
+          angular_damping: 0.2
+          min_position_iters: 64
+          min_velocity_iters: 16
+          max_depenetration_velocity: 1.0
+        collision_props:
+          contact_offset: 0.0005
+          rest_offset: 0.0001
+          torsional_patch_radius: 0.009
+          min_torsional_patch_radius: 0.008
+        material_props:
+          dynamic_friction: 1.2
+          static_friction: 1.2
+          restitution: 0.0
+  rigid_object_group: []
+  articulation: []
+
+env:
+  sim_steps_per_control: 10
+  control_parts: [arm, eef]
+  events:
+    settle_cup:
+      func: wait_for_dynamic_objects_to_settle
+      mode: reset
+      params:
+        entity_cfgs:
+          - uid: cup
+        min_steps: 10
+        max_steps: 120
+        check_interval_steps: 2
+        required_stable_checks: 3
+        timeout_behavior: raise
+    record_demo:
+      func: record_camera_data
+      mode: interval
+      interval_step: 1
+      params:
+        name: franka_side_pour_mug
+        resolution: [1280, 960]
+        intrinsics: [1000.0, 1000.0, 640.0, 480.0]
+        eye: [-1.1, -1.25, 0.85]
+        target: [-0.22, -0.02, 0.32]
+        up: [0.0, 0.0, 1.0]
+        save_path: outputs/franka_pour_cup/videos
+  dataset: {}
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml
new file mode 100644
index 000000000..675b375c2
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml
@@ -0,0 +1,26 @@
+id: PourCup-Franka-Newton-v1
+seed: 0
+enable_sensor: false
+environment:
+  component: env.newton.yaml
+task_program:
+  program: task_program/program.newton.yaml
+  integration: task_program/integration.newton.yaml
+  execution_policy: ../../../../components/execution_policies/trajectory_open_loop_gentle.yaml
+embodiment:
+  component: ../../../../components/embodiments/franka_panda_firm_grip_newton.yaml
+  overrides:
+    init_rot:
+    - 0.0
+    - 0.0
+    - 180.0
+    init_qpos:
+    - 0.0023885604459792376
+    - -0.09648692607879639
+    - -0.0022385704796761274
+    - -2.388152599334717
+    - 0.00024301925441250205
+    - 3.862462282180786
+    - 0.7850000262260437
+    - 0.04
+    - 0.04
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml
new file mode 100644
index 000000000..c4d180c8f
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml
@@ -0,0 +1,16 @@
+id: PourCup-Franka-v1
+seed: 0
+enable_sensor: false
+environment:
+  component: env.yaml
+task_program:
+  program: task_program/program.yaml
+  integration: task_program/integration.yaml
+  execution_policy: ../../../../components/execution_policies/trajectory_open_loop_pour_cup.yaml
+embodiment:
+  component: ../../../../components/embodiments/franka_panda_firm_grip.yaml
+  overrides:
+    init_rot:
+    - 0.0
+    - 0.0
+    - 180.0
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.newton.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.newton.yaml
new file mode 100644
index 000000000..43db3b170
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.newton.yaml
@@ -0,0 +1,67 @@
+integration_id: pour_cup_franka_newton_v1
+program_id: pick_pour_return_cup
+
+requires:
+  scene_contract: pour_cup_scene_v1
+  embodiment_contract: single_arm_parallel_gripper
+
+scene_binding:
+  contract_id: pour_cup_scene_v1
+  registry_id: task_program_pour_cup
+  rigid_objects:
+    - entity_id: cup
+      simulation_uid: cup
+      dynamics: dynamic
+      semantic_type: cup
+      affordances:
+        - entity_id: cup_grasp
+          kind: antipodal_grasp
+          internal_axis: [1.0, 0.0, 0.0]
+
+profile:
+  defaults:
+    pick_up:
+      primary: primary_manipulator
+    pour:
+      primary: primary_manipulator
+    place:
+      primary: primary_manipulator
+  action_options:
+    pick:
+      kind: pick_up
+      hand_interp_steps: 20
+      grasp_settle_steps: 20
+      lift_height: 0.12
+      pre_grasp_distance: 0.08
+      approach_direction: [-1.0, 0.0, 0.0]
+      fixed_object_to_eef:
+        - 0.0
+        - 0.0
+        - -1.0
+        - 0.065
+        - 0.0
+        - 1.0
+        - 0.0
+        - 0.0
+        - 1.0
+        - 0.0
+        - 0.0
+        - 0.0
+        - 0.0
+        - 0.0
+        - 0.0
+        - 1.0
+    simulation.pour:
+      kind: pour
+      rotate_angle: 1.3962634015954636
+    place:
+      kind: place
+      hand_interp_steps: 20
+      release_settle_steps: 20
+      preserve_current_object_orientation: false
+  effect_monitors: {}
+
+runtime_services:
+  registered_semantic_lowerers:
+    - kind: pour
+      object_id: cup
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.yaml
new file mode 100644
index 000000000..50308ae4f
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.yaml
@@ -0,0 +1,67 @@
+integration_id: pour_cup_franka_v1
+program_id: pick_pour_return_cup
+
+requires:
+  scene_contract: pour_cup_scene_v1
+  embodiment_contract: single_arm_parallel_gripper
+
+scene_binding:
+  contract_id: pour_cup_scene_v1
+  registry_id: task_program_pour_cup
+  rigid_objects:
+    - entity_id: cup
+      simulation_uid: cup
+      dynamics: dynamic
+      semantic_type: cup
+      affordances:
+        - entity_id: cup_grasp
+          kind: antipodal_grasp
+          internal_axis: [1.0, 0.0, 0.0]
+
+profile:
+  defaults:
+    pick_up:
+      primary: primary_manipulator
+    pour:
+      primary: primary_manipulator
+    place:
+      primary: primary_manipulator
+  action_options:
+    pick:
+      kind: pick_up
+      hand_interp_steps: 20
+      grasp_settle_steps: 20
+      lift_height: 0.20
+      pre_grasp_distance: 0.12
+      approach_direction: [-1.0, 0.0, 0.0]
+      fixed_object_to_eef:
+        - 0.0
+        - 0.0
+        - -1.0
+        - 0.065
+        - 0.0
+        - 1.0
+        - 0.0
+        - 0.0
+        - 1.0
+        - 0.0
+        - 0.0
+        - 0.0
+        - 0.0
+        - 0.0
+        - 0.0
+        - 1.0
+    simulation.pour:
+      kind: pour
+      rotate_angle: 1.3962634015954636
+    place:
+      kind: place
+      hand_interp_steps: 20
+      release_settle_steps: 20
+      preserve_current_object_orientation: false
+  effect_monitors: {}
+
+runtime_services:
+  registered_semantic_lowerers:
+    - kind: pour
+      object_id: cup
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.newton.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.newton.yaml
new file mode 100644
index 000000000..1f77d32e4
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.newton.yaml
@@ -0,0 +1,40 @@
+program_id: pick_pour_return_cup
+targets:
+  cup_return_pose:
+    kind: cyclic_pose
+    values:
+      - position: [-0.75, 0.0, 0.552]
+        quaternion_xyzw: [0.0, 0.0, 0.0, 1.0]
+
+program:
+  kind: segment
+  name: pick_pour_return_cup
+  steps:
+    kind: sequence
+    items:
+      - kind: invoke
+        call:
+          kind: pick
+          object: cup
+          grasp: cup_grasp
+      - kind: invoke
+        call:
+          kind: registered
+          call_id: simulation.pour
+          arguments:
+            object: cup
+      - kind: invoke
+        call:
+          kind: place
+          object: cup
+          at:
+            kind: target_ref
+            target: cup_return_pose
+  post:
+    - kind: wait_stable
+      entity: cup
+  validators:
+    - kind: object_near_target
+      object: cup
+      target: cup_return_pose
+      position_tolerance: 0.03
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.yaml
new file mode 100644
index 000000000..18bf4cb97
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.yaml
@@ -0,0 +1,40 @@
+program_id: pick_pour_return_cup
+targets:
+  cup_return_pose:
+    kind: cyclic_pose
+    values:
+      - position: [-0.485, -0.08, 0.052]
+        quaternion_xyzw: [0.0, 0.0, 0.0, 1.0]
+
+program:
+  kind: segment
+  name: pick_pour_return_cup
+  steps:
+    kind: sequence
+    items:
+      - kind: invoke
+        call:
+          kind: pick
+          object: cup
+          grasp: cup_grasp
+      - kind: invoke
+        call:
+          kind: registered
+          call_id: simulation.pour
+          arguments:
+            object: cup
+      - kind: invoke
+        call:
+          kind: place
+          object: cup
+          at:
+            kind: target_ref
+            target: cup_return_pose
+  post:
+    - kind: wait_stable
+      entity: cup
+  validators:
+    - kind: object_near_target
+      object: cup
+      target: cup_return_pose
+      position_tolerance: 0.03
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/.gitignore b/scripts/tutorials/sim/assets/franka_dfsph_pour/.gitignore
new file mode 100644
index 000000000..4a97e0644
--- /dev/null
+++ b/scripts/tutorials/sim/assets/franka_dfsph_pour/.gitignore
@@ -0,0 +1,6 @@
+!trajectory.pt
+!franka_dfsph_pour.mp4
+!water_closeup.mp4
+!water_settling_closeup.mp4
+!frame_15.png
+!close_23.png
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/README.md b/scripts/tutorials/sim/assets/franka_dfsph_pour/README.md
new file mode 100644
index 000000000..28448ce09
--- /dev/null
+++ b/scripts/tutorials/sim/assets/franka_dfsph_pour/README.md
@@ -0,0 +1,41 @@
+# 已验证的 Franka DFSPH 演示资料
+
+运行说明和边界条件见
+[任务 README](../../../../../embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md)。
+此目录约 31 MB，为 draft 提供可直接回放的输入和可检查的最终输出。
+
+| 文件 | 用途 |
+| --- | --- |
+| `trajectory.pt` | MuJoCo 实际机器人/杯子状态，1160 帧、20 Hz；脚本默认输入 |
+| `franka_dfsph_pour.mp4` | 完整全景，31 秒、744 帧 |
+| `water_closeup.mp4` | 完整杯口/水流/碗近景，31 秒、744 帧 |
+| `water_settling_closeup.mp4` | 近景第 12–30 秒片段，18 秒、432 帧 |
+| `frame_15.png` / `close_23.png` | 倾倒全景与静置后液面 |
+| `particles_30.npz` | 最后时刻 30.975 秒的全部粒子位置、速度、杯位姿 |
+| `validation.json` | DFSPH 参数及全程 32 组物理统计 |
+| `media_validation.json` | 三段视频逐帧解码、31 份原始快照的有限值和数量检查 |
+| `newton_smooth_validation.json` | 轨迹平滑前后指标与原 MuJoCo 任务完成元数据 |
+| `settling_comparison.json` | 相同初始快照的两组静水对照 |
+| `environment.json` | 验证环境版本与提交来源 |
+| `pr_smoke_validation.json` | 当前分支 2 秒 GPU 短测，76,538 粒，容器外粒子数为 0 |
+| `manifest.json` | 资料大小和 SHA-256 |
+
+原始报告保留录制机器的路径作为来源记录，不用作运行输入。
+除最终粒子快照和两张预览外，其余每秒快照/图片未提交；可运行脚本重新生成。
+`media_validation.json` 记录的是当时所有 31 份原始快照的检查结果。
+
+轨迹和完整录像生成于 EC 提交 `3bb2a03c5`，使用本机 DexSim 0.5.1 wheel。
+Draft 基于 `453675d8a`，配置按当前 `eef` 命名调整；此次基线的静态检查与
+GPU 短测另记在 PR。这里的原录像不是新基线重新完整运行的声明。
+
+`trajectory.pt` 是本演示自产的 Torch / TensorDict 序列化文件，包含
+`states`、`actions` 和 `meta`。液体脚本读取 `meta.dt`、机器人 9 维 `qpos`
+和杯子 7 维位姿（XYZ + XYZW），不会依赖原机器的绝对路径。
+
+单向耦合：机械臂和杯子为轨迹回放，水为真实 DFSPH 粒子仿真。
+最终约 140.38 mL 进入碗、117.11 mL 留在杯中、0.83 mL 在容器外。
+台面以下粒子只从渲染副本过滤，物理和统计仍保留全部 76,538 粒。
+
+![倾倒全景](frame_15.png)
+
+![倾倒后的碗内液面](close_23.png)
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/close_23.png b/scripts/tutorials/sim/assets/franka_dfsph_pour/close_23.png
new file mode 100644
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diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/environment.json b/scripts/tutorials/sim/assets/franka_dfsph_pour/environment.json
new file mode 100644
index 000000000..07d96955e
--- /dev/null
+++ b/scripts/tutorials/sim/assets/franka_dfsph_pour/environment.json
@@ -0,0 +1,30 @@
+{
+  "python": "3.10.21",
+  "os": "Linux-6.8.0-117-generic-x86_64-with-glibc2.35",
+  "gpu": "NVIDIA GeForce RTX 5090 D v2",
+  "recorded_on_embodichain_commit": "3bb2a03c5500350116d37cf876590f3ec12accc4",
+  "draft_base_commit": "453675d8ab7a1f7ae0564ae40db80d9c707a30f4",
+  "packages": {
+    "dexsim-engine": "0.5.1",
+    "newton": "1.6.0",
+    "mujoco": "3.12.0",
+    "mujoco-warp": "3.12.0",
+    "warp-lang": "1.17.0",
+    "torch": "2.14.0",
+    "tensordict": "0.14.2",
+    "numpy": "2.2.6",
+    "scipy": "1.15.3",
+    "trimesh": "4.12.2",
+    "imageio": "2.38.0",
+    "imageio-ffmpeg": "0.6.0",
+    "opencv-python": "5.0.0.93",
+    "viser": "1.0.21",
+    "black": "26.3.1",
+    "pytest": "9.1.1"
+  },
+  "notes": [
+    "DexSim is the locally supplied wheel; no wheel or virtual environment is included.",
+    "Videos and reports were recorded before rebasing the demo onto the draft base.",
+    "Current-base compatibility checks are reported separately in the pull request."
+  ]
+}
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/frame_15.png b/scripts/tutorials/sim/assets/franka_dfsph_pour/frame_15.png
new file mode 100644
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+    "path": "/home/oem/EmbodiChain/outputs/franka_pour_cup/videos/episode_0_franka_newton_smooth_pour_mug.mp4",
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+    "fps": 20.0,
+    "width": 1280,
+    "height": 960
+  },
+  "keyframes": {
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+    "grasp": 230,
+    "lift": 380,
+    "pour": 570,
+    "return": 1159
+  }
+}
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/particles_30.npz b/scripts/tutorials/sim/assets/franka_dfsph_pour/particles_30.npz
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diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/pr_smoke_validation.json b/scripts/tutorials/sim/assets/franka_dfsph_pour/pr_smoke_validation.json
new file mode 100644
index 000000000..8ec1725a1
--- /dev/null
+++ b/scripts/tutorials/sim/assets/franka_dfsph_pour/pr_smoke_validation.json
@@ -0,0 +1,73 @@
+{
+  "physics": "Newton SPH / DFSPH",
+  "rigid_motion": "kinematic replay of verified MuJoCo trajectory",
+  "coupling": "one-way moving volume-map boundaries; no fluid reaction on robot",
+  "boundary": {
+    "method": "volume_map",
+    "volume_map_enabled": true,
+    "sdf_penalty_enabled": false,
+    "volume_map_resolution": 192,
+    "boundary_shape_count": 4,
+    "rest_density_kg_m3": 1000.0,
+    "viscosity": 0.0006
+  },
+  "bowl": {
+    "center_m": [
+      -0.755,
+      -0.137,
+      0.5
+    ],
+    "outer_diameter_m": 0.18894443627691185,
+    "height_m": 0.066,
+    "shape": "curved ceramic bowl with flat foot"
+  },
+  "trajectory": "/home/oem/.codex/worktrees/franka-dfsph-pour-demo/EmbodiChain/scripts/tutorials/sim/assets/franka_dfsph_pour/trajectory.pt",
+  "particle_count": 76538,
+  "spacing_m": 0.0015,
+  "source_start_time_s": 0.0,
+  "rest_density_kg_m3": 1000.0,
+  "viscosity": 0.0006,
+  "surface_tension_coefficient": 0.002,
+  "render_aabb_trim_fraction": 0.0,
+  "render_only_cull_below_z_m": 0.49,
+  "nominal_volume_ml": 258.31575,
+  "simulation_dt": 0.0006944444444444445,
+  "substeps": 1,
+  "duration_s": 2.0,
+  "wall_seconds": 21.189388323997264,
+  "samples": [
+    {
+      "in_mug": 76538,
+      "in_receiver": 0,
+      "below_table": 0,
+      "outside_containers": 0,
+      "received_ml": 0.0,
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+      "time_s": 0.0006944444444444445,
+      "speed_p99_m_s": 0.09959307312965393,
+      "density_p99_kg_m3": 1011.4403076171875
+    },
+    {
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+      "in_receiver": 0,
+      "below_table": 0,
+      "outside_containers": 0,
+      "received_ml": 0.0,
+      "min_z": 0.5096086859703064,
+      "time_s": 1.0006944444444446,
+      "speed_p99_m_s": 0.0166236013174057,
+      "density_p99_kg_m3": 1014.6708374023438
+    },
+    {
+      "in_mug": 76538,
+      "in_receiver": 0,
+      "below_table": 0,
+      "outside_containers": 0,
+      "received_ml": 0.0,
+      "min_z": 0.5096070170402527,
+      "time_s": 2.0,
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+    }
+  ]
+}
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/settling_comparison.json b/scripts/tutorials/sim/assets/franka_dfsph_pour/settling_comparison.json
new file mode 100644
index 000000000..8f8e19112
--- /dev/null
+++ b/scripts/tutorials/sim/assets/franka_dfsph_pour/settling_comparison.json
@@ -0,0 +1,44 @@
+{
+  "experiment": "same saved post-pour particle positions, initially zero velocity, stationary bowl and final robot pose",
+  "duration_s": 5,
+  "initial_snapshot": "outputs/franka_pour_cup/dfsph_volume_map/particles_30.npz",
+  "baseline": {
+    "viscosity": 0.00025,
+    "surface_tension": 0.02,
+    "volume_map_resolution": 96,
+    "final": {
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+      "in_receiver": 33850,
+      "below_table": 1728,
+      "outside_containers": 1728,
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+      "min_z": -45.27298355102539,
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+      "time_s": 5.0,
+      "speed_p99_m_s": 2.0,
+      "density_p99_kg_m3": 1023.318115234375
+    }
+  },
+  "revised": {
+    "viscosity": 0.0006,
+    "surface_tension": 0.002,
+    "volume_map_resolution": 192,
+    "final": {
+      "in_mug": 40960,
+      "in_receiver": 33850,
+      "below_table": 1728,
+      "outside_containers": 1728,
+      "received_ml": 114.24374999999999,
+      "min_z": -45.27298355102539,
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+      "bowl_speed_p95_m_s": 0.008076288737356663,
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+      "speed_p99_m_s": 2.0,
+      "density_p99_kg_m3": 1009.6124267578125
+    }
+  },
+  "bowl_speed_rms_reduction_percent": 96.45694061961943
+}
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/trajectory.pt b/scripts/tutorials/sim/assets/franka_dfsph_pour/trajectory.pt
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diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/validation.json b/scripts/tutorials/sim/assets/franka_dfsph_pour/validation.json
new file mode 100644
index 000000000..2da102288
--- /dev/null
+++ b/scripts/tutorials/sim/assets/franka_dfsph_pour/validation.json
@@ -0,0 +1,446 @@
+{
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diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/water_settling_closeup.mp4 b/scripts/tutorials/sim/assets/franka_dfsph_pour/water_settling_closeup.mp4
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diff --git a/scripts/tutorials/sim/franka_dfsph_pour_demo.py b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
new file mode 100644
index 000000000..7df0adacd
--- /dev/null
+++ b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
@@ -0,0 +1,571 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+"""Temporary EmbodiChain pouring demo using DexSim's Newton DFSPH solver.
+
+The robot and mug replay the recorded MuJoCo grasp trajectory. Water is simulated
+by DFSPH with volume-map boundaries, without reaction forces on the robot.
+Volume maps use baked mesh SDFs; the additional SDF penalty force is disabled.
+The temporary adapter uses the manager's existing Spawn builder and Newton
+render-sync object; it does not add a production fluid facade.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+import cv2
+import imageio.v2 as imageio
+import numpy as np
+import torch
+import trimesh
+import warp as wp
+from scipy.interpolate import PchipInterpolator
+from scipy.spatial.transform import Rotation, Slerp
+
+from dexsim import spawn
+from dexsim.engine.newton_physics import SPHSolverCfg
+from dexsim.engine.newton_physics.backend_registry import get_newton_backend
+
+from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
+from embodichain.lab.sim.cfg import LightCfg, NewtonPhysicsCfg, RenderCfg
+from embodichain.lab.sim.robots.franka_panda import FrankaPandaCfg
+from embodichain.lab.sim.sensors import CameraCfg
+
+__all__ = ["main"]
+
+ROOT = Path(__file__).resolve().parents[3]
+DEFAULT_TRAJECTORY = Path(__file__).parent / "assets/franka_dfsph_pour/trajectory.pt"
+CUP_ASSET = ROOT / (
+    "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/cup.glb"
+)
+RECEIVER_CENTER = np.array([-0.755, -0.137, 0.50])
+BOWL_OUTER_RADIUS = 0.097
+BOWL_INNER_RADIUS = 0.0915
+BOWL_SPHERE_Z = 0.088
+BOWL_HEIGHT = 0.066
+BOWL_BOTTOM_Z = 0.006
+
+
+def _pose(position: np.ndarray, rotation: np.ndarray | None = None) -> np.ndarray:
+    pose = np.eye(4, dtype=np.float32)
+    pose[:3, 3] = position
+    if rotation is not None:
+        pose[:3, :3] = rotation
+    return pose
+
+
+def _mesh_geometry(mesh: trimesh.Trimesh, name: str) -> spawn.GeometryDesc:
+    return spawn.GeometryDesc.mesh(
+        vertices=np.asarray(mesh.vertices, dtype=np.float32),
+        triangles=np.asarray(mesh.faces, dtype=np.int32),
+        segment_name=name,
+    )
+
+
+def _sdf(geometry: spawn.GeometryDesc, resolution: int = 192) -> spawn.CollisionDesc:
+    collision = spawn.CollisionDesc.from_geometry(
+        geometry, approximation=spawn.CollisionApproximation.SDF
+    )
+    collision.newton = spawn.NewtonCollisionDesc(
+        has_particle_collision=True,
+        # Bake the mesh SDF eagerly: Newton 1.6 rejects mesh sdf_* shape fields.
+        force_sdf=False,
+        sdf_max_resolution=resolution,
+        sdf_narrow_band_range=(-0.025, 0.025),
+        sdf_padding=0.012,
+        sdf_texture_format="uint16",
+        mu=0.2,
+    )
+    return collision
+
+
+def _particle_summary(
+    positions: np.ndarray,
+    cup_pose: np.ndarray,
+    spacing: float,
+    velocities: np.ndarray,
+) -> dict[str, float | int]:
+    local = (positions - cup_pose[:3, 3]) @ cup_pose[:3, :3]
+    radius = 0.031 + (local[:, 2] + 0.044) * (0.0075 / 0.094)
+    in_mug = (
+        (np.linalg.norm(local[:, :2], axis=1) < radius + 0.003)
+        & (local[:, 2] > -0.049)
+        & (local[:, 2] < 0.053)
+    )
+    receiver = positions - RECEIVER_CENTER
+    bowl_radius = np.sqrt(
+        np.maximum(0.0, BOWL_INNER_RADIUS**2 - (receiver[:, 2] - BOWL_SPHERE_Z) ** 2)
+    )
+    in_receiver = (
+        (np.linalg.norm(receiver[:, :2], axis=1) < bowl_radius + spacing)
+        & (receiver[:, 2] > BOWL_BOTTOM_Z - spacing)
+        & (receiver[:, 2] < BOWL_HEIGHT + spacing)
+    )
+    summary = {
+        "in_mug": int(in_mug.sum()),
+        "in_receiver": int(in_receiver.sum()),
+        "below_table": int((positions[:, 2] < 0.49).sum()),
+        "outside_containers": int((~(in_mug | in_receiver)).sum()),
+        "received_ml": float(in_receiver.sum() * spacing**3 * 1e6),
+        "min_z": float(positions[:, 2].min()),
+    }
+    if in_receiver.any():
+        bowl_speed = np.linalg.norm(velocities[in_receiver], axis=1)
+        summary["bowl_speed_rms_m_s"] = float(np.sqrt(np.mean(bowl_speed**2)))
+        summary["bowl_speed_p95_m_s"] = float(np.quantile(bowl_speed, 0.95))
+        summary["bowl_height_p99_m"] = float(
+            np.quantile(receiver[in_receiver, 2], 0.99)
+        )
+    return summary
+
+
+def main() -> None:
+    """Replay the arm and simulate water in the same EmbodiChain world."""
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--trajectory", type=Path, default=DEFAULT_TRAJECTORY)
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=ROOT / "outputs/franka_pour_cup/dfsph_stable_bowl",
+    )
+    parser.add_argument("--seconds", type=float, default=None)
+    parser.add_argument("--start-time", type=float, default=0.0)
+    parser.add_argument("--speed", type=float, default=2.0)
+    parser.add_argument("--spacing", type=float, default=0.0015)
+    parser.add_argument("--volume-map-resolution", type=int, default=192)
+    parser.add_argument("--viscosity", type=float, default=0.0006)
+    parser.add_argument("--surface-tension", type=float, default=0.002)
+    parser.add_argument("--fps", type=int, default=24)
+    parser.add_argument("--physics-hz", type=int, default=1440)
+    parser.add_argument("--substeps", type=int, default=1)
+    parser.add_argument("--renderer", choices=["rt", "hybrid"], default="rt")
+    parser.add_argument("--headless", action="store_true")
+    args = parser.parse_args()
+    if min(args.speed, args.spacing, args.fps, args.substeps, args.physics_hz) <= 0:
+        parser.error("speed, spacing, fps, substeps and physics-hz must be positive")
+    args.output.mkdir(parents=True, exist_ok=True)
+    data = torch.load(args.trajectory, map_location="cpu", weights_only=False)
+    joints = data["states"]["robot", "qpos"][0].numpy()
+    cup_states = data["states"]["rigid_objects", "cup", "pose"][0].numpy()
+    recorded_times = np.arange(len(joints)) * float(data["meta"]["dt"])
+    joint_curve = PchipInterpolator(recorded_times, joints)
+    position_curve = PchipInterpolator(recorded_times, cup_states[:, :3])
+    orientation_curve = Slerp(recorded_times, Rotation.from_quat(cup_states[:, 3:]))
+    if not 0.0 <= args.start_time < recorded_times[-1]:
+        parser.error("start-time must lie inside the recorded trajectory")
+    duration = 1.0 + (recorded_times[-1] - args.start_time) / args.speed + 1.0
+    if args.seconds is not None:
+        duration = min(duration, args.seconds)
+    # Update the moving boundary at every solver step. With coarse pose writes
+    # and many solver substeps, DFSPH estimates an impulsive boundary velocity.
+    dt = 1.0 / args.physics_hz
+    frame_stride = round(1.0 / (dt * args.fps))
+    if not np.isclose(frame_stride * dt * args.fps, 1.0):
+        parser.error("fps must divide physics-hz")
+    spacing = args.spacing
+    grid = np.arange(-0.04, 0.0401, spacing)
+    xx, yy, zz = np.meshgrid(grid, grid, np.arange(-0.036, 0.044, spacing))
+    local_points = np.column_stack([xx.ravel(), yy.ravel(), zz.ravel()])
+    inside_radius = 0.031 + (local_points[:, 2] + 0.044) * (0.0075 / 0.094)
+    local_points = local_points[
+        np.linalg.norm(local_points[:, :2], axis=1) < inside_radius - 0.003
+    ]
+    initial_pose = _pose(
+        position_curve(args.start_time), orientation_curve(args.start_time).as_matrix()
+    )
+    points = (local_points @ initial_pose[:3, :3].T + initial_pose[:3, 3]).astype(
+        np.float32
+    )
+    sim = SimulationManager(
+        SimulationManagerCfg(
+            headless=True,
+            width=1280,
+            height=960,
+            num_envs=1,
+            arena_space=2.5,
+            device="cuda",
+            physics_dt=dt,
+            render_cfg=RenderCfg(renderer=args.renderer, spp=8),
+            physics_cfg=NewtonPhysicsCfg(
+                num_substeps=args.substeps,
+                collision_cfg=None,
+                solver_cfg=SPHSolverCfg(
+                    sph_method="dfsph",
+                    particle_diameter=spacing,
+                    particle_support_radius=2.25 * spacing,
+                    rest_density=1000.0,
+                    viscosity=args.viscosity,
+                    surface_tension_coefficient=args.surface_tension,
+                    surface_normal_threshold=0.08,
+                    pressure_relaxation=0.2,
+                    divergence_max_iterations=10,
+                    density_max_iterations=20,
+                    max_neighbors=192,
+                    max_velocity=2.0,
+                    volume_map_boundary_enabled=True,
+                    volume_map_resolution=args.volume_map_resolution,
+                    volume_map_quadrature_samples=6,
+                    volume_map_volume_scale=0.8,
+                    sdf_boundary_enabled=False,
+                ),
+            ),
+        )
+    )
+    writer = None
+    close_writer = None
+    try:
+        robot_cfg = FrankaPandaCfg.from_dict(
+            {
+                "uid": "FrankaPanda",
+                "init_rot": [0.0, 0.0, 180.0],
+                "init_qpos": joint_curve(args.start_time).tolist(),
+                "attrs": {
+                    "collision_props": {
+                        "backend": "newton",
+                        "has_particle_collision": False,
+                    }
+                },
+            }
+        )
+        robot = sim.add_robot(robot_cfg)
+        # Temporary adapter: add fluid to the manager's one existing scene.
+        builder = sim._spawn_scene.builder
+        # FluidDesc currently accepts a native material, unlike RenderDesc.
+        water_material = sim.get_env().create_pbr_material("dfsph_water")
+        water_inst = water_material.get_inst()
+        water_inst.set_pbr_param("baseColor", 0.88, 0.96, 1.0)
+        water_inst.set_pbr_param("roughness", 0.025)
+        water_inst.set_pbr_param("metallic", 0.0)
+        water_inst.set_pbr_param("ior", 1.333)
+        water_inst.set_pbr_param("transmission", 0.96)
+        water_inst.set_pbr_param("volume_thickness", 0.04)
+        water_inst.set_pbr_param("volume_attenuation_distance", 0.6)
+        water_inst.set_pbr_param("volume_attenuation_color", 0.80, 0.92, 1.0)
+        builder.materials["ceramic"] = spawn.MaterialDesc(
+            name="ceramic", base_color=(0.82, 0.85, 0.88, 1.0), roughness=0.22
+        )
+        builder.materials["table"] = spawn.MaterialDesc(
+            name="table", base_color=(0.23, 0.29, 0.34, 1.0), roughness=0.6
+        )
+        table_geometry = _mesh_geometry(
+            trimesh.creation.box([0.42, 0.70, 0.50]), "table"
+        )
+        builder.add_object(
+            spawn.ObjectDesc(
+                name="pour_table",
+                pose=_pose(np.array([-0.91, -0.08, 0.25])),
+                renders=[
+                    spawn.RenderDesc.from_geometry(table_geometry, material_ref="table")
+                ],
+                collisions=[_sdf(table_geometry, 96)],
+                physics=spawn.RigidBodyPhysicsDesc.kinematic(),
+                per_env=False,
+            )
+        )
+        floor_geometry = _mesh_geometry(
+            trimesh.creation.box([2.5, 2.5, 0.10]), "fluid_floor"
+        )
+        builder.add_object(
+            spawn.ObjectDesc(
+                name="fluid_floor",
+                pose=_pose(np.array([-0.5, 0.0, -0.055])),
+                collisions=[_sdf(floor_geometry, 128)],
+                physics=spawn.RigidBodyPhysicsDesc.kinematic(),
+                per_env=False,
+            )
+        )
+        mug_mesh = trimesh.creation.revolve(
+            [
+                [0.0, -0.05],
+                [0.035, -0.05],
+                [0.0425, 0.05],
+                [0.0385, 0.05],
+                [0.031, -0.044],
+                [0.0, -0.044],
+            ],
+            sections=128,
+        )
+        builder.add_object(
+            spawn.ObjectDesc(
+                name="liquid_mug",
+                pose=initial_pose,
+                renders=[
+                    spawn.RenderDesc.from_geometry(
+                        spawn.GeometryDesc.mesh(str(CUP_ASSET))
+                    )
+                ],
+                collisions=[_sdf(_mesh_geometry(mug_mesh, "mug_wall"))],
+                physics=spawn.RigidBodyPhysicsDesc.kinematic(),
+                per_env=False,
+            )
+        )
+        outer_z = np.linspace(0.0, BOWL_HEIGHT, 64)
+        inner_z = np.linspace(BOWL_HEIGHT, BOWL_BOTTOM_Z, 64)
+        outer_r = np.sqrt(BOWL_OUTER_RADIUS**2 - (outer_z - BOWL_SPHERE_Z) ** 2)
+        inner_r = np.sqrt(BOWL_INNER_RADIUS**2 - (inner_z - BOWL_SPHERE_Z) ** 2)
+        bowl_profile = np.vstack(
+            [
+                [0.0, 0.0],
+                np.column_stack([outer_r, outer_z]),
+                np.column_stack([inner_r, inner_z]),
+                [0.0, BOWL_BOTTOM_Z],
+            ]
+        )
+        bowl_mesh = trimesh.creation.revolve(bowl_profile, sections=192)
+        bowl_geometry = _mesh_geometry(bowl_mesh, "receiver_wall")
+        builder.add_object(
+            spawn.ObjectDesc(
+                name="receiver",
+                pose=_pose(RECEIVER_CENTER),
+                renders=[
+                    spawn.RenderDesc.from_geometry(
+                        bowl_geometry, material_ref="ceramic"
+                    )
+                ],
+                collisions=[_sdf(bowl_geometry)],
+                physics=spawn.RigidBodyPhysicsDesc.kinematic(),
+                per_env=False,
+            )
+        )
+        builder.add_fluid(
+            "water",
+            points,
+            density=1000.0,
+            particle_spacing=spacing,
+            particle_radius=0.30 * spacing,
+            render_radius=0.30 * spacing,
+            render_mode=spawn.FluidRenderMode.MESH_SURFACE,
+            mesh_surface=spawn.FluidMeshSurfaceRenderDesc(
+                gpu_enabled=True,
+                cell_size=0.54 * spacing,
+                max_res=512,
+                kernel_radius=1.01 * spacing,
+                iso_density=0.57,
+                density_smooth_iters=2,
+                aabb_trim_fraction=0.0,
+                min_component_tris=32,
+                laplacian_iters=4,
+                laplacian_alpha=0.11,
+                update_period=1,
+            ),
+            material=water_material,
+            per_env=False,
+        )
+        sim.add_light(
+            LightCfg(
+                uid="key", light_type="sun", intensity=4.0, direction=[-0.3, 0.2, -1.0]
+            )
+        )
+        sim.add_light(
+            LightCfg(
+                uid="fill",
+                light_type="rect",
+                intensity=6.0,
+                init_pos=[-0.6, -0.4, 1.4],
+                direction=[0.0, 0.0, -1.0],
+                rect_width=0.8,
+                rect_height=0.8,
+            )
+        )
+        sim.prepare()
+        backend = get_newton_backend(sim.get_world())
+        boundary_info = {
+            "method": "volume_map",
+            "volume_map_enabled": backend.solver.volume_map_boundary is not None,
+            "sdf_penalty_enabled": backend.solver.sdf_boundary is not None,
+            "volume_map_resolution": args.volume_map_resolution,
+            "boundary_shape_count": backend.solver.volume_map_boundary.shape_count,
+            "rest_density_kg_m3": backend.solver.rest_density,
+            "viscosity": backend.solver.viscosity,
+        }
+        print(json.dumps(boundary_info), flush=True)
+        mug = sim.spawn_result.get_actor("liquid_mug")
+        fluid = sim.spawn_result.get_fluid_particle_set("water")
+        camera = sim.add_sensor(
+            CameraCfg(
+                uid="demo_camera",
+                width=1280,
+                height=960,
+                intrinsics=[1100.0, 1100.0, 640.0, 480.0],
+                extrinsics=CameraCfg.ExtrinsicsCfg(
+                    eye=[-1.4, -1.4, 1.2],
+                    target=[-0.43, -0.03, 0.47],
+                    up=[0.0, 0.0, 1.0],
+                ),
+            )
+        )
+        close_camera = sim.add_sensor(
+            CameraCfg(
+                uid="water_camera",
+                width=960,
+                height=720,
+                intrinsics=[1150.0, 1150.0, 480.0, 360.0],
+                extrinsics=CameraCfg.ExtrinsicsCfg(
+                    eye=[-1.12, -0.73, 0.96],
+                    target=[-0.76, -0.07, 0.61],
+                    up=[0.0, 0.0, 1.0],
+                ),
+            )
+        )
+        if not args.headless:
+            sim.open_window()
+        writer = imageio.get_writer(
+            args.output / "franka_dfsph_pour.mp4",
+            fps=args.fps,
+            codec="libx264",
+            quality=8,
+            macro_block_size=1,
+        )
+        close_writer = imageio.get_writer(
+            args.output / "water_closeup.mp4",
+            fps=args.fps,
+            codec="libx264",
+            quality=8,
+            macro_block_size=1,
+        )
+        metrics = []
+        started = time.perf_counter()
+        previous_cup_pose = initial_pose
+        print(
+            f"DFSPH: {len(points)} particles, {len(points) * spacing**3 * 1e6:.1f} mL, "
+            f"{duration:.2f} s",
+            flush=True,
+        )
+        for step in range(round(duration / dt)):
+            elapsed = step * dt
+            source_time = float(
+                np.clip(
+                    args.start_time + (elapsed - 1.0) * args.speed,
+                    args.start_time,
+                    recorded_times[-1],
+                )
+            )
+            robot.set_qpos(
+                torch.as_tensor(
+                    joint_curve(source_time), dtype=torch.float32, device=sim.device
+                ).unsqueeze(0),
+                target=False,
+            )
+            cup_pose = _pose(
+                position_curve(source_time), orientation_curve(source_time).as_matrix()
+            )
+            mug.set_world_pose(cup_pose)
+            mug.set_linear_velocity((cup_pose[:3, 3] - previous_cup_pose[:3, 3]) / dt)
+            mug.set_angular_velocity(
+                Rotation.from_matrix(
+                    cup_pose[:3, :3] @ previous_cup_pose[:3, :3].T
+                ).as_rotvec()
+                / dt
+            )
+            previous_cup_pose = cup_pose
+            sim.update(step=1, render_final_step=False)
+            if step % frame_stride == 0:
+                with sim.render_frame():
+                    # Cull below-table droplets only in a separate render copy.
+                    # The mesh builder explicitly skips non-finite render points.
+                    # Physics positions, velocities and counts remain untouched.
+                    render_positions = wp.to_torch(
+                        fluid.get_particle_positions()
+                    ).clone()
+                    render_positions[render_positions[:, 2] < 0.49] = float("nan")
+                    render_positions_wp = wp.from_torch(render_positions, dtype=wp.vec3)
+                    backend.render_sync.sync(
+                        sim.get_world(),
+                        backend.state_0.body_q,
+                        render_positions_wp,
+                        simulation_time=backend.sim_time,
+                    )
+                    camera.update()
+                    close_camera.update()
+                    rgb = camera.get_data()["color"][0, :, :, :3].cpu().numpy()
+                    close_rgb = (
+                        close_camera.get_data()["color"][0, :, :, :3].cpu().numpy()
+                    )
+                writer.append_data(rgb)
+                close_writer.append_data(close_rgb)
+                if step % args.physics_hz == 0:
+                    cv2.imwrite(
+                        str(args.output / f"frame_{int(elapsed):02d}.png"),
+                        cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR),
+                    )
+                    cv2.imwrite(
+                        str(args.output / f"close_{int(elapsed):02d}.png"),
+                        cv2.cvtColor(close_rgb, cv2.COLOR_RGB2BGR),
+                    )
+            if step % args.physics_hz == 0 or step == round(duration / dt) - 1:
+                positions = fluid.get_particle_positions().numpy()
+                if not np.isfinite(positions).all():
+                    raise RuntimeError("Non-finite DFSPH particle state")
+                velocities = fluid.get_particle_velocities().numpy()
+                summary = _particle_summary(positions, cup_pose, spacing, velocities)
+                summary["time_s"] = elapsed + dt
+                speed = np.linalg.norm(velocities, axis=1)
+                density = backend.solver.particle_density.numpy()
+                summary["speed_p99_m_s"] = float(np.quantile(speed, 0.99))
+                summary["density_p99_kg_m3"] = float(np.quantile(density, 0.99))
+                metrics.append(summary)
+                np.savez_compressed(
+                    args.output / f"particles_{int(elapsed):02d}.npz",
+                    positions=positions,
+                    velocities=velocities,
+                    cup_pose=cup_pose,
+                    time_s=elapsed + dt,
+                )
+                print(json.dumps(summary), flush=True)
+        report = {
+            "physics": "Newton SPH / DFSPH",
+            "rigid_motion": "kinematic replay of verified MuJoCo trajectory",
+            "coupling": "one-way moving volume-map boundaries; no fluid reaction on robot",
+            "boundary": boundary_info,
+            "bowl": {
+                "center_m": RECEIVER_CENTER.tolist(),
+                "outer_diameter_m": float(2.0 * outer_r[-1]),
+                "height_m": BOWL_HEIGHT,
+                "shape": "curved ceramic bowl with flat foot",
+            },
+            "trajectory": str(args.trajectory.resolve()),
+            "particle_count": len(points),
+            "spacing_m": spacing,
+            "source_start_time_s": args.start_time,
+            "rest_density_kg_m3": backend.solver.rest_density,
+            "viscosity": backend.solver.viscosity,
+            "surface_tension_coefficient": args.surface_tension,
+            "render_aabb_trim_fraction": 0.0,
+            "render_only_cull_below_z_m": 0.49,
+            "nominal_volume_ml": len(points) * spacing**3 * 1e6,
+            "simulation_dt": dt,
+            "substeps": args.substeps,
+            "duration_s": duration,
+            "wall_seconds": time.perf_counter() - started,
+            "samples": metrics,
+        }
+        (args.output / "validation.json").write_text(
+            json.dumps(report, indent=2) + "\n"
+        )
+        print(f"Saved {args.output}", flush=True)
+    finally:
+        if writer is not None:
+            writer.close()
+        if close_writer is not None:
+            close_writer.close()
+        sim.destroy(exit_process=False)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/gym/envs/test_official_task_layout.py b/tests/gym/envs/test_official_task_layout.py
index b655d9b53..3fd6a43c1 100644
--- a/tests/gym/envs/test_official_task_layout.py
+++ b/tests/gym/envs/test_official_task_layout.py
@@ -58,6 +58,7 @@
     "blocks_ranking_size",
     "match_object_container",
     "place_object_drawer",
+    "pour_cup",
     "pour_water",
     "scoop_ice",
     "stack_blocks_two",

From 40f73d879f85250925f8e292273583fb1eca56bd Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Wed, 14 Oct 2026 00:36:47 +0800
Subject: [PATCH 02/19] feat(sim): integrate fixed-count fluids through public
 scene APIs

---
 IMPLEMENTATION_PLAN.md                        | 101 ++++++
 agent_context/MAP.yaml                        |   2 +-
 .../topics/simulation-system/fluids.md        |  38 ++
 .../simulation-system/simulation-system.md    |   1 +
 docs/source/api_reference/public_api.rst      |  36 ++
 embodichain/compute/geometry/particles.py     | 107 ++++++
 embodichain/lab/sim/_runtime_controls.py      |  56 +++
 embodichain/lab/sim/_startup_summary.py       |   1 +
 embodichain/lab/sim/cfg/__init__.py           |   4 +
 embodichain/lab/sim/cfg/fluid.py              | 168 +++++++++
 embodichain/lab/sim/cfg/physics.py            |  19 +-
 embodichain/lab/sim/cfg/rigid_object.py       |   8 +
 embodichain/lab/sim/material.py               |  25 ++
 embodichain/lab/sim/objects/__init__.py       |   1 +
 embodichain/lab/sim/objects/fluid.py          | 224 ++++++++++++
 embodichain/lab/sim/objects/rigid_object.py   |  85 ++++-
 embodichain/lab/sim/physics/newton.py         |  61 +++-
 embodichain/lab/sim/shapes.py                 |  11 +
 embodichain/lab/sim/sim_manager.py            | 188 ++++++++++
 embodichain/lab/sim/spawn/descriptors.py      |  35 +-
 embodichain/lab/sim/spawn/scene.py            |  19 +-
 .../lab/visualization/scene_exporter.py       |  25 +-
 .../manipulation/tableware/pour_cup/README.md |  15 +-
 .../tutorials/sim/franka_dfsph_pour_demo.py   | 337 ++++++++++--------
 tests/sim/objects/test_fluid_object.py        | 204 +++++++++++
 tests/sim/objects/test_fluid_runtime.py       | 106 ++++++
 tests/sim/test_fluid_cfg.py                   | 175 +++++++++
 tests/sim/test_sim_manager.py                 |  44 +++
 tests/visualization/test_scene_exporter.py    |  21 ++
 29 files changed, 1940 insertions(+), 177 deletions(-)
 create mode 100644 IMPLEMENTATION_PLAN.md
 create mode 100644 agent_context/topics/simulation-system/fluids.md
 create mode 100644 embodichain/compute/geometry/particles.py
 create mode 100644 embodichain/lab/sim/cfg/fluid.py
 create mode 100644 embodichain/lab/sim/objects/fluid.py
 create mode 100644 tests/sim/objects/test_fluid_object.py
 create mode 100644 tests/sim/objects/test_fluid_runtime.py
 create mode 100644 tests/sim/test_fluid_cfg.py

diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
new file mode 100644
index 000000000..f88cecf58
--- /dev/null
+++ b/IMPLEMENTATION_PLAN.md
@@ -0,0 +1,101 @@
+# Fluid integration implementation plan
+
+## Accepted scope
+
+Start from PR #771 (d5322aa). Use DexSim's existing SPHSolverCfg directly;
+add only the existing Newton solver mapping, not a new EC solver config.
+Initial fill, fixed particle counts, Newton CUDA DFSPH, one-way coupling.
+
+## Delivery gates
+
+1. **Sim public interfaces (complete)**: FluidObjectCfg / spawn and render
+   configuration; declared FluidObject / particle state; manager and Spawn
+   lifecycle; rigid boundary state and SDF adaptation; production tests.
+2. **Match the existing Sim demo (complete)**: migrate #771 using identical
+   particles, trajectory, numerical parameters, camera and material. Run a
+   same-environment baseline and migrated short test, then full pouring.
+   Compare received/residual/spilled volume, settling, images, runtime and VRAM.
+   A short test alone does not pass this gate. Record environment and evidence.
+3. **Gym integration (gated, do not start before gate 2 passes)**: YAML scene
+   ownership, vectorized observations, partial reset, optional particle records.
+   DexSim world-filtered SPH neighborhoods/boundaries and per-world solver reset
+   are prerequisites; spacing worlds apart is not isolation.
+
+## Sim acceptance
+
+- Native solver object and mapping-valued sph configuration both work.
+- Object declaration -> prepare -> GPU particle state -> write/reset -> cleanup.
+- Deterministic points and volume sampling; local/world pose conventions.
+- Rendering never changes physical particle state; no native solver, builder or
+  render-sync access from the migrated tutorial.
+- Start with one environment and reject unsupported batch use explicitly.
+- Production tests, Black 26.3.1, API coverage and updated project context.
+
+## Evidence
+
+Implementation and validation results will be appended here. No Gym changes
+will be represented as complete before full demo parity is established.
+
+
+## Implemented Sim interfaces
+
+- `FluidObjectCfg`, `FluidSpawnCfg`, `FluidRenderCfg`; points/NPZ, box, sphere,
+  cylinder and closed fill-mesh sources. Shared numerical sampling is in compute.
+- `add_fluid_object`, `get_fluid_object`, existing `get_asset`, and detached
+  world-frame particle data; write/reset/reset_to_pose through Scene batches.
+- Direct DexSim SPHSolverCfg and mapping-valued `solver_type: sph` support.
+- Rigid kinematic pose/twist writes and native PRE_SUBSTEP trajectory controls.
+- Independent rigid visual/collision geometry and pre-baked SDF policy.
+- Native render publication, render-only clipping, and detached Viser points.
+- No Gym modifications. Multi-environment fluid declarations fail explicitly.
+
+## Current qualification limits
+
+- Fixed physical topology after prepare; no particle/rigid topology rebuild.
+- `FluidRenderCfg(mode="none")` requires the NoRender backend.
+- Native runtime used here is a local DexSim build; repository dependency pin
+  is unchanged. This does not qualify every published wheel.
+- GPU memory was sampled on matching 2-second runs: baseline 4981 MiB,
+  public API 4985 MiB. This does not measure the full-pour peak.
+
+## Review and validation
+
+Architecture/API and engine reviewers checked the implementation independently.
+Integer initial velocities, independent-visual-mesh UV selection, optional optical
+material forwarding and render-frame cache scope were corrected and regression
+covered. The full run must include a pixel/video check: an earlier iteration had
+correct particle snapshots but stale offscreen images, fixed by publishing via
+`sim.sync_render_state()` before camera acquisition.
+
+
+## Full-demo parity result
+
+- Runtime: Python 3.11.14, local DexSim 0.5.1+21bfad162518216fdd9cf313c70b15af94e6fa84,
+  Torch 2.10.0, Warp 1.15.0, Newton 1.4.0, RTX 5090.
+- Same-environment original script versus public-API script: 30.975 seconds,
+  76,538 particles, 31 snapshots with **bit-identical positions and velocities**.
+  All recorded statistics match exactly, including 139.16475 mL received and
+  final receiver speed RMS 0.0048424867 m/s.
+- Two videos each decode all 744 frames. Mean pixel absolute difference on the
+  0-255 scale: 0.477 (wide), 0.586 (close); moving/pouring and settling frames
+  were also visually inspected. Original native linear water color is converted
+  explicitly to the public material's sRGB convention.
+- Full-loop wall time: original 341.69 s, public API 349.04 s (+2.15%).
+  This is a single-run comparison, not a benchmark distribution.
+- Evidence: `outputs/fluid-parity.json`, `outputs/fluid-baseline-full/`,
+  `outputs/fluid-public-final/`. Generated videos/NPZ remain local ignored outputs.
+- Focused CPU/config/lifecycle/visualization/material/context/API-check tests:
+  533 passed, 1 skipped in the final combined run. GPU lifecycle/substep/reset
+  test: 1 passed. The tutorial also passed a two-substep smoke run.
+- GPU lifecycle test verifies declaration, state, native substep control,
+  selected reset, repeated rollout, endpoint hold and cleanup.
+
+## Next milestone
+
+Sim and demo parity are the current delivered milestone. Gym remains unimplemented.
+Its next dependency is native world-filtered neighborhoods/boundaries, per-world
+DFSPH convergence and selected-world cache reset, followed by EC YAML/manager
+integration. Do not remove the single-environment gate before that qualification.
+
+GPU memory evidence: `outputs/fluid-memory-comparison.json` (process samples
+at approximately 250 ms cadence; 4981 -> 4985 MiB).
diff --git a/agent_context/MAP.yaml b/agent_context/MAP.yaml
index a0168abc3..67a985e17 100644
--- a/agent_context/MAP.yaml
+++ b/agent_context/MAP.yaml
@@ -13,7 +13,7 @@ topics:
     NRDCfg, DLSS, NRD, frame_time_delta_ms, explicit physics stepping, arena, DexSim, PhysicsBackendCfg,
     DefaultPhysicsCfg, NewtonPhysicsCfg, AutoSolverCfg, DexUniSolverCfg, DexUni, runtime control, deformable, particle set,
     ArticulationJointKinematics, get_parent_joint_chain, asset_physics_mode, joint_drive_props, enable_gravity,
-    fixed-cadence playback, quaternion, xyzw]
+    fixed-cadence playback, FluidObjectCfg, FluidObject, SPHSolverCfg, fluid, 流体, quaternion, xyzw]
   paths: [topics/simulation-system/simulation-system.md]
   source_of_truth:
   - embodichain/lab/sim/motion/__init__.py
diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
new file mode 100644
index 000000000..f5ce42a68
--- /dev/null
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -0,0 +1,38 @@
+# Initial-fill fluids
+
+`cfg/fluid.py` owns source, object and render configuration. Numerical solver
+configuration remains DexSim `SPHSolverCfg`, accepted directly or through the
+existing `NewtonPhysicsCfg.solver_cfg` mapping with `solver_type: sph`.
+`SimulationManager.add_fluid_object()` declares through SpawnScene and binds
+`objects/fluid.py` in place at `prepare()`.
+
+The current qualification boundary is one Newton CUDA environment without
+gradients. Fixed particle counts and shared density/spacing are required.
+The prepared particle scene has fixed topology: adding, replacing or removing
+rigid assets also requires a new scene. Batch Gym integration remains gated on demo parity and native SPH world
+isolation and selected-world reset; arena spacing does not prove isolation.
+
+Fluid state uses world-frame `(instances, particles, 3|6)` Torch snapshots.
+Writes update Scene particle batches; resetting transforms a saved local
+particle template or restores the immutable initial state. Render topology is
+reconstructed and is not inherited from volume/surface deformables.
+
+Kinematic rigid boundaries can be written with `set_kinematic_state()` or
+driven before each solver substep using `register_kinematic_rigid_trajectory()`.
+Registered host controls disable graph replay and rewind selected clocks on
+object reset.
+
+A spawn reference is a declared rigid body's initial pose, not a persistent
+attachment. Mesh volume sources must describe closed cavities, not cup walls.
+Sampling math belongs to `compute/geometry/particles.py`.
+
+Native rendering is published once through the existing Newton render bridge.
+Display clipping acts only on copied positions; Viser consumes detached point
+clouds. NoRender fluid mode does not require optical materials. Ordinary rigid
+objects may supply a separate `visual_shape`; SDF cooking stays on the physical
+shape. Explicit `force_sdf=False` permits pre-baked mesh SDFs.
+
+Focused checks are `tests/sim/test_fluid_cfg.py`,
+`tests/sim/objects/test_fluid_object.py` and the serial CUDA lifecycle test
+`tests/sim/objects/test_fluid_runtime.py`. The pouring tutorial is an end-to-end
+qualification workload, not a unit-test import target.
diff --git a/agent_context/topics/simulation-system/simulation-system.md b/agent_context/topics/simulation-system/simulation-system.md
index db78a4afe..d4c0c8338 100644
--- a/agent_context/topics/simulation-system/simulation-system.md
+++ b/agent_context/topics/simulation-system/simulation-system.md
@@ -95,6 +95,7 @@ tests need non-symmetric rotations to expose component-order mistakes.
 | Preparation, topology changes, reset, cleanup, controls | [Lifecycle](lifecycle.md); manager and `spawn/scene.py` |
 | Backend/device selection, source overlays, collision/mass policy | [Physics configuration](physics-configuration.md); `cfg/`, `spawn/descriptors.py` |
 | Joint state order, drive lowering, mimic coupling, native adaptation | [Articulation and batch adapters](articulation-adapters.md); `objects/backends/`, `objects/articulation.py` |
+| Initial-fill fluid configuration, state and rendering | [Fluids](fluids.md); `cfg/fluid.py`, `objects/fluid.py` |
 | Volume/surface state, topology and kinematic nodes | [Deformables](deformables.md); `cfg/deformable.py`, `objects/deformable/` |
 | Camera synchronization, native window/offscreen denoising, startup readiness | [Rendering and diagnostics](rendering.md); manager, `cfg/simulation.py`, `_startup_summary.py` |
 | Robot definition, control parts, robot presets | [Robot system](../robot-system/robot-system.md) |
diff --git a/docs/source/api_reference/public_api.rst b/docs/source/api_reference/public_api.rst
index 390107de8..a6fcee2b2 100644
--- a/docs/source/api_reference/public_api.rst
+++ b/docs/source/api_reference/public_api.rst
@@ -22,6 +22,17 @@ decoding and compiling the constrained Task Program schema surface.
    compile_mllm_task_program
    decode_mllm_task_program
 
+embodichain.compute.geometry.particles
+-----------------------------------------
+
+Deterministic initial-fill volume sampling without simulation dependencies.
+
+.. currentmodule:: embodichain.compute.geometry.particles
+
+.. autosummary::
+
+   sample_volume_particles
+
 embodichain.data.assets.demo_assets
 -----------------------------------
 
@@ -342,6 +353,31 @@ embodichain.lab.gym.envs.embodied_env
    EmbodiedEnvCfg
    EmbodiedEnv
 
+embodichain.lab.sim.cfg.fluid
+--------------------------------
+
+Fixed-count fluid source and render configuration. Numerical solver parameters remain owned by DexSim SPHSolverCfg.
+
+.. currentmodule:: embodichain.lab.sim.cfg.fluid
+
+.. autosummary::
+
+   FluidSpawnCfg
+   FluidRenderCfg
+   FluidObjectCfg
+
+embodichain.lab.sim.objects.fluid
+------------------------------------
+
+Scene-bound fluid state with detached particle snapshots and explicit state writes. Fluid rendering has no fixed mesh-connectivity contract.
+
+.. currentmodule:: embodichain.lab.sim.objects.fluid
+
+.. autosummary::
+
+   FluidObject
+   FluidObjectData
+
 embodichain.lab.task_program.integrations.simulation.handover
 -------------------------------------------------------------
 
diff --git a/embodichain/compute/geometry/particles.py b/embodichain/compute/geometry/particles.py
new file mode 100644
index 000000000..4f5d9a9a9
--- /dev/null
+++ b/embodichain/compute/geometry/particles.py
@@ -0,0 +1,107 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Deterministic volume sampling, independent of simulation objects."""
+
+from __future__ import annotations
+
+import math
+from typing import Sequence
+import numpy as np
+
+__all__ = ["sample_volume_particles"]
+
+
+def sample_volume_particles(
+    kind: str,
+    spacing: float,
+    *,
+    size: Sequence[float] = (0.05, 0.05, 0.05),
+    radius: float = 0.025,
+    height: float = 0.05,
+    clearance: float = 0.0,
+    mesh_path: str | None = None,
+    max_particles: int = 1_000_000,
+) -> np.ndarray:
+    """Sample an origin-centred primitive or closed mesh on a cubic lattice.
+
+    Args:
+        kind: ``box``, ``sphere``, ``cylinder`` (Z axis), or ``mesh_volume``.
+        spacing: Positive lattice spacing, metres.
+        size: Full box dimensions, metres.
+        radius: Sphere or cylinder radius, metres.
+        height: Full cylinder height, metres.
+        clearance: Inward distance from the volume boundary, metres.
+        mesh_path: Closed fill-volume mesh for ``mesh_volume``.
+        max_particles: Maximum candidate-grid size before allocation.
+
+    Returns:
+        Contiguous float32 local positions of shape ``(N, 3)``.
+    """
+    if not math.isfinite(spacing) or spacing <= 0:
+        raise ValueError("spacing must be finite and positive.")
+    if not math.isfinite(clearance) or clearance < 0:
+        raise ValueError("clearance must be finite and nonnegative.")
+    if type(max_particles) is not int or max_particles <= 0:
+        raise ValueError("max_particles must be a positive integer.")
+    mesh = None
+    if kind == "mesh_volume":
+        import trimesh
+
+        if mesh_path is None:
+            raise ValueError("mesh_volume requires mesh_path.")
+        mesh = trimesh.load(mesh_path, force="mesh")
+        if not mesh.is_watertight or not mesh.is_volume:
+            raise ValueError("Fluid fill mesh must be a closed, oriented volume.")
+        lower, upper = mesh.bounds
+    else:
+        if mesh_path is not None:
+            raise ValueError("mesh_path is only valid for mesh_volume.")
+        if kind == "box":
+            extent = np.asarray(size, dtype=float)
+        elif kind == "sphere":
+            extent = np.array([2 * radius] * 3)
+        elif kind == "cylinder":
+            extent = np.array([2 * radius, 2 * radius, height])
+        else:
+            raise ValueError(f"Unsupported fluid volume {kind!r}.")
+        if extent.shape != (3,) or not np.isfinite(extent).all() or (extent <= 0).any():
+            raise ValueError("Volume dimensions must be finite and positive.")
+        lower, upper = -extent / 2, extent / 2
+    lower, upper = lower + clearance, upper - clearance
+    counts = [max(0, math.ceil(float(v) / spacing)) for v in upper - lower]
+    if not all(counts):
+        raise ValueError("clearance leaves an empty fill volume.")
+    if math.prod(counts) > max_particles:
+        raise ValueError("Fluid candidate grid exceeds max_particles.")
+    axes = [lo + (np.arange(n) + 0.5) * spacing for lo, n in zip(lower, counts)]
+    points = np.stack(np.meshgrid(*axes, indexing="ij"), axis=-1).reshape(-1, 3)
+    keep = np.all(points < upper, axis=1)
+    if kind == "sphere":
+        keep &= np.linalg.norm(points, axis=1) < radius - clearance
+    elif kind == "cylinder":
+        keep &= np.linalg.norm(points[:, :2], axis=1) < radius - clearance
+    elif mesh is not None:
+        import trimesh
+
+        keep &= mesh.contains(points)
+        if clearance:
+            _, distance, _ = trimesh.proximity.closest_point(mesh, points)
+            keep &= distance >= clearance
+    points = points[keep]
+    if not len(points):
+        raise ValueError("Fluid volume contains no particles at this spacing.")
+    return np.ascontiguousarray(points, dtype=np.float32)
diff --git a/embodichain/lab/sim/_runtime_controls.py b/embodichain/lab/sim/_runtime_controls.py
index 6c0b23954..b56f37c67 100644
--- a/embodichain/lab/sim/_runtime_controls.py
+++ b/embodichain/lab/sim/_runtime_controls.py
@@ -122,3 +122,59 @@ def close(self) -> None:
         """Release runtime particle references retained after initialization."""
         self._particle_set = None
         self._initial_positions = None
+
+
+class _KinematicRigidTrajectoryControl(RuntimeControl):
+    """Sample world pose tracks on the native solver substep clock."""
+
+    def __init__(self, target: str, poses: np.ndarray, fps: float) -> None:
+        from scipy.spatial.transform import Rotation, Slerp
+
+        self.target = target
+        self.poses = poses.copy()
+        self.fps = fps
+        self._times = np.arange(len(poses)) / fps
+        self._rotations = Rotation.from_quat(poses[:, 3:])
+        self._slerp = Slerp(self._times, self._rotations) if len(poses) > 1 else None
+        self._actor = None
+        self.reset()
+
+    def initialize(self, context: Any) -> None:
+        self._actor = context.result.get_actor(self.target)
+        self.reset()
+
+    def exclusive_resource_claims(self) -> tuple[object, ...]:
+        return (("kinematic_rigid_trajectory", self.target),)
+
+    def reset(self) -> None:
+        self._elapsed = 0.0
+        self._previous = None
+
+    def __call__(
+        self, context: Any, substep_index: int, substep_count: int, substep_dt: float
+    ) -> None:
+        from scipy.spatial.transform import Rotation
+
+        del context, substep_index, substep_count
+        if self._actor is None:
+            raise RuntimeError("Kinematic rigid trajectory is not initialized.")
+        t = min(self._elapsed, self._times[-1])
+        position = np.array(
+            [np.interp(t, self._times, self.poses[:, axis]) for axis in range(3)]
+        )
+        rotation = self._rotations[0] if self._slerp is None else self._slerp(t)
+        pose = np.eye(4, dtype=np.float32)
+        pose[:3, :3] = rotation.as_matrix()
+        pose[:3, 3] = position
+        previous = pose if self._previous is None else self._previous
+        self._actor.set_world_pose(pose)
+        self._actor.set_linear_velocity((pose[:3, 3] - previous[:3, 3]) / substep_dt)
+        self._actor.set_angular_velocity(
+            Rotation.from_matrix(pose[:3, :3] @ previous[:3, :3].T).as_rotvec()
+            / substep_dt
+        )
+        self._previous = pose
+        self._elapsed += substep_dt
+
+    def close(self) -> None:
+        self._actor = None
diff --git a/embodichain/lab/sim/_startup_summary.py b/embodichain/lab/sim/_startup_summary.py
index 03b601761..0b8a15461 100644
--- a/embodichain/lab/sim/_startup_summary.py
+++ b/embodichain/lab/sim/_startup_summary.py
@@ -264,6 +264,7 @@ def scene_rows(sim: SimulationManager) -> list[Row]:
             ("Scene / env", "Rigid objects", str(len(sim._rigid_objects))),
             ("Scene / env", "Object groups", str(len(sim._rigid_object_groups))),
             ("Scene / env", "Deformables", str(len(sim._deformable_objects))),
+            ("Scene / env", "Fluids", str(len(getattr(sim, "_fluid_objects", {})))),
             ("Scene / env", "Sensors", str(len(sim._sensors))),
             (
                 "Scene",
diff --git a/embodichain/lab/sim/cfg/__init__.py b/embodichain/lab/sim/cfg/__init__.py
index c4ddd4fef..613bd42f5 100644
--- a/embodichain/lab/sim/cfg/__init__.py
+++ b/embodichain/lab/sim/cfg/__init__.py
@@ -39,6 +39,7 @@
     link_attrs_from_dict,
 )
 from .asset import AssetPhysicsMode, ObjectBaseCfg, _resolve_asset_physics_mode
+from .fluid import FluidObjectCfg, FluidSpawnCfg, FluidRenderCfg
 from .deformable import (
     SurfaceElementPropertiesCfg,
     VolumeDeformableMeshingCfg,
@@ -86,6 +87,9 @@
 from .robot import RobotCfg, RobotPresetCfg  # noqa: E402
 
 __all__ = [
+    "FluidObjectCfg",
+    "FluidSpawnCfg",
+    "FluidRenderCfg",
     "DEFAULT_RENDERER",
     "AssetPhysicsMode",
     "DenoisingCfg",
diff --git a/embodichain/lab/sim/cfg/fluid.py b/embodichain/lab/sim/cfg/fluid.py
new file mode 100644
index 000000000..f73ecd055
--- /dev/null
+++ b/embodichain/lab/sim/cfg/fluid.py
@@ -0,0 +1,168 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Initial-fill fluid configuration; numerical solvers remain owned by DexSim."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import Any, Literal
+import math
+import numpy as np
+
+from embodichain.utils import configclass
+from ..material import VisualMaterialCfg
+from .asset import ObjectBaseCfg
+
+__all__ = ["FluidSpawnCfg", "FluidRenderCfg", "FluidObjectCfg"]
+
+
+@configclass
+class FluidSpawnCfg:
+    """Deterministic local particle source, with no runtime emission.
+
+    Mesh sources describe a closed *fill volume*, not the solid container wall.
+    File paths are resolved by the caller; NPZ sources use the ``positions`` key.
+    ``reference_uid`` resolves a declared rigid body's initial pose once.
+    """
+
+    kind: Literal["points", "box", "sphere", "cylinder", "mesh_volume"] = "points"
+    positions: Any = None
+    file_path: str | None = None
+    size: tuple[float, float, float] = (0.05, 0.05, 0.05)
+    radius: float = 0.025
+    height: float = 0.05
+    clearance: float = 0.0
+    reference_uid: str | None = None
+    initial_velocity: tuple[float, float, float] = (0.0, 0.0, 0.0)
+    max_particles: int = 1_000_000
+    """Bound the candidate grid allocation before sampling."""
+
+    def sample(self, spacing: float) -> np.ndarray:
+        """Return independent finite local positions of shape ``(N, 3)``.
+
+        Args:
+            spacing: Positive lattice spacing in metres.
+
+        Returns:
+            Contiguous float32 positions in deterministic lattice order.
+        """
+        from embodichain.compute.geometry.particles import sample_volume_particles
+
+        if not math.isfinite(spacing) or spacing <= 0:
+            raise ValueError("spacing must be finite and positive.")
+        if type(self.max_particles) is not int or self.max_particles <= 0:
+            raise ValueError("max_particles must be a positive integer.")
+        if self.kind == "points":
+            if (self.positions is None) == (self.file_path is None):
+                raise ValueError(
+                    "points requires exactly one of positions or file_path."
+                )
+            if self.file_path is not None:
+                with np.load(self.file_path, allow_pickle=False) as source:
+                    points = np.asarray(source["positions"], dtype=np.float32).copy()
+            else:
+                points = np.asarray(self.positions, dtype=np.float32).copy()
+        else:
+            if self.positions is not None:
+                raise ValueError("positions is only valid for kind='points'.")
+            points = sample_volume_particles(
+                self.kind,
+                spacing,
+                size=self.size,
+                radius=self.radius,
+                height=self.height,
+                clearance=self.clearance,
+                mesh_path=self.file_path,
+                max_particles=self.max_particles,
+            )
+        if (
+            points.ndim != 2
+            or points.shape[1] != 3
+            or not len(points)
+            or not np.isfinite(points).all()
+        ):
+            raise ValueError("Fluid positions must be finite, nonempty (N, 3) points.")
+        if len(points) > self.max_particles:
+            raise ValueError("Fluid source exceeds max_particles.")
+        return np.ascontiguousarray(points)
+
+
+@configclass
+class FluidRenderCfg:
+    """Native particle/surface display, independent of physical particle state.
+
+    ``mesh_surface`` forwards DexSim FluidMeshSurfaceRenderDesc fields directly.
+    Optional world-space clipping affects only a detached render copy.
+    ``mode="none"`` currently requires the simulation NoRender backend.
+    """
+
+    mode: Literal["none", "particles", "mesh_surface"] = "particles"
+    render_radius: float | None = None
+    mesh_surface: dict[str, Any] = {}
+    material: VisualMaterialCfg | None = None
+    clip_below_z: float | None = None
+
+    def __post_init__(self) -> None:
+        if isinstance(self.material, Mapping):
+            self.material = VisualMaterialCfg.from_dict(dict(self.material))
+        if self.mode not in {"none", "particles", "mesh_surface"}:
+            raise ValueError("Unsupported fluid render mode.")
+        for name in ("render_radius", "clip_below_z"):
+            value = getattr(self, name)
+            if value is not None and not math.isfinite(value):
+                raise ValueError(f"{name} must be finite.")
+        if self.render_radius is not None and self.render_radius <= 0:
+            raise ValueError("render_radius must be positive.")
+
+
+@configclass
+class FluidObjectCfg(ObjectBaseCfg):
+    """A fixed-count Newton SPH fluid, declared before scene preparation.
+
+    Density and spacing determine mass and must agree across fluid objects.
+    Solver viscosity, surface tension and iteration controls belong exclusively
+    to DexSim's SPHSolverCfg supplied to NewtonPhysicsCfg.solver_cfg.
+    """
+
+    density: float = 1000.0
+    particle_spacing: float = 0.0015
+    particle_radius: float | None = None
+    spawn: FluidSpawnCfg = FluidSpawnCfg()
+    render: FluidRenderCfg = FluidRenderCfg()
+
+    def __post_init__(self) -> None:
+        if isinstance(self.spawn, Mapping):
+            self.spawn = FluidSpawnCfg(**self.spawn)
+        if isinstance(self.render, Mapping):
+            self.render = FluidRenderCfg(**self.render)
+        for name in ("density", "particle_spacing", "particle_radius"):
+            value = getattr(self, name)
+            if value is not None and (not math.isfinite(value) or value <= 0):
+                raise ValueError(f"{name} must be finite and positive.")
+        velocity = np.asarray(self.spawn.initial_velocity)
+        if velocity.shape != (3,) or not np.isfinite(velocity).all():
+            raise ValueError("initial_velocity must contain three finite values.")
+        if (
+            not isinstance(self.spawn.max_particles, int)
+            or self.spawn.max_particles <= 0
+        ):
+            raise ValueError("max_particles must be a positive integer.")
+
+    @classmethod
+    def from_dict(cls, init_dict: dict[str, Any]) -> FluidObjectCfg:
+        """Decode the same typed configuration used by Python callers."""
+        return cls(**init_dict)
diff --git a/embodichain/lab/sim/cfg/physics.py b/embodichain/lab/sim/cfg/physics.py
index 314eca508..d53be2113 100644
--- a/embodichain/lab/sim/cfg/physics.py
+++ b/embodichain/lab/sim/cfg/physics.py
@@ -402,6 +402,21 @@ def to_dexsim_cfg(
             "featherstone": FeatherstoneSolverCfg,
             "vbd": VBDSolverCfg,
         }
+        # Import SPH lazily so older DexSim builds can still run other solvers.
+        if isinstance(self.solver_cfg, Mapping):
+            requested = (
+                self.solver_cfg.get("solver_type")
+                or self.solver_cfg.get("class_type")
+                or "auto"
+            )
+            if _normalize_newton_solver_type(str(requested)) == "sph":
+                try:
+                    from dexsim.engine.newton_physics import SPHSolverCfg
+                except ImportError as exc:
+                    raise ImportError(
+                        "SPH requires a DexSim build exporting SPHSolverCfg."
+                    ) from exc
+                solver_cfg_map["sph"] = SPHSolverCfg
         solver_cfg = _newton_solver_cfg_to_dexsim(
             solver_cfg=self.solver_cfg,
             solver_cfg_map=solver_cfg_map,
@@ -460,6 +475,8 @@ def _normalize_newton_solver_type(solver_type: str) -> str:
     """Normalize public EmbodiChain and DexSim Newton solver aliases."""
     key = solver_type.replace("-", "_").lower()
     aliases = {
+        "sph": "sph",
+        "sphsolvercfg": "sph",
         "auto": "auto",
         "autosolver": "auto",
         "autosolvercfg": "auto",
@@ -497,7 +514,7 @@ def _normalize_newton_solver_type(solver_type: str) -> str:
         logger.log_error(
             f"Unsupported Newton solver type '{solver_type}'. "
             "Expected one of 'auto', 'dexuni', 'mjwarp', 'xpbd', 'semi_implicit', "
-            "'featherstone', or 'vbd'."
+            "'featherstone', 'vbd', or 'sph'."
         )
     return aliases[key]
 
diff --git a/embodichain/lab/sim/cfg/rigid_object.py b/embodichain/lab/sim/cfg/rigid_object.py
index facc35b76..430a6b6ba 100644
--- a/embodichain/lab/sim/cfg/rigid_object.py
+++ b/embodichain/lab/sim/cfg/rigid_object.py
@@ -46,6 +46,12 @@ class RigidObjectCfg(ObjectBaseCfg):
 
     # TODO: supoort basic primitive shapes, such as box, sphere, etc cfg and spawn method.
 
+    visual_shape: ShapeCfg | None = None
+    """Optional independent render geometry; shape remains collision geometry."""
+
+    render_enabled: bool = True
+    """Whether to create native render geometry for this rigid object."""
+
     attrs: RigidBodyPhysicsCfg = RigidBodyPhysicsCfg()
     """Rigid-body physics.
 
@@ -73,6 +79,8 @@ def resolve_asset_physics_mode(self) -> AssetPhysicsMode:
     def from_dict(cls, init_dict: Dict[str, Any]) -> RigidObjectCfg:
         """Parse a rigid object and normalize legacy mesh collision ownership."""
         data = dict(init_dict)
+        if isinstance(data.get("visual_shape"), Mapping):
+            data["visual_shape"] = ShapeCfg.from_dict(data["visual_shape"])
         attrs_value = data.get("attrs")
         if isinstance(attrs_value, Mapping) and "mesh_collision_props" in attrs_value:
             shape_value = data.get("shape")
diff --git a/embodichain/lab/sim/material.py b/embodichain/lab/sim/material.py
index d88c0410f..cc2e41746 100644
--- a/embodichain/lab/sim/material.py
+++ b/embodichain/lab/sim/material.py
@@ -76,6 +76,12 @@ class VisualMaterialCfg:
     ior: float = 1.5
     """Index of refraction for PBR materials, only used in ray tracing."""
 
+    transmission: float | None = None
+    """Optional native PBR transmission coefficient."""
+    volume_thickness: float | None = None
+    volume_attenuation_distance: float | None = None
+    volume_attenuation_color: tuple[float, float, float] | None = None
+
     material_type: str = "BRDF"
     """Ray tracing material type. Options: 'BRDF', 'BTDF', 'BSDF'"""
 
@@ -94,6 +100,24 @@ def from_dict(cls, cfg_dict: dict) -> VisualMaterialCfg:
         return base
 
 
+def _apply_volume_material_properties(
+    native: MaterialInst, cfg: VisualMaterialCfg
+) -> None:
+    """Apply optical volume parameters not carried by Spawn MaterialDesc."""
+    for name in (
+        "transmission",
+        "volume_thickness",
+        "volume_attenuation_distance",
+        "volume_attenuation_color",
+    ):
+        value = getattr(cfg, name)
+        if value is not None:
+            if isinstance(value, (list, tuple)):
+                native.set_pbr_param(name, *value)
+            else:
+                native.set_pbr_param(name, value)
+
+
 class VisualMaterial:
     """Visual material definition in the simulation environment.
 
@@ -151,6 +175,7 @@ def set_default_properties(
         mat_inst.set_ao_texture(cfg.ao_texture)
 
         mat_inst.set_ior(cfg.ior)
+        _apply_volume_material_properties(mat_inst.mat, cfg)
         mat_inst.mat.update_pbr_material_type(self.MAT_TYPE_MAPPING[cfg.material_type])
 
     def create_instance(self, uid: str) -> VisualMaterialInst:
diff --git a/embodichain/lab/sim/objects/__init__.py b/embodichain/lab/sim/objects/__init__.py
index af97f9741..974e28f58 100644
--- a/embodichain/lab/sim/objects/__init__.py
+++ b/embodichain/lab/sim/objects/__init__.py
@@ -28,6 +28,7 @@
     RigidBodyGroupData,
     RigidObjectGroupCfg,
 )
+from .fluid import FluidObject, FluidObjectData
 from .deformable import (
     DeformableObject,
     DeformableObjectData,
diff --git a/embodichain/lab/sim/objects/fluid.py b/embodichain/lab/sim/objects/fluid.py
new file mode 100644
index 000000000..727cd8b40
--- /dev/null
+++ b/embodichain/lab/sim/objects/fluid.py
@@ -0,0 +1,224 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Declared fixed-count fluids with Scene-owned particle state."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from typing import Any, Callable, Sequence
+import numpy as np
+import torch
+
+from embodichain.lab.sim.cfg.fluid import FluidObjectCfg
+from embodichain.lab.sim.objects.deformable.data import DeformableObjectData
+from embodichain.utils.math import matrix_from_quat
+
+__all__ = ["FluidObject", "FluidObjectData"]
+
+
+class FluidObjectData:
+    """Detached world-frame state snapshots, shaped ``(E, N, 3|6)``.
+
+    The shared Scene particle batch adapter is composed without inheriting
+    any deformable connectivity or fixed render topology.
+    """
+
+    def __init__(
+        self, entities: Sequence[Any], scene: Any, device: torch.device
+    ) -> None:
+        self._state = DeformableObjectData(entities, scene, device)
+
+    @property
+    def particle_pos_w(self) -> torch.Tensor:
+        """Current positions in metres; modifying the result does not write state."""
+        return self._state.nodal_pos_w
+
+    @property
+    def particle_vel_w(self) -> torch.Tensor:
+        """Current world-frame velocities in metres per second."""
+        return self._state.nodal_vel_w
+
+    @property
+    def particle_state_w(self) -> torch.Tensor:
+        """Concatenated positions and velocities, with shape ``(E, N, 6)``."""
+        return self._state.nodal_state_w
+
+    @property
+    def default_particle_state_w(self) -> torch.Tensor:
+        """Independent copy of the finalized initial state."""
+        return self._state.default_nodal_state_w
+
+
+class FluidObject:
+    """One logical fluid declared and bound by SimulationManager.
+
+    Particle count is fixed. There is no physical rigid root pose or persistent
+    attachment to the initial container. Pose reset transforms the rest template.
+    """
+
+    def __init__(
+        self,
+        cfg: FluidObjectCfg,
+        positions: np.ndarray,
+        pose: np.ndarray,
+        device: torch.device,
+        *,
+        on_write: Callable[[], None],
+    ) -> None:
+        self.cfg = deepcopy(cfg)
+        self.uid = cfg.uid
+        self.device = device
+        self._positions = torch.as_tensor(positions.copy(), device=device)
+        self._initial_pose = torch.as_tensor(pose.copy(), device=device)
+        self._entities: list[Any] = []
+        self._spawn_result = None
+        self._data: FluidObjectData | None = None
+        self._num_instances = 0
+        self._on_write = on_write
+
+    def _initialize_spawn_declaration(self, num_instances: int) -> None:
+        self._num_instances = num_instances
+
+    @property
+    def is_declared(self) -> bool:
+        """Whether state is still unavailable pending ``prepare()``."""
+        return self._data is None
+
+    @property
+    def num_instances(self) -> int:
+        """Number of scene-owned replicas."""
+        return self._num_instances
+
+    @property
+    def num_particles(self) -> int:
+        """Fixed particle count per replica."""
+        return len(self._positions)
+
+    @property
+    def particle_mass(self) -> float:
+        """Uniform particle mass in kilograms."""
+        return self.cfg.density * self.cfg.particle_spacing**3
+
+    @property
+    def nominal_volume(self) -> float:
+        """Rest volume in cubic metres, independent of render reconstruction."""
+        return self.num_particles * self.cfg.particle_spacing**3
+
+    @property
+    def data(self) -> FluidObjectData:
+        """Return prepared particle data, or raise before binding."""
+        if self._data is None:
+            raise RuntimeError("Fluid state requires SimulationManager.prepare().")
+        return self._data
+
+    def attach_spawn_handles(self, entities: Sequence[Any]) -> None:
+        """Attach the replicas materialized by Spawn."""
+        if len(entities) != self.num_instances:
+            raise RuntimeError("Fluid replica count does not match the declaration.")
+        self._entities = list(entities)
+
+    def bind_spawn(self, scene: Any) -> None:
+        """Bind Scene batch state and capture configured initial velocities."""
+        if self._data is not None:
+            return
+        data = FluidObjectData(self._entities, scene, self.device)
+        if data._state.n_nodes != self.num_particles:
+            raise RuntimeError("Spawn changed the fluid particle count.")
+        state = data.particle_state_w
+        velocity = torch.as_tensor(
+            self.cfg.spawn.initial_velocity, device=self.device, dtype=torch.float32
+        )
+        state[..., 3:] = velocity @ self._initial_pose[:3, :3].T
+        data._state._apply_nodal_state(
+            state[..., :3], state[..., 3:], list(range(self.num_instances))
+        )
+        data._state._default_nodal_state_w = state.clone()
+        self._spawn_result = scene
+        self._data = data
+
+    def _indices(self, env_ids: Sequence[int] | None) -> list[int]:
+        if env_ids is None:
+            return list(range(self.num_instances))
+        ids = torch.as_tensor(env_ids)
+        if ids.ndim != 1 or (
+            ids.numel() and ids.dtype not in (torch.int32, torch.int64)
+        ):
+            raise ValueError("env_ids must be a one-dimensional integer selection.")
+        result = ids.tolist()
+        if len(set(result)) != len(result) or any(
+            i < 0 or i >= self.num_instances for i in result
+        ):
+            raise ValueError("env_ids must be unique valid instance indices.")
+        return result
+
+    def write_particle_state_to_sim(
+        self,
+        state: torch.Tensor,
+        env_ids: Sequence[int] | None = None,
+    ) -> None:
+        """Write selected world-frame positions and velocities without stepping.
+
+        Args:
+            state: Finite floating tensor ``(selected E, N, 6)``.
+            env_ids: Unique instance indices; None selects all instances.
+        """
+        data = self.data
+        ids = self._indices(env_ids)
+        if not isinstance(state, torch.Tensor) or not state.is_floating_point():
+            raise TypeError("Fluid state must be a floating Torch tensor.")
+        if tuple(state.shape) != (len(ids), self.num_particles, 6):
+            raise ValueError("Fluid state must have shape (selected E, N, 6).")
+        if not torch.isfinite(state).all():
+            raise ValueError("Fluid state must be finite.")
+        if not ids:
+            return
+        self._on_write()
+        data._state._apply_nodal_state(state[..., :3], state[..., 3:], ids)
+
+    def reset(self, env_ids: Sequence[int] | None = None) -> None:
+        """Restore selected initial particle states without advancing time."""
+        ids = self._indices(env_ids)
+        self.write_particle_state_to_sim(self.data.default_particle_state_w[ids], ids)
+
+    def reset_to_pose(
+        self, pose_w: torch.Tensor, env_ids: Sequence[int] | None = None
+    ) -> None:
+        """Place the local rest template at world poses ``xyz + xyzw``.
+
+        Args:
+            pose_w: Finite ``(selected E, 7)`` poses with unit quaternions.
+            env_ids: Unique instance indices, or None for all.
+        """
+        ids = self._indices(env_ids)
+        if tuple(pose_w.shape) != (len(ids), 7) or not torch.isfinite(pose_w).all():
+            raise ValueError("pose_w must be finite (selected E, 7).")
+        pose = pose_w.to(device=self.device, dtype=torch.float32)
+        if not torch.allclose(
+            pose[:, 3:].norm(dim=-1),
+            torch.ones(len(ids), device=self.device),
+            atol=1e-5,
+        ):
+            raise ValueError("pose_w requires unit xyzw quaternions.")
+        rotation = matrix_from_quat(pose[:, 3:])
+        points = (
+            torch.einsum("eij,nj->eni", rotation, self._positions) + pose[:, None, :3]
+        )
+        velocity = torch.as_tensor(
+            self.cfg.spawn.initial_velocity, device=self.device, dtype=torch.float32
+        )
+        velocities = (rotation @ velocity)[..., None, :].expand_as(points)
+        self.write_particle_state_to_sim(torch.cat((points, velocities), dim=-1), ids)
diff --git a/embodichain/lab/sim/objects/rigid_object.py b/embodichain/lab/sim/objects/rigid_object.py
index 64ea84d4b..ad9acc056 100644
--- a/embodichain/lab/sim/objects/rigid_object.py
+++ b/embodichain/lab/sim/objects/rigid_object.py
@@ -75,6 +75,7 @@
     _restore_render_materials,
     _set_render_material,
     _wrap_first_render_material,
+    _apply_volume_material_properties,
 )
 from ._mesh_utils import (
     get_combined_triangles,
@@ -496,7 +497,9 @@ def _initialize_spawn_bound(self, result: Scene) -> None:
 
     def _apply_spawn_visual_uv(self) -> None:
         """Apply configured projective UV mapping to spawned render bodies."""
-        shape = self.cfg.shape
+        shape = getattr(self.cfg, "visual_shape", None) or getattr(
+            self.cfg, "shape", None
+        )
         if not isinstance(shape, MeshCfg) or not shape.compute_uv:
             return
 
@@ -935,6 +938,78 @@ def add_force_torque(
         else:
             logger.log_error("Cannot apply force or torque before body view is ready.")
 
+    def set_kinematic_state(
+        self,
+        pose_w: torch.Tensor,
+        lin_vel_w: torch.Tensor,
+        ang_vel_w: torch.Tensor,
+        env_ids: Sequence[int] | None = None,
+    ) -> None:
+        """Set a kinematic boundary's world pose and twist without stepping.
+
+        Args:
+            pose_w: World poses, ``(E, 7)`` xyz+xyzw or ``(E, 4, 4)``.
+            lin_vel_w: World linear velocity ``(E, 3)``, m/s.
+            ang_vel_w: World angular velocity ``(E, 3)``, rad/s.
+            env_ids: Selected replicas, or None for all.
+        """
+        if self.body_type != "kinematic":
+            raise ValueError("set_kinematic_state requires a kinematic rigid body.")
+        selection = torch.as_tensor(self._all_indices if env_ids is None else env_ids)
+        if selection.ndim != 1 or (
+            selection.numel() and selection.dtype not in (torch.int32, torch.int64)
+        ):
+            raise ValueError("env_ids must be a one-dimensional integer selection.")
+        ids = selection.tolist()
+        if len(set(ids)) != len(ids) or any(
+            i < 0 or i >= self.num_instances for i in ids
+        ):
+            raise ValueError("env_ids must be unique valid indices.")
+        if tuple(pose_w.shape) not in {(len(ids), 7), (len(ids), 4, 4)}:
+            raise ValueError("pose_w has an invalid shape.")
+        if any(tuple(v.shape) != (len(ids), 3) for v in (lin_vel_w, ang_vel_w)):
+            raise ValueError("Boundary velocities must have shape (E, 3).")
+        if not all(torch.isfinite(v).all() for v in (pose_w, lin_vel_w, ang_vel_w)):
+            raise ValueError("Kinematic state must be finite.")
+        if pose_w.ndim == 2:
+            if not torch.allclose(
+                pose_w[:, 3:].norm(dim=-1),
+                torch.ones(len(ids), device=pose_w.device, dtype=pose_w.dtype),
+                atol=1e-5,
+            ):
+                raise ValueError("pose_w requires unit xyzw quaternions.")
+        else:
+            rotations = pose_w[:, :3, :3]
+            identity = torch.eye(3, device=pose_w.device, dtype=pose_w.dtype).expand(
+                len(ids), -1, -1
+            )
+            if (
+                not torch.allclose(
+                    rotations.transpose(-1, -2) @ rotations, identity, atol=1e-5
+                )
+                or not torch.allclose(
+                    torch.linalg.det(rotations),
+                    torch.ones(len(ids), device=pose_w.device, dtype=pose_w.dtype),
+                    atol=1e-5,
+                )
+                or not torch.allclose(
+                    pose_w[:, 3], pose_w.new_tensor([0, 0, 0, 1]).expand(len(ids), -1)
+                )
+            ):
+                raise ValueError("pose_w requires rigid homogeneous transforms.")
+        if self._data is None or not self._data.body_view.is_ready:
+            raise RuntimeError("Kinematic state requires prepare().")
+        if pose_w.ndim == 2:
+            pose = torch.eye(4).repeat(len(ids), 1, 1)
+            pose[:, :3, :3] = matrix_from_quat(pose_w[:, 3:].cpu())
+            pose[:, :3, 3] = pose_w[:, :3].cpu()
+        else:
+            pose = pose_w.cpu()
+        for row, index in enumerate(ids):
+            self._entities[index].set_world_pose(pose[row].numpy())
+            self._entities[index].set_linear_velocity(lin_vel_w[row].cpu().numpy())
+            self._entities[index].set_angular_velocity(ang_vel_w[row].cpu().numpy())
+
     def set_velocity(
         self,
         lin_vel: torch.Tensor | None = None,
@@ -1506,6 +1581,14 @@ def _initialize_existing_visual_material(self) -> None:
             if render_body is None:
                 continue
             original_materials = _capture_render_materials(render_body)
+            shape = getattr(self.cfg, "visual_shape", None) or getattr(
+                self.cfg, "shape", None
+            )
+            material_cfg = getattr(shape, "visual_material", None)
+            if material_cfg is not None:
+                for native in original_materials:
+                    if native is not None:
+                        _apply_volume_material_properties(native, material_cfg)
             self._original_visual_material[env_idx] = original_materials
             wrapped = _wrap_first_render_material(original_materials)
             if wrapped is not None:
diff --git a/embodichain/lab/sim/physics/newton.py b/embodichain/lab/sim/physics/newton.py
index cbb37d11b..688af307f 100644
--- a/embodichain/lab/sim/physics/newton.py
+++ b/embodichain/lab/sim/physics/newton.py
@@ -29,6 +29,7 @@
     import dexsim
 
     from embodichain.lab.sim.sim_manager import SimulationManagerCfg
+    from embodichain.lab.sim.objects.fluid import FluidObject
 
 __all__ = ["NewtonPhysicsBackend"]
 
@@ -111,7 +112,65 @@ def sync_render_state(self, result: "dexsim.scene.Scene") -> None:
             raise RuntimeError(
                 "Newton backend is unavailable for render-state synchronization."
             )
-        backend.sync_to_dexsim(result.world)
+        fluids = getattr(self._manager, "_fluid_objects", {})
+        clipped = [f for f in fluids.values() if f.cfg.render.clip_below_z is not None]
+        if not clipped:
+            backend.sync_to_dexsim(result.world)
+            return
+        positions = wp.to_torch(backend.state_0.particle_q).clone()
+        for fluid in clipped:
+            for entity in fluid._entities:
+                binding = entity._physics_binding
+                start = binding.particle_start
+                selected = positions[start : start + entity.particle_count]
+                selected[selected[:, 2] < fluid.cfg.render.clip_below_z] = float("nan")
+        backend.render_sync.sync(
+            result.world,
+            backend.state_0.body_q,
+            wp.from_torch(positions, dtype=wp.vec3),
+            simulation_time=backend.sim_time,
+        )
+
+    def invalidate_fluid_state(self) -> None:
+        """Invalidate single-world SPH history after an explicit state write.
+
+        DFSPH rebuilds neighbors, density and Jacobi scratch on each step.
+        Optional SDF-penalty body history is reset to the current pose.
+        """
+        from dexsim.engine.newton_physics.backend_registry import get_newton_backend
+
+        backend = get_newton_backend(self._manager._world)
+        if backend is None or backend.solver_type != "sph":
+            raise RuntimeError("Prepared SPH backend required.")
+        backend.invalidate_cuda_graph("fluid state write")
+        boundary = backend.solver.sdf_boundary
+        if boundary is not None and boundary._body_q_prev is not None:
+            boundary._body_q_prev.assign(backend.state_0.body_q)
+
+    def fluid_diagnostics(self, fluid: FluidObject) -> dict[str, object]:
+        """Read detached native SPH diagnostics without publishing or stepping."""
+        from dexsim.engine.newton_physics.backend_registry import get_newton_backend
+        import torch
+
+        backend = get_newton_backend(self._manager._world)
+        solver = backend.solver
+        density = wp.to_torch(solver.particle_density)
+        values = []
+        for entity in fluid._entities:
+            start = entity._physics_binding.particle_start
+            values.append(density[start : start + entity.particle_count].clone())
+        return {
+            "particle_density": torch.stack(values),
+            "volume_map_enabled": solver.volume_map_boundary is not None,
+            "sdf_penalty_enabled": solver.sdf_boundary is not None,
+            "boundary_shape_count": (
+                0
+                if solver.volume_map_boundary is None
+                else solver.volume_map_boundary.shape_count
+            ),
+            "rest_density_kg_m3": solver.rest_density,
+            "viscosity": solver.viscosity,
+        }
 
     def _step(self, physics_dt: float) -> None:
         """Let the manager batch window and offscreen consumers after stepping."""
diff --git a/embodichain/lab/sim/shapes.py b/embodichain/lab/sim/shapes.py
index 2c2e4612b..dba47693e 100755
--- a/embodichain/lab/sim/shapes.py
+++ b/embodichain/lab/sim/shapes.py
@@ -71,6 +71,9 @@ class MeshCollisionCfg:
     VisACD requires a DexSim build with CUDA/OptiX support.
     """
 
+    force_sdf: bool | None = None
+    """Optional Newton SDF strategy override; False uses an eagerly baked SDF."""
+
     sdf_resolution: int | None = None
     """Maximum SDF grid resolution; valid only for the ``sdf`` strategy."""
 
@@ -126,6 +129,7 @@ def __post_init__(self) -> None:
             )
 
         sdf_values = {
+            "force_sdf": self.force_sdf,
             "sdf_resolution": self.sdf_resolution,
             "is_hydroelastic": self.is_hydroelastic,
             "sdf_narrow_band_range": self.sdf_narrow_band_range,
@@ -187,6 +191,9 @@ def from_dict(cls, init_dict: Dict[str, Any]) -> MeshCollisionCfg:
             "force_sdf",
             "sdf_max_resolution",
         }
+        explicit_force_sdf = data.get("force_sdf") if "approximation" in data else None
+        if "approximation" in data:
+            legacy_fields.discard("force_sdf")
         has_legacy_fields = bool(legacy_fields.intersection(data))
         if has_legacy_fields:
             warnings.warn(
@@ -230,6 +237,8 @@ def from_dict(cls, init_dict: Dict[str, Any]) -> MeshCollisionCfg:
 
         if data.get("sdf_resolution") == 0:
             data.pop("sdf_resolution")
+        if explicit_force_sdf is not None:
+            data["force_sdf"] = explicit_force_sdf
         return cls(**data)
 
 
@@ -241,6 +250,7 @@ def _mesh_collision_cfg_init_with_legacy_max_hulls(
     approximation: MeshCollisionApproximation | None = None,
     max_hulls: int | None = None,
     acd_method: Literal["visacd", "coacd", "vhacd"] | None = None,
+    force_sdf: bool | None = None,
     sdf_resolution: int | None = None,
     is_hydroelastic: bool | None = None,
     sdf_narrow_band_range: tuple[float, float] | None = None,
@@ -283,6 +293,7 @@ def _mesh_collision_cfg_init_with_legacy_max_hulls(
         approximation="convex_hull" if approximation is None else approximation,
         max_hulls=max_hulls,
         acd_method=acd_method,
+        force_sdf=force_sdf,
         sdf_resolution=sdf_resolution,
         is_hydroelastic=is_hydroelastic,
         sdf_narrow_band_range=sdf_narrow_band_range,
diff --git a/embodichain/lab/sim/sim_manager.py b/embodichain/lab/sim/sim_manager.py
index bc2be6789..485f6a25c 100644
--- a/embodichain/lab/sim/sim_manager.py
+++ b/embodichain/lab/sim/sim_manager.py
@@ -16,6 +16,10 @@
 
 from __future__ import annotations
 
+from collections.abc import Mapping
+from embodichain.lab.sim.cfg.fluid import FluidObjectCfg
+from embodichain.lab.sim.objects.fluid import FluidObject
+
 import os
 import gc
 import sys
@@ -680,6 +684,8 @@ def __init__(
         self._constraints: Dict[str, RigidConstraint] = dict()
         self._rigid_object_groups: Dict[str, RigidObjectGroup] = dict()
         self._deformable_objects: Dict[str, DeformableObject] = dict()
+        self._fluid_objects: dict[str, FluidObject] = {}
+        self._rigid_trajectory_controls: dict[str, tuple] = {}
         self._articulations: Dict[str, Articulation] = dict()
         self._robots: Dict[str, Robot] = dict()
 
@@ -957,6 +963,7 @@ def asset_uids(self) -> List[str]:
         uid_list.extend(list(self._rigid_objects.keys()))
         uid_list.extend(list(self._rigid_object_groups.keys()))
         uid_list.extend(list(self._deformable_objects.keys()))
+        uid_list.extend(list(self._fluid_objects.keys()))
         uid_list.extend(list(self._articulations.keys()))
         return uid_list
 
@@ -1024,6 +1031,7 @@ def start_visualization(self) -> VisualizationRuntime | None:
             getattr(self, "_rigid_objects", {}),
             getattr(self, "_rigid_object_groups", {}),
             getattr(self, "_deformable_objects", {}),
+            getattr(self, "_fluid_objects", {}),
             getattr(self, "_articulations", {}),
             getattr(self, "_robots", {}),
             getattr(self, "_sensors", {}),
@@ -1300,6 +1308,61 @@ def _init_sim_resources(self) -> None:
 
         self._default_resources = SimResources()
 
+    def register_kinematic_rigid_trajectory(
+        self,
+        uid: str,
+        world_poses: torch.Tensor | np.ndarray,
+        *,
+        fps: float,
+    ) -> None:
+        """Drive declared kinematic rigid bodies at each Newton solver substep.
+
+        Args:
+            uid: Previously declared kinematic rigid-object UID.
+            world_poses: Finite unit-quaternion xyz+xyzw tracks, ``(E, frames, 7)``.
+            fps: Positive pose sample frequency in Hz. Positions interpolate
+                linearly, orientations use SLERP, and the last pose is held.
+
+        The host-side control disables CUDA graph replay. Register before
+        prepare; reset_objects_state rewinds only selected instance clocks.
+        """
+        from embodichain.lab.sim._runtime_controls import (
+            _KinematicRigidTrajectoryControl,
+        )
+
+        if not self.is_newton_backend or self._spawn_scene.builder.is_finalized:
+            raise RuntimeError("Register Newton rigid trajectories before prepare().")
+        rigid = self._rigid_objects.get(uid)
+        if rigid is None or rigid.body_type != "kinematic":
+            raise ValueError("uid must name a declared kinematic rigid object.")
+        if uid in self._rigid_trajectory_controls:
+            raise ValueError("A rigid trajectory already controls this object.")
+        poses = (
+            world_poses.detach().cpu().numpy()
+            if isinstance(world_poses, torch.Tensor)
+            else np.asarray(world_poses)
+        )
+        if (
+            poses.ndim != 3
+            or poses.shape[0] != self.num_envs
+            or poses.shape[1] == 0
+            or poses.shape[2] != 7
+        ):
+            raise ValueError("world_poses must have shape (E, frames, 7).")
+        if not np.isfinite(poses).all() or not np.allclose(
+            np.linalg.norm(poses[..., 3:], axis=-1), 1.0, atol=1e-5
+        ):
+            raise ValueError("world_poses must be finite with unit xyzw quaternions.")
+        if not np.isfinite(fps) or fps <= 0:
+            raise ValueError("fps must be finite and positive.")
+        controls = tuple(
+            _KinematicRigidTrajectoryControl(f"{arena}/{uid}", poses[i], fps)
+            for i, arena in enumerate(self._spawn_scene.arena_names)
+        )
+        for control in controls:
+            self._spawn_scene.builder.add_runtime_control(control)
+        self._rigid_trajectory_controls[uid] = controls
+
     def register_kinematic_joint_trajectory(
         self,
         uid: str,
@@ -2274,6 +2337,7 @@ def get_asset(
         | RigidObject
         | RigidObjectGroup
         | DeformableObject
+        | FluidObject
         | Articulation
         | None
     ):
@@ -2300,6 +2364,8 @@ def get_asset(
             return self._rigid_object_groups[uid]
         if uid in self._deformable_objects:
             return self._deformable_objects[uid]
+        if uid in self._fluid_objects:
+            return self._fluid_objects[uid]
         if uid in self._articulations:
             return self._articulations[uid]
 
@@ -2651,6 +2717,118 @@ def _declare_rigid_objects(
             self.prepare()
         return [item[3] for item in declarations]
 
+    def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
+        """Declare a fixed-count, single-environment Newton DFSPH fluid.
+
+        Args:
+            cfg: Fluid source, initial state, physical and render properties.
+
+        Returns:
+            A facade bound in place by ``prepare()``.
+        """
+        from dexsim.spawn import FluidDesc, FluidRenderMode, FluidMeshSurfaceRenderDesc
+        from embodichain.lab.sim.spawn.descriptors import _pose_from_cfg
+
+        if self.physics.name != "newton" or self.device.type != "cuda":
+            raise NotImplementedError("Fluid objects require Newton on CUDA.")
+        if self.num_envs != 1:
+            raise NotImplementedError(
+                "Fluid world isolation is not yet qualified; num_envs must be 1."
+            )
+        if (
+            self._active_newton_solver_type != "sph"
+            or self.sim_config.physics_cfg.requires_grad
+        ):
+            raise NotImplementedError("Fluid objects require non-differentiable SPH.")
+        if self.spawn_result is not None:
+            raise NotImplementedError("Declare fluids before prepare().")
+        if not isinstance(cfg, FluidObjectCfg):
+            raise TypeError("Expected FluidObjectCfg.")
+        cfg = cfg.copy()
+        cfg.validate()
+        cfg.__post_init__()
+        if not cfg.uid or cfg.uid in self.asset_uids:
+            raise ValueError(
+                "Fluid uid must be nonempty and unique across scene assets."
+            )
+        for other in self._fluid_objects.values():
+            if (cfg.density, cfg.particle_spacing) != (
+                other.cfg.density,
+                other.cfg.particle_spacing,
+            ):
+                raise ValueError("All fluids must share density and particle_spacing.")
+        configured = self.sim_config.physics_cfg.solver_cfg
+        for key, value in (
+            ("rest_density", cfg.density),
+            ("particle_diameter", cfg.particle_spacing),
+        ):
+            existing = (
+                configured.get(key)
+                if isinstance(configured, Mapping)
+                else getattr(configured, key, None)
+            )
+            if existing is not None and not np.isclose(
+                existing, value, rtol=1e-7, atol=0
+            ):
+                raise ValueError(f"Fluid {key} conflicts with solver_cfg.{key}.")
+        points = cfg.spawn.sample(cfg.particle_spacing)
+        pose = _pose_from_cfg(cfg)
+        if cfg.spawn.reference_uid is not None:
+            reference = self._rigid_objects.get(cfg.spawn.reference_uid)
+            if reference is None:
+                raise ValueError(
+                    "Fluid reference_uid must name an already declared rigid object."
+                )
+            pose = _pose_from_cfg(reference.cfg) @ pose
+        mesh_surface = FluidMeshSurfaceRenderDesc(**cfg.render.mesh_surface)
+        if cfg.render.mode == "none" and self.has_native_renderer:
+            raise NotImplementedError(
+                "Fluid mode='none' currently requires renderer='no-render'."
+            )
+        material = None
+        if self.has_native_renderer and cfg.render.material is not None:
+            material_cfg = cfg.render.material.copy()
+            material_cfg.uid = f"{cfg.uid}_fluid_material"
+            material = self.create_visual_material(material_cfg).inst.mat
+        descriptor = FluidDesc(
+            name=cfg.uid,
+            positions=points,
+            pose=pose,
+            density=cfg.density,
+            particle_spacing=cfg.particle_spacing,
+            particle_radius=cfg.particle_radius,
+            render_radius=cfg.render.render_radius,
+            render_mode=FluidRenderMode(
+                "particles" if cfg.render.mode == "none" else cfg.render.mode
+            ),
+            mesh_surface=mesh_surface,
+            render_material=material,
+            per_env=True,
+        )
+        fluid = FluidObject(
+            cfg, points, pose, self.device, on_write=self._invalidate_fluid_state
+        )
+        self._spawn_scene.declare("fluid_object", cfg.uid, descriptor, facade=fluid)
+        self._fluid_objects[cfg.uid] = fluid
+        self.notify_visualization_topology_changed()
+        return fluid
+
+    def _invalidate_fluid_state(self) -> None:
+        self.physics.invalidate_fluid_state()
+        if self._render_state_published is not None:
+            self._render_state_published = False
+
+    def get_fluid_object(self, uid: str) -> FluidObject | None:
+        """Return the fluid registered under ``uid``, or None."""
+        return self._fluid_objects.get(uid)
+
+    def get_fluid_diagnostics(self, uid: str) -> dict[str, object]:
+        """Return detached diagnostics for a prepared fluid without stepping."""
+        fluid = self._fluid_objects[uid]
+        if fluid.is_declared:
+            raise RuntimeError("Fluid diagnostics require prepare().")
+        return self.physics.fluid_diagnostics(fluid)
+
     def add_deformable_object(self, cfg: DeformableObjectCfg) -> DeformableObject:
         """Declare a volume or surface deformable in the scene.
 
@@ -4693,6 +4871,13 @@ def reset_objects_state(
         for uid, rigid_obj_group in self._rigid_object_groups.items():
             if uid not in excluded_uids:
                 rigid_obj_group.reset(env_ids)
+        for uid, controls in self._rigid_trajectory_controls.items():
+            if uid not in excluded_uids:
+                for index in (range(self.num_envs) if env_ids is None else env_ids):
+                    controls[int(index)].reset()
+        for uid, fluid_obj in self._fluid_objects.items():
+            if uid not in excluded_uids:
+                fluid_obj.reset(env_ids)
         for uid, deformable_obj in self._deformable_objects.items():
             if uid not in excluded_uids:
                 deformable_obj.reset(env_ids)
@@ -4874,6 +5059,7 @@ def _deferred_destroy(self) -> None:
                 "_rigid_objects",
                 "_rigid_object_groups",
                 "_deformable_objects",
+                "_fluid_objects",
                 "_articulations",
                 "_robots",
             ):
@@ -4889,6 +5075,7 @@ def _deferred_destroy(self) -> None:
             finally:
                 self._spawn_scene = None
 
+        self._rigid_trajectory_controls.clear()
         self.clean_materials()
 
         if self._env:
@@ -4924,6 +5111,7 @@ def _sever_wrapper_refs(obj_registry):
         _sever_wrapper_refs("_constraints")
         _sever_wrapper_refs("_rigid_object_groups")
         _sever_wrapper_refs("_deformable_objects")
+        _sever_wrapper_refs("_fluid_objects")
         _sever_wrapper_refs("_articulations")
         _sever_wrapper_refs("_robots")
         _sever_wrapper_refs("_sensors")
diff --git a/embodichain/lab/sim/spawn/descriptors.py b/embodichain/lab/sim/spawn/descriptors.py
index bbcbe2af0..c21c2e575 100644
--- a/embodichain/lab/sim/spawn/descriptors.py
+++ b/embodichain/lab/sim/spawn/descriptors.py
@@ -303,8 +303,14 @@ def rigid_desc_from_cfg(
         )
     )
     geometry, approximation, max_hulls, acd_method = _compile_geometry(cfg)
+    render_shape = cfg.visual_shape or cfg.shape
+    render_geometry = geometry
+    if cfg.visual_shape is not None:
+        render_cfg = cfg.copy()
+        render_cfg.shape = cfg.visual_shape
+        render_geometry = _compile_geometry(render_cfg)[0]
     material_ref, material_entry = _compile_visual_material(
-        uid, cfg.shape.visual_material
+        uid, render_shape.visual_material
     )
     collision = CollisionDesc.from_geometry(
         geometry,
@@ -325,18 +331,24 @@ def rigid_desc_from_cfg(
             cfg.shape.collision if isinstance(cfg.shape, MeshCfg) else None
         ),
     )
-    collision.render_source_index = 0
+    collision.render_source_index = (
+        0 if cfg.visual_shape is None and cfg.render_enabled else None
+    )
 
     descriptor = ObjectDesc(
         name=uid,
         pose=_pose_from_cfg(cfg),
-        renders=[
-            RenderDesc.from_geometry(
-                geometry,
-                load_option=_compile_load_option(cfg.shape),
-                material_ref=material_ref,
-            )
-        ],
+        renders=(
+            [
+                RenderDesc.from_geometry(
+                    render_geometry,
+                    load_option=_compile_load_option(render_shape),
+                    material_ref=material_ref,
+                )
+            ]
+            if cfg.render_enabled
+            else []
+        ),
         collisions=[collision],
         physics=_compile_rigid_physics(physics, cfg.body_type),
         per_env=per_env,
@@ -1549,7 +1561,9 @@ def _compile_newton_collision(
             values["gap"] = gap
     values.update(physics.newton_collision_props)
     if mesh_collision is not None and mesh_collision.approximation == "sdf":
-        values["force_sdf"] = True
+        values["force_sdf"] = (
+            True if mesh_collision.force_sdf is None else mesh_collision.force_sdf
+        )
         for field_name in (
             "is_hydroelastic",
             "sdf_narrow_band_range",
@@ -1690,6 +1704,7 @@ def _compile_visual_material(
         metallic_map=cfg.metallic_texture,
         ao_map=cfg.ao_texture,
         ior=float(cfg.ior),
+        transmission=cfg.transmission,
     )
     return key, (key, desc)
 
diff --git a/embodichain/lab/sim/spawn/scene.py b/embodichain/lab/sim/spawn/scene.py
index 73df30664..640574b7b 100644
--- a/embodichain/lab/sim/spawn/scene.py
+++ b/embodichain/lab/sim/spawn/scene.py
@@ -29,6 +29,7 @@
     "articulation",
     "soft_object",
     "cloth_object",
+    "fluid_object",
 ]
 
 
@@ -95,7 +96,21 @@ def declare(
             source_configurator=configure_source,
         )
 
-        if kind == "rigid_object_group":
+        if kind == "fluid_object":
+            declaration.descriptor = self.builder.add_fluid(
+                descriptor.name,
+                descriptor.positions,
+                pose=descriptor.pose,
+                density=descriptor.density,
+                particle_spacing=descriptor.particle_spacing,
+                particle_radius=descriptor.particle_radius,
+                render_radius=descriptor.render_radius,
+                render_mode=descriptor.render_mode,
+                mesh_surface=descriptor.mesh_surface,
+                material=descriptor.render_material,
+                per_env=descriptor.per_env,
+            )
+        elif kind == "rigid_object_group":
             declaration.descriptor = tuple(
                 self.builder.add_object(member) for member in descriptor
             )
@@ -175,7 +190,7 @@ def track(
     def remove(self, uid: str) -> None:
         """Remove a declared asset from its DexSim owner."""
         declaration = self._assets[uid]
-        if declaration.kind in {"soft_object", "cloth_object"}:
+        if declaration.kind in {"soft_object", "cloth_object", "fluid_object"}:
             raise NotImplementedError(
                 "DexSim Spawn does not yet expose pending removal for "
                 f"{declaration.kind.replace('_', ' ')}."
diff --git a/embodichain/lab/visualization/scene_exporter.py b/embodichain/lab/visualization/scene_exporter.py
index b7d6061a8..8b76fc4b0 100644
--- a/embodichain/lab/visualization/scene_exporter.py
+++ b/embodichain/lab/visualization/scene_exporter.py
@@ -757,10 +757,29 @@ def _prepare_overlays(self, overlays: SceneOverlays | None) -> SceneOverlays:
             else None
         )
         marker_frames = self._capture_axis_marker_overlays(reserved_frame_ids)
-        if overlays is None:
-            return SceneOverlays(frames=marker_frames)
+        overlays = overlays or SceneOverlays()
+        fluid_points = []
+        for uid, fluid in getattr(self._sim, "_fluid_objects", {}).items():
+            if fluid.is_declared or fluid.cfg.render.mode == "none":
+                continue
+            state = fluid.data.particle_pos_w
+            for env_id in self._env_ids:
+                points = state[env_id].detach().cpu().numpy().copy()
+                if fluid.cfg.render.clip_below_z is not None:
+                    points = points[points[:, 2] >= fluid.cfg.render.clip_below_z]
+                fluid_points.append(
+                    PointCloudOverlay(
+                        overlay_id=f"fluid/{uid}/{env_id}",
+                        points=points,
+                        point_size=2
+                        * (
+                            fluid.cfg.render.render_radius
+                            or fluid.cfg.particle_spacing / 2
+                        ),
+                    )
+                )
         point_clouds: list[PointCloudOverlay] = []
-        for point_cloud in overlays.point_clouds:
+        for point_cloud in overlays.point_clouds + tuple(fluid_points):
             point_count = point_cloud.points.shape[0]
             if point_count <= self.cfg.point_cloud_max_points:
                 point_clouds.append(point_cloud)
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
index 71c0a9ed4..10e5cc3d7 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
@@ -1,5 +1,18 @@
 # Franka 单臂马克杯倒水 demo
 
+## Sim 公共接口接入
+
+当前实现用 `SimulationManager.add_fluid_object(FluidObjectCfg(...))` 声明水体；
+求解器仍直接使用 DexSim 的 `SPHSolverCfg`。杯壁、台面和碗通过刚体配置声明，
+碰撞网格与显示网格可分离。原生 SDF 和液面重建由底层适配负责。
+离屏相机采集前使用 `sim.sync_render_state()` 发布状态，裁剪仅影响显示副本。
+多子步时杯壁轨迹通过 `register_kinematic_rigid_trajectory()` 在 solver 子步采样。
+
+当前支持单个 Newton CUDA 环境、固定粒子数、单向耦合。场景准备后物理拓扑冻结。
+Gym 批量环境及部分 reset 尚未接入；需先完成当前 demo 的完整效果验收，
+再补齐底层 SPH world 隔离和按环境复位。`IMPLEMENTATION_PLAN.md` 记录进度。
+
+
 使用真实 Franka Panda 机型的仿真模型，从侧面夹住正常大小的马克杯杯把，
 抬杯、向碗里倒水、回正并放回。这里包含两个入口：
 
@@ -183,7 +196,7 @@ python .agents/skills/add-task-program/scripts/inspect_deployment.py \
   embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml
 ```
 
-本分支新增任务配置、教程、资产和演示证据，EC 生产代码与公共 API 未变。
+原始 PR #771 新增任务配置、教程、资产和演示证据；当前接入分支另增加了 EC 流体公共 API。
 没有新建教程单元测试；使用部署检查、现有配置/资源测试、语法检查和 GPU
 短测验证。完整演示最初在提交 `3bb2a03c5` 上录制，分支基于 `453675d8a` 整理；
 当前分支的检查结果单独记录在 PR，不能把原录像当作新基线的完整复跑。
diff --git a/scripts/tutorials/sim/franka_dfsph_pour_demo.py b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
index 7df0adacd..14894661b 100644
--- a/scripts/tutorials/sim/franka_dfsph_pour_demo.py
+++ b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
@@ -13,13 +13,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ----------------------------------------------------------------------------
-"""Temporary EmbodiChain pouring demo using DexSim's Newton DFSPH solver.
+"""EmbodiChain pouring demo using the public fluid and rigid-boundary APIs.
 
 The robot and mug replay the recorded MuJoCo grasp trajectory. Water is simulated
 by DFSPH with volume-map boundaries, without reaction forces on the robot.
 Volume maps use baked mesh SDFs; the additional SDF penalty force is disabled.
-The temporary adapter uses the manager's existing Spawn builder and Newton
-render-sync object; it does not add a production fluid facade.
+The manager owns fluid declaration, state binding and render publication.
 """
 
 from __future__ import annotations
@@ -34,16 +33,23 @@
 import numpy as np
 import torch
 import trimesh
-import warp as wp
 from scipy.interpolate import PchipInterpolator
 from scipy.spatial.transform import Rotation, Slerp
 
-from dexsim import spawn
 from dexsim.engine.newton_physics import SPHSolverCfg
-from dexsim.engine.newton_physics.backend_registry import get_newton_backend
 
 from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
-from embodichain.lab.sim.cfg import LightCfg, NewtonPhysicsCfg, RenderCfg
+from embodichain.lab.sim.cfg import (
+    LightCfg,
+    NewtonPhysicsCfg,
+    RenderCfg,
+    RigidObjectCfg,
+    FluidObjectCfg,
+    FluidSpawnCfg,
+    FluidRenderCfg,
+)
+from embodichain.lab.sim.material import VisualMaterialCfg
+from embodichain.lab.visualization import VisualizationCfg
 from embodichain.lab.sim.robots.franka_panda import FrankaPandaCfg
 from embodichain.lab.sim.sensors import CameraCfg
 
@@ -70,29 +76,56 @@ def _pose(position: np.ndarray, rotation: np.ndarray | None = None) -> np.ndarra
     return pose
 
 
-def _mesh_geometry(mesh: trimesh.Trimesh, name: str) -> spawn.GeometryDesc:
-    return spawn.GeometryDesc.mesh(
-        vertices=np.asarray(mesh.vertices, dtype=np.float32),
-        triangles=np.asarray(mesh.faces, dtype=np.int32),
-        segment_name=name,
-    )
-
-
-def _sdf(geometry: spawn.GeometryDesc, resolution: int = 192) -> spawn.CollisionDesc:
-    collision = spawn.CollisionDesc.from_geometry(
-        geometry, approximation=spawn.CollisionApproximation.SDF
-    )
-    collision.newton = spawn.NewtonCollisionDesc(
-        has_particle_collision=True,
-        # Bake the mesh SDF eagerly: Newton 1.6 rejects mesh sdf_* shape fields.
-        force_sdf=False,
-        sdf_max_resolution=resolution,
-        sdf_narrow_band_range=(-0.025, 0.025),
-        sdf_padding=0.012,
-        sdf_texture_format="uint16",
-        mu=0.2,
+def _add_boundary(
+    sim: SimulationManager,
+    name: str,
+    mesh: trimesh.Trimesh,
+    pose: np.ndarray,
+    *,
+    resolution: int = 192,
+    material: VisualMaterialCfg | None = None,
+    visual_asset: Path | None = None,
+    render_enabled: bool = True,
+):
+    if material is not None:
+        material = material.copy()
+        material.uid = f"{name}_material"
+    return sim.add_rigid_object(
+        RigidObjectCfg.from_dict(
+            {
+                "uid": name,
+                "body_type": "kinematic",
+                "init_local_pose": pose,
+                "render_enabled": render_enabled,
+                "shape": {
+                    "shape_type": "Mesh",
+                    "vertices": np.asarray(mesh.vertices, dtype=np.float32),
+                    "triangles": np.asarray(mesh.faces, dtype=np.int32),
+                    "visual_material": None if material is None else material.to_dict(),
+                    "collision": {
+                        "approximation": "sdf",
+                        "force_sdf": False,
+                        "sdf_resolution": resolution,
+                        "sdf_narrow_band_range": [-0.025, 0.025],
+                        "sdf_padding": 0.012,
+                        "sdf_texture_format": "uint16",
+                    },
+                },
+                "visual_shape": (
+                    None
+                    if visual_asset is None
+                    else {"shape_type": "Mesh", "fpath": str(visual_asset)}
+                ),
+                "attrs": {
+                    "collision_props": {
+                        "backend": "newton",
+                        "has_particle_collision": True,
+                    },
+                    "material_props": {"backend": "newton", "dynamic_friction": 0.2},
+                },
+            }
+        )
     )
-    return collision
 
 
 def _particle_summary(
@@ -195,6 +228,7 @@ def main() -> None:
     sim = SimulationManager(
         SimulationManagerCfg(
             headless=True,
+            visualization=VisualizationCfg(),
             width=1280,
             height=960,
             num_envs=1,
@@ -244,51 +278,35 @@ def main() -> None:
             }
         )
         robot = sim.add_robot(robot_cfg)
-        # Temporary adapter: add fluid to the manager's one existing scene.
-        builder = sim._spawn_scene.builder
-        # FluidDesc currently accepts a native material, unlike RenderDesc.
-        water_material = sim.get_env().create_pbr_material("dfsph_water")
-        water_inst = water_material.get_inst()
-        water_inst.set_pbr_param("baseColor", 0.88, 0.96, 1.0)
-        water_inst.set_pbr_param("roughness", 0.025)
-        water_inst.set_pbr_param("metallic", 0.0)
-        water_inst.set_pbr_param("ior", 1.333)
-        water_inst.set_pbr_param("transmission", 0.96)
-        water_inst.set_pbr_param("volume_thickness", 0.04)
-        water_inst.set_pbr_param("volume_attenuation_distance", 0.6)
-        water_inst.set_pbr_param("volume_attenuation_color", 0.80, 0.92, 1.0)
-        builder.materials["ceramic"] = spawn.MaterialDesc(
-            name="ceramic", base_color=(0.82, 0.85, 0.88, 1.0), roughness=0.22
-        )
-        builder.materials["table"] = spawn.MaterialDesc(
-            name="table", base_color=(0.23, 0.29, 0.34, 1.0), roughness=0.6
+        # VisualMaterialCfg colors use sRGB; preserve the native demo
+        # linear baseColor [0.88, 0.96, 1.0] after conversion.
+        water_material = VisualMaterialCfg(
+            base_color=[0.9452768, 0.982207, 1.0, 1.0],
+            roughness=0.025,
+            metallic=0.0,
+            ior=1.333,
+            transmission=0.96,
+            volume_thickness=0.04,
+            volume_attenuation_distance=0.6,
+            volume_attenuation_color=(0.80, 0.92, 1.0),
         )
-        table_geometry = _mesh_geometry(
-            trimesh.creation.box([0.42, 0.70, 0.50]), "table"
-        )
-        builder.add_object(
-            spawn.ObjectDesc(
-                name="pour_table",
-                pose=_pose(np.array([-0.91, -0.08, 0.25])),
-                renders=[
-                    spawn.RenderDesc.from_geometry(table_geometry, material_ref="table")
-                ],
-                collisions=[_sdf(table_geometry, 96)],
-                physics=spawn.RigidBodyPhysicsDesc.kinematic(),
-                per_env=False,
-            )
+        _add_boundary(
+            sim,
+            "pour_table",
+            trimesh.creation.box([0.42, 0.70, 0.50]),
+            _pose(np.array([-0.91, -0.08, 0.25])),
+            resolution=96,
+            material=VisualMaterialCfg(
+                base_color=[0.23, 0.29, 0.34, 1.0], roughness=0.6, metallic=0.5, ior=1.0
+            ),
         )
-        floor_geometry = _mesh_geometry(
-            trimesh.creation.box([2.5, 2.5, 0.10]), "fluid_floor"
-        )
-        builder.add_object(
-            spawn.ObjectDesc(
-                name="fluid_floor",
-                pose=_pose(np.array([-0.5, 0.0, -0.055])),
-                collisions=[_sdf(floor_geometry, 128)],
-                physics=spawn.RigidBodyPhysicsDesc.kinematic(),
-                per_env=False,
-            )
+        _add_boundary(
+            sim,
+            "fluid_floor",
+            trimesh.creation.box([2.5, 2.5, 0.10]),
+            _pose(np.array([-0.5, 0.0, -0.055])),
+            resolution=128,
+            render_enabled=False,
         )
         mug_mesh = trimesh.creation.revolve(
             [
@@ -301,20 +319,28 @@ def main() -> None:
             ],
             sections=128,
         )
-        builder.add_object(
-            spawn.ObjectDesc(
-                name="liquid_mug",
-                pose=initial_pose,
-                renders=[
-                    spawn.RenderDesc.from_geometry(
-                        spawn.GeometryDesc.mesh(str(CUP_ASSET))
-                    )
-                ],
-                collisions=[_sdf(_mesh_geometry(mug_mesh, "mug_wall"))],
-                physics=spawn.RigidBodyPhysicsDesc.kinematic(),
-                per_env=False,
-            )
+        mug = _add_boundary(
+            sim, "liquid_mug", mug_mesh, initial_pose, visual_asset=CUP_ASSET
         )
+        if args.substeps > 1:
+            boundary_times = (
+                np.arange(round(duration / dt) * args.substeps) * dt / args.substeps
+            )
+            source_times = np.clip(
+                args.start_time + (boundary_times - 1.0) * args.speed,
+                args.start_time,
+                recorded_times[-1],
+            )
+            boundary_poses = np.concatenate(
+                (
+                    position_curve(source_times),
+                    orientation_curve(source_times).as_quat(),
+                ),
+                axis=1,
+            )
+            sim.register_kinematic_rigid_trajectory(
+                "liquid_mug", boundary_poses[None], fps=args.physics_hz * args.substeps
+            )
         outer_z = np.linspace(0.0, BOWL_HEIGHT, 64)
         inner_z = np.linspace(BOWL_HEIGHT, BOWL_BOTTOM_Z, 64)
         outer_r = np.sqrt(BOWL_OUTER_RADIUS**2 - (outer_z - BOWL_SPHERE_Z) ** 2)
@@ -328,44 +354,45 @@ def main() -> None:
             ]
         )
         bowl_mesh = trimesh.creation.revolve(bowl_profile, sections=192)
-        bowl_geometry = _mesh_geometry(bowl_mesh, "receiver_wall")
-        builder.add_object(
-            spawn.ObjectDesc(
-                name="receiver",
-                pose=_pose(RECEIVER_CENTER),
-                renders=[
-                    spawn.RenderDesc.from_geometry(
-                        bowl_geometry, material_ref="ceramic"
-                    )
-                ],
-                collisions=[_sdf(bowl_geometry)],
-                physics=spawn.RigidBodyPhysicsDesc.kinematic(),
-                per_env=False,
-            )
-        )
-        builder.add_fluid(
-            "water",
-            points,
-            density=1000.0,
-            particle_spacing=spacing,
-            particle_radius=0.30 * spacing,
-            render_radius=0.30 * spacing,
-            render_mode=spawn.FluidRenderMode.MESH_SURFACE,
-            mesh_surface=spawn.FluidMeshSurfaceRenderDesc(
-                gpu_enabled=True,
-                cell_size=0.54 * spacing,
-                max_res=512,
-                kernel_radius=1.01 * spacing,
-                iso_density=0.57,
-                density_smooth_iters=2,
-                aabb_trim_fraction=0.0,
-                min_component_tris=32,
-                laplacian_iters=4,
-                laplacian_alpha=0.11,
-                update_period=1,
+        _add_boundary(
+            sim,
+            "receiver",
+            bowl_mesh,
+            _pose(RECEIVER_CENTER),
+            material=VisualMaterialCfg(
+                base_color=[0.82, 0.85, 0.88, 1.0],
+                roughness=0.22,
+                metallic=0.5,
+                ior=1.0,
             ),
-            material=water_material,
-            per_env=False,
+        )
+        fluid = sim.add_fluid_object(
+            FluidObjectCfg(
+                uid="water",
+                density=1000.0,
+                particle_spacing=spacing,
+                particle_radius=0.30 * spacing,
+                spawn=FluidSpawnCfg(kind="points", positions=points),
+                render=FluidRenderCfg(
+                    mode="mesh_surface",
+                    render_radius=0.30 * spacing,
+                    material=water_material,
+                    clip_below_z=0.49,
+                    mesh_surface={
+                        "gpu_enabled": True,
+                        "cell_size": 0.54 * spacing,
+                        "max_res": 512,
+                        "kernel_radius": 1.01 * spacing,
+                        "iso_density": 0.57,
+                        "density_smooth_iters": 2,
+                        "aabb_trim_fraction": 0.0,
+                        "min_component_tris": 32,
+                        "laplacian_iters": 4,
+                        "laplacian_alpha": 0.11,
+                        "update_period": 1,
+                    },
+                ),
+            )
         )
         sim.add_light(
             LightCfg(
@@ -384,19 +411,17 @@ def main() -> None:
             )
         )
         sim.prepare()
-        backend = get_newton_backend(sim.get_world())
+        diagnostics = sim.get_fluid_diagnostics("water")
         boundary_info = {
             "method": "volume_map",
-            "volume_map_enabled": backend.solver.volume_map_boundary is not None,
-            "sdf_penalty_enabled": backend.solver.sdf_boundary is not None,
+            "volume_map_enabled": diagnostics["volume_map_enabled"],
+            "sdf_penalty_enabled": diagnostics["sdf_penalty_enabled"],
             "volume_map_resolution": args.volume_map_resolution,
-            "boundary_shape_count": backend.solver.volume_map_boundary.shape_count,
-            "rest_density_kg_m3": backend.solver.rest_density,
-            "viscosity": backend.solver.viscosity,
+            "boundary_shape_count": diagnostics["boundary_shape_count"],
+            "rest_density_kg_m3": diagnostics["rest_density_kg_m3"],
+            "viscosity": diagnostics["viscosity"],
         }
         print(json.dumps(boundary_info), flush=True)
-        mug = sim.spawn_result.get_actor("liquid_mug")
-        fluid = sim.spawn_result.get_fluid_particle_set("water")
         camera = sim.add_sensor(
             CameraCfg(
                 uid="demo_camera",
@@ -465,32 +490,24 @@ def main() -> None:
             cup_pose = _pose(
                 position_curve(source_time), orientation_curve(source_time).as_matrix()
             )
-            mug.set_world_pose(cup_pose)
-            mug.set_linear_velocity((cup_pose[:3, 3] - previous_cup_pose[:3, 3]) / dt)
-            mug.set_angular_velocity(
-                Rotation.from_matrix(
-                    cup_pose[:3, :3] @ previous_cup_pose[:3, :3].T
-                ).as_rotvec()
-                / dt
-            )
+            if args.substeps == 1:
+                mug.set_kinematic_state(
+                    torch.from_numpy(cup_pose).unsqueeze(0),
+                    torch.from_numpy(
+                        (cup_pose[:3, 3] - previous_cup_pose[:3, 3]) / dt
+                    ).unsqueeze(0),
+                    torch.from_numpy(
+                        Rotation.from_matrix(
+                            cup_pose[:3, :3] @ previous_cup_pose[:3, :3].T
+                        ).as_rotvec()
+                        / dt
+                    ).unsqueeze(0),
+                )
             previous_cup_pose = cup_pose
             sim.update(step=1, render_final_step=False)
             if step % frame_stride == 0:
                 with sim.render_frame():
-                    # Cull below-table droplets only in a separate render copy.
-                    # The mesh builder explicitly skips non-finite render points.
-                    # Physics positions, velocities and counts remain untouched.
-                    render_positions = wp.to_torch(
-                        fluid.get_particle_positions()
-                    ).clone()
-                    render_positions[render_positions[:, 2] < 0.49] = float("nan")
-                    render_positions_wp = wp.from_torch(render_positions, dtype=wp.vec3)
-                    backend.render_sync.sync(
-                        sim.get_world(),
-                        backend.state_0.body_q,
-                        render_positions_wp,
-                        simulation_time=backend.sim_time,
-                    )
+                    sim.sync_render_state()
                     camera.update()
                     close_camera.update()
                     rgb = camera.get_data()["color"][0, :, :, :3].cpu().numpy()
@@ -509,14 +526,20 @@ def main() -> None:
                         cv2.cvtColor(close_rgb, cv2.COLOR_RGB2BGR),
                     )
             if step % args.physics_hz == 0 or step == round(duration / dt) - 1:
-                positions = fluid.get_particle_positions().numpy()
+                if args.substeps > 1:
+                    cup_pose = mug.get_local_pose(to_matrix=True)[0].cpu().numpy()
+                positions = fluid.data.particle_pos_w[0].cpu().numpy()
                 if not np.isfinite(positions).all():
                     raise RuntimeError("Non-finite DFSPH particle state")
-                velocities = fluid.get_particle_velocities().numpy()
+                velocities = fluid.data.particle_vel_w[0].cpu().numpy()
                 summary = _particle_summary(positions, cup_pose, spacing, velocities)
                 summary["time_s"] = elapsed + dt
                 speed = np.linalg.norm(velocities, axis=1)
-                density = backend.solver.particle_density.numpy()
+                density = (
+                    sim.get_fluid_diagnostics("water")["particle_density"][0]
+                    .cpu()
+                    .numpy()
+                )
                 summary["speed_p99_m_s"] = float(np.quantile(speed, 0.99))
                 summary["density_p99_kg_m3"] = float(np.quantile(density, 0.99))
                 metrics.append(summary)
@@ -543,8 +566,8 @@ def main() -> None:
             "particle_count": len(points),
             "spacing_m": spacing,
             "source_start_time_s": args.start_time,
-            "rest_density_kg_m3": backend.solver.rest_density,
-            "viscosity": backend.solver.viscosity,
+            "rest_density_kg_m3": diagnostics["rest_density_kg_m3"],
+            "viscosity": diagnostics["viscosity"],
             "surface_tension_coefficient": args.surface_tension,
             "render_aabb_trim_fraction": 0.0,
             "render_only_cull_below_z_m": 0.49,
diff --git a/tests/sim/objects/test_fluid_object.py b/tests/sim/objects/test_fluid_object.py
new file mode 100644
index 000000000..317f3c808
--- /dev/null
+++ b/tests/sim/objects/test_fluid_object.py
@@ -0,0 +1,204 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Fluid state contracts exercised without native simulation."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+import numpy as np
+import pytest
+import torch
+from embodichain.lab.sim.cfg import FluidObjectCfg, FluidSpawnCfg
+from embodichain.lab.sim.objects.fluid import FluidObject
+
+pytestmark = pytest.mark.no_sim
+
+
+class ParticleBatch:
+    def __init__(self, scene, entities):
+        self.scene = scene
+        self.ids = [e.index for e in entities]
+
+    def fetch_particle_positions(self, out):
+        out.copy_(self.scene.state[self.ids, :, :3].reshape(-1, 3))
+
+    def fetch_particle_velocities(self, out):
+        out.copy_(self.scene.state[self.ids, :, 3:].reshape(-1, 3))
+
+    def apply_particle_positions(self, value):
+        self.scene.state[self.ids, :, :3] = value.reshape(len(self.ids), -1, 3)
+
+    def apply_particle_velocities(self, value):
+        self.scene.state[self.ids, :, 3:] = value.reshape(len(self.ids), -1, 3)
+
+
+@pytest.fixture
+def fluid():
+    points = np.array([[0.01, 0, 0], [0, 0.01, 0]], dtype=np.float32)
+    scene = SimpleNamespace(state=torch.zeros(2, 2, 6))
+    scene.state[:, :, :3] = torch.from_numpy(points)
+    scene.create_particle_set_batch = lambda entities: ParticleBatch(scene, entities)
+    result = FluidObject(
+        FluidObjectCfg(uid="water", spawn=FluidSpawnCfg(positions=points)),
+        points,
+        np.eye(4, dtype=np.float32),
+        torch.device("cpu"),
+        on_write=lambda: None,
+    )
+    result._initialize_spawn_declaration(2)
+    result.attach_spawn_handles(
+        [SimpleNamespace(index=i, particle_count=2) for i in range(2)]
+    )
+    result.bind_spawn(scene)
+    return result
+
+
+def test_reads_do_not_alias_live_or_default_state(fluid):
+    before = fluid.data.particle_state_w
+    detached = fluid.data.particle_pos_w
+    detached.fill_(12)
+    torch.testing.assert_close(fluid.data.particle_state_w, before)
+    default = fluid.data.default_particle_state_w
+    default.fill_(13)
+    torch.testing.assert_close(fluid.data.default_particle_state_w, before)
+
+
+def test_selected_write_and_reset_preserve_other_instance(fluid):
+    original = fluid.data.particle_state_w
+    changed = original[[1]].clone() + 4
+    fluid.write_particle_state_to_sim(changed, [1])
+    torch.testing.assert_close(fluid.data.particle_state_w[0], original[0])
+    torch.testing.assert_close(fluid.data.particle_state_w[1], changed[0])
+    fluid.reset([1])
+    torch.testing.assert_close(fluid.data.particle_state_w, original)
+
+
+def test_reset_to_pose_rotates_template_without_changing_default(fluid):
+    original = fluid.data.default_particle_state_w
+    q = 2**-0.5
+    fluid.reset_to_pose(torch.tensor([[1, 2, 3, 0, 0, q, q]]), [1])
+    expected = torch.tensor([[1, 2.01, 3], [0.99, 2, 3]])
+    torch.testing.assert_close(fluid.data.particle_pos_w[1], expected)
+    torch.testing.assert_close(fluid.data.default_particle_state_w, original)
+
+
+@pytest.mark.parametrize("ids", [[0, 0], [-1], [2], [0.5]])
+def test_invalid_selection_does_not_mutate(fluid, ids):
+    before = fluid.data.particle_state_w
+    with pytest.raises(ValueError):
+        fluid.write_particle_state_to_sim(torch.zeros(len(ids), 2, 6), ids)
+    torch.testing.assert_close(fluid.data.particle_state_w, before)
+
+
+def test_empty_selection_and_invalid_state(fluid):
+    fluid.write_particle_state_to_sim(torch.empty(0, 2, 6), [])
+    with pytest.raises(ValueError, match="finite"):
+        fluid.write_particle_state_to_sim(torch.full((1, 2, 6), float("nan")), [0])
+    with pytest.raises(ValueError, match="shape"):
+        fluid.write_particle_state_to_sim(torch.zeros(1, 3, 6), [0])
+
+
+def test_integer_initial_velocity_is_normalized():
+    points = np.array([[0, 0, 0]], dtype=np.float32)
+    scene = SimpleNamespace(state=torch.zeros(1, 1, 6))
+    scene.create_particle_set_batch = lambda entities: ParticleBatch(scene, entities)
+    fluid = FluidObject(
+        FluidObjectCfg(
+            uid="water",
+            spawn=FluidSpawnCfg(positions=points, initial_velocity=(1, 0, 0)),
+        ),
+        points,
+        np.eye(4, dtype=np.float32),
+        torch.device("cpu"),
+        on_write=lambda: None,
+    )
+    fluid._initialize_spawn_declaration(1)
+    fluid.attach_spawn_handles([SimpleNamespace(index=0, particle_count=1)])
+    fluid.bind_spawn(scene)
+    torch.testing.assert_close(fluid.data.particle_vel_w, torch.tensor([[[1.0, 0, 0]]]))
+    fluid.reset_to_pose(torch.tensor([[0.0, 0, 0, 0, 0, 0, 1]]))
+    torch.testing.assert_close(fluid.data.particle_vel_w, torch.tensor([[[1.0, 0, 0]]]))
+
+
+@pytest.mark.parametrize("inside_frame", [False, True])
+def test_fluid_write_respects_render_frame_cache_scope(inside_frame):
+    from embodichain.lab.sim import SimulationManager
+
+    calls = []
+    sim = SimpleNamespace(
+        physics=SimpleNamespace(invalidate_fluid_state=lambda: None),
+        _render_state_published=True if inside_frame else None,
+        sync_render_state=lambda: calls.append("publish"),
+    )
+    SimulationManager._invalidate_fluid_state(sim)
+    SimulationManager._ensure_render_state(sim)
+    SimulationManager._ensure_render_state(sim)
+    assert len(calls) == (1 if inside_frame else 2)
+    assert sim._render_state_published is (True if inside_frame else None)
+
+
+def test_rigid_trajectory_samples_solver_substeps_and_rewinds():
+    from embodichain.lab.sim._runtime_controls import _KinematicRigidTrajectoryControl
+
+    poses = np.array([[0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 1]], dtype=np.float32)
+    samples, velocities = [], []
+    actor = SimpleNamespace(
+        set_world_pose=lambda value: samples.append(value.copy()),
+        set_linear_velocity=lambda value: velocities.append(value.copy()),
+        set_angular_velocity=lambda value: None,
+    )
+    control = _KinematicRigidTrajectoryControl("arena_0/cup", poses, fps=1.0)
+    control.initialize(
+        SimpleNamespace(result=SimpleNamespace(get_actor=lambda _: actor))
+    )
+    for index in range(3):
+        control(None, index, 3, 0.1)
+    np.testing.assert_allclose([p[0, 3] for p in samples], [0, 0.1, 0.2])
+    np.testing.assert_allclose(velocities[1], [1, 0, 0])
+    control.reset()
+    control(None, 0, 1, 0.1)
+    np.testing.assert_array_equal(samples[-1], samples[0])
+    control.close()
+    assert control._actor is None
+
+
+def test_rigid_trajectory_rotation_hold_across_outer_steps():
+    from scipy.spatial.transform import Rotation
+    from embodichain.lab.sim._runtime_controls import _KinematicRigidTrajectoryControl
+
+    axis = np.array([1.0, 2.0, 3.0])
+    axis /= np.linalg.norm(axis)
+    end = Rotation.from_rotvec(0.6 * axis).as_quat()
+    poses = np.array([[0, 0, 0, 0, 0, 0, 1], [1, 2, 3, *end]])
+    captured = []
+    actor = SimpleNamespace(
+        set_world_pose=lambda value: captured.append({"pose": value.copy()}),
+        set_linear_velocity=lambda value: captured[-1].update(linear=value.copy()),
+        set_angular_velocity=lambda value: captured[-1].update(angular=value.copy()),
+    )
+    control = _KinematicRigidTrajectoryControl("arena_0/cup", poses, fps=1)
+    control.initialize(
+        SimpleNamespace(result=SimpleNamespace(get_actor=lambda _: actor))
+    )
+    for outer in range(3):
+        for substep in range(2):
+            control(None, substep, 2, 0.25)
+    np.testing.assert_allclose(captured[3]["pose"][:3, 3], [0.75, 1.5, 2.25])
+    np.testing.assert_allclose(captured[3]["angular"], axis * 0.6, atol=1e-6)
+    np.testing.assert_allclose(captured[4]["pose"], captured[5]["pose"])
+    np.testing.assert_allclose(captured[5]["linear"], 0, atol=1e-6)
+    np.testing.assert_allclose(captured[5]["angular"], 0, atol=1e-6)
diff --git a/tests/sim/objects/test_fluid_runtime.py b/tests/sim/objects/test_fluid_runtime.py
new file mode 100644
index 000000000..ad60002b9
--- /dev/null
+++ b/tests/sim/objects/test_fluid_runtime.py
@@ -0,0 +1,106 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Small CUDA integration test of the public fluid lifecycle."""
+
+from __future__ import annotations
+
+import pytest
+import torch
+from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
+from embodichain.lab.sim.cfg import (
+    FluidObjectCfg,
+    FluidSpawnCfg,
+    FluidRenderCfg,
+    NewtonPhysicsCfg,
+    RenderCfg,
+)
+
+pytestmark = [pytest.mark.gpu, pytest.mark.requires_sim]
+
+
+class TestFluidRuntime:
+    def test_prepare_write_reset_and_repeat(self):
+        from embodichain.lab.sim.cfg import RigidObjectCfg
+        from embodichain.lab.sim.shapes import CubeCfg
+
+        sim = SimulationManager(
+            SimulationManagerCfg(
+                headless=True,
+                device="cuda",
+                num_envs=1,
+                physics_dt=0.001,
+                render_cfg=RenderCfg(renderer="no-render"),
+                physics_cfg=NewtonPhysicsCfg(
+                    num_substeps=2,
+                    collision_cfg=None,
+                    solver_cfg={
+                        "solver_type": "sph",
+                        "sph_method": "dfsph",
+                        "particle_support_radius": 0.0225,
+                        "volume_map_boundary_enabled": False,
+                    },
+                ),
+            )
+        )
+        fluid = rigid = None
+        try:
+            rigid = sim.add_rigid_object(
+                RigidObjectCfg(
+                    uid="boundary", body_type="kinematic", shape=CubeCfg(size=[0.1] * 3)
+                )
+            )
+            sim.register_kinematic_rigid_trajectory(
+                "boundary",
+                torch.tensor([[[0.0, 0, 0, 0, 0, 0, 1], [1.0, 0, 0, 0, 0, 0, 1]]]),
+                fps=10,
+            )
+            fluid = sim.add_fluid_object(
+                FluidObjectCfg(
+                    uid="water",
+                    particle_spacing=0.01,
+                    init_pos=(0.0, 0.0, 1.0),
+                    spawn=FluidSpawnCfg(kind="box", size=(0.04, 0.04, 0.04)),
+                    render=FluidRenderCfg(mode="none"),
+                )
+            )
+            with pytest.raises(RuntimeError, match="prepare"):
+                _ = fluid.data
+            sim.prepare()
+            assert sim.get_fluid_object("water") is fluid
+            assert sim.get_asset("water") is fluid
+            assert fluid.num_particles == 64
+            original = fluid.data.default_particle_state_w
+            sim.update(step=3, render_final_step=False)
+            assert rigid.get_local_pose()[0, 0].item() == pytest.approx(0.025, abs=1e-6)
+            first = fluid.data.particle_state_w
+            assert torch.isfinite(first).all()
+            assert first[..., 2].mean() < original[..., 2].mean()
+            sim.reset_objects_state()
+            torch.testing.assert_close(fluid.data.particle_state_w, original)
+            sim.update(step=3, render_final_step=False)
+            torch.testing.assert_close(
+                fluid.data.particle_state_w, first, rtol=1e-5, atol=1e-6
+            )
+            assert rigid.get_local_pose()[0, 0].item() == pytest.approx(0.025, abs=1e-6)
+            sim.update(step=105, render_final_step=False)
+            assert rigid.get_local_pose()[0, 0].item() == pytest.approx(1.0, abs=1e-6)
+            with pytest.raises(NotImplementedError, match="before prepare"):
+                sim.add_fluid_object(FluidObjectCfg(uid="late"))
+        finally:
+            fluid = rigid = None
+            sim.destroy(exit_process=False)
+            SimulationManager.flush_cleanup_queue()
diff --git a/tests/sim/test_fluid_cfg.py b/tests/sim/test_fluid_cfg.py
new file mode 100644
index 000000000..bc146470b
--- /dev/null
+++ b/tests/sim/test_fluid_cfg.py
@@ -0,0 +1,175 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Fluid declaration and solver configuration contracts."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from embodichain.lab.sim.cfg import (
+    FluidObjectCfg,
+    FluidSpawnCfg,
+    NewtonPhysicsCfg,
+    RigidObjectCfg,
+)
+from embodichain.lab.sim.spawn.descriptors import rigid_desc_from_cfg
+from embodichain.compute.geometry.particles import sample_volume_particles
+
+pytestmark = pytest.mark.no_sim
+
+
+def test_fluid_round_trip_keeps_native_render_options():
+    cfg = FluidObjectCfg.from_dict(
+        {
+            "uid": "water",
+            "spawn": {"kind": "box", "size": [0.02] * 3},
+            "render": {"mode": "mesh_surface", "mesh_surface": {"iso_density": 0.57}},
+        }
+    )
+    restored = FluidObjectCfg.from_dict(cfg.to_dict())
+    np.testing.assert_array_equal(cfg.spawn.sample(0.005), restored.spawn.sample(0.005))
+    assert restored.render.mesh_surface == {"iso_density": 0.57}
+    restored.render.mesh_surface["iso_density"] = 0.8
+    assert cfg.render.mesh_surface["iso_density"] == 0.57
+
+
+def test_native_sph_and_yaml_mapping_use_same_solver():
+    from dexsim.engine.newton_physics import SPHSolverCfg
+
+    source = SPHSolverCfg(sph_method="dfsph", viscosity=0.0006)
+    native = NewtonPhysicsCfg(solver_cfg=source).to_dexsim_cfg(0)
+    mapping = NewtonPhysicsCfg(
+        solver_cfg={"solver_type": "sph", "sph_method": "dfsph", "viscosity": 0.0006}
+    ).to_dexsim_cfg(0)
+    assert type(native.solver_cfg) is SPHSolverCfg
+    assert type(mapping.solver_cfg) is SPHSolverCfg
+    assert mapping.solver_cfg == source
+
+
+@pytest.mark.parametrize("kind", ["box", "sphere", "cylinder"])
+def test_lattice_is_deterministic_and_inside_volume(kind):
+    points = sample_volume_particles(
+        kind, 0.01, size=(0.04, 0.04, 0.04), radius=0.02, height=0.04
+    )
+    np.testing.assert_array_equal(
+        points,
+        sample_volume_particles(
+            kind, 0.01, size=(0.04, 0.04, 0.04), radius=0.02, height=0.04
+        ),
+    )
+    assert np.all(np.abs(points) < 0.02)
+    if kind == "sphere":
+        assert np.all(np.linalg.norm(points, axis=1) < 0.02)
+    if kind == "cylinder":
+        assert np.all(np.linalg.norm(points[:, :2], axis=1) < 0.02)
+
+
+def test_grid_limit_is_checked_before_allocation():
+    with pytest.raises(ValueError, match="max_particles"):
+        sample_volume_particles("box", 1e-9)
+
+
+@pytest.mark.parametrize(
+    "positions", [[], [[float("nan"), 0, 0]], [[0, 1]], [[[0, 0, 0]]]]
+)
+def test_invalid_particle_sources_rejected(positions):
+    with pytest.raises(ValueError):
+        FluidSpawnCfg(positions=positions).sample(0.001)
+
+
+def test_npz_points_are_loaded_without_pickle(tmp_path):
+    path = tmp_path / "fill.npz"
+    np.savez(path, positions=np.array([[1, 2, 3]], dtype=np.float32))
+    np.testing.assert_array_equal(
+        FluidSpawnCfg(file_path=str(path)).sample(0.01), [[1, 2, 3]]
+    )
+
+
+def test_fill_mesh_must_be_closed(tmp_path):
+    import trimesh
+
+    path = tmp_path / "open.obj"
+    trimesh.Trimesh(
+        vertices=[[0, 0, 0], [1, 0, 0], [0, 1, 0]], faces=[[0, 1, 2]]
+    ).export(path)
+    with pytest.raises(ValueError, match="closed"):
+        sample_volume_particles("mesh_volume", 0.01, mesh_path=str(path))
+
+
+def test_rigid_visual_mesh_does_not_replace_sdf_collision():
+    cfg = RigidObjectCfg.from_dict(
+        {
+            "uid": "cup",
+            "body_type": "kinematic",
+            "shape": {
+                "shape_type": "Mesh",
+                "vertices": [[0, 0, 0], [1, 0, 0], [0, 1, 0]],
+                "triangles": [[0, 1, 2]],
+                "collision": {
+                    "approximation": "sdf",
+                    "force_sdf": False,
+                    "sdf_resolution": 192,
+                },
+            },
+            "visual_shape": {"shape_type": "Mesh", "fpath": "cup.glb"},
+        }
+    )
+    desc, _ = rigid_desc_from_cfg(cfg, newton_solver_type="sph")
+    assert desc.renders[0].file_path == "cup.glb"
+    assert desc.collisions[0].newton.force_sdf is False
+    assert desc.collisions[0].render_source_index is None
+
+
+def test_rigid_material_preserves_transmission():
+    from embodichain.lab.sim.shapes import CubeCfg
+    from embodichain.lab.sim.material import VisualMaterialCfg
+
+    cfg = RigidObjectCfg(
+        uid="glass", shape=CubeCfg(visual_material=VisualMaterialCfg(transmission=0.8))
+    )
+    _, materials = rigid_desc_from_cfg(cfg, newton_solver_type="sph")
+    assert next(iter(materials.values())).transmission == 0.8
+
+
+@pytest.mark.parametrize("backend,num_envs", [("default", 1), ("newton", 2)])
+def test_manager_rejects_unsupported_fluid_worlds_before_declaration(backend, num_envs):
+    from types import SimpleNamespace
+    import torch
+    from embodichain.lab.sim import SimulationManager
+
+    manager = SimpleNamespace(
+        physics=SimpleNamespace(name=backend),
+        device=torch.device("cuda"),
+        num_envs=num_envs,
+    )
+    with pytest.raises(NotImplementedError):
+        SimulationManager.add_fluid_object(manager, FluidObjectCfg(uid="water"))
+
+
+def test_points_source_validates_spacing():
+    with pytest.raises(ValueError, match="spacing"):
+        FluidSpawnCfg(positions=[[0, 0, 0]]).sample(0)
+
+
+def test_closed_mesh_sampling_matches_equivalent_box(tmp_path):
+    import trimesh
+
+    mesh = tmp_path / "fill.obj"
+    trimesh.creation.box(extents=[0.04] * 3).export(mesh)
+    actual = sample_volume_particles("mesh_volume", 0.01, mesh_path=str(mesh))
+    expected = sample_volume_particles("box", 0.01, size=(0.04,) * 3)
+    np.testing.assert_allclose(actual, expected)
diff --git a/tests/sim/test_sim_manager.py b/tests/sim/test_sim_manager.py
index 16fe7292d..4bb45629c 100644
--- a/tests/sim/test_sim_manager.py
+++ b/tests/sim/test_sim_manager.py
@@ -463,6 +463,8 @@ def _make_runtime_control_sim_manager(
     sim._robots = {"robot": object()}
     sim._articulations = {}
     sim._rigid_objects = {"table": object()}
+    sim._fluid_objects = {}
+    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {
         "cloth": SimpleNamespace(
             cfg=SurfaceDeformableObjectCfg(uid="cloth", particle_flags=[0, 1, 0])
@@ -542,6 +544,8 @@ def test_deferred_destroy_prepares_backend_before_releasing_world(
     sim._rigid_objects = {}
     sim._constraints = {}
     sim._rigid_object_groups = {}
+    sim._fluid_objects = {}
+    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._articulations = {}
     sim._robots = {}
@@ -1277,6 +1281,8 @@ def capture(self, **_kwargs: object) -> None:
     )
     sim._rigid_objects = {}
     sim._rigid_object_groups = {}
+    sim._fluid_objects = {}
+    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._articulations = {}
     sim._robots = {}
@@ -2211,6 +2217,8 @@ def test_remove_asset_marks_visualization_topology_dirty() -> None:
     sim.prepare = MagicMock()
     sim._rigid_objects = {"cube": rigid_object}
     sim._rigid_object_groups = {}
+    sim._fluid_objects = {}
+    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._articulations = {}
     sim._robots = {}
@@ -2475,6 +2483,8 @@ def test_reset_objects_state_includes_deformable_assets() -> None:
     sim._rigid_object_groups = {}
     sim._lights = {}
     sim._sensors = {}
+    sim._fluid_objects = {}
+    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {
         "soft": MagicMock(),
         "cloth": MagicMock(),
@@ -2507,6 +2517,8 @@ def test_newton_reset_clears_all_articulations_in_selected_worlds_together() ->
     sim._articulations = {"arm": articulation}
     sim._rigid_objects = {}
     sim._rigid_object_groups = {}
+    sim._fluid_objects = {}
+    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._lights = {}
     sim._sensors = {}
@@ -2528,6 +2540,8 @@ def test_newton_reset_rejects_excluding_one_articulation_from_selected_worlds()
     sim._articulations = {"arm": articulation}
     sim._rigid_objects = {}
     sim._rigid_object_groups = {}
+    sim._fluid_objects = {}
+    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._lights = {}
     sim._sensors = {}
@@ -2556,6 +2570,8 @@ def test_newton_non_mujoco_reset_allows_excluding_an_articulation() -> None:
     sim._articulations = {"arm": articulation}
     sim._rigid_objects = {}
     sim._rigid_object_groups = {}
+    sim._fluid_objects = {}
+    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._lights = {}
     sim._sensors = {}
@@ -2783,3 +2799,31 @@ def test_process_no_render_is_detected_after_world_cleanup(monkeypatch) -> None:
         lambda: SimpleNamespace(renderer=dexsim.types.Renderer.NORENDER),
     )
     assert SimulationManager._get_process_renderer() == "no-render"
+
+
+def test_reset_fluid_and_rigid_controls_selects_rows_and_honors_exclusions():
+    sim = object.__new__(SimulationManager)
+    sim.physics = SimpleNamespace(name="default")
+    sim.num_envs = 2
+    for name in (
+        "_robots",
+        "_articulations",
+        "_rigid_objects",
+        "_rigid_object_groups",
+        "_lights",
+        "_sensors",
+        "_deformable_objects",
+    ):
+        setattr(sim, name, {})
+    first, second, excluded = MagicMock(), MagicMock(), MagicMock()
+    fluid = MagicMock()
+    sim._rigid_trajectory_controls = {
+        "cup": (first, second),
+        "other": (excluded, excluded),
+    }
+    sim._fluid_objects = {"water": fluid}
+    sim.reset_objects_state(env_ids=[1], excluded_uids=["other"])
+    first.reset.assert_not_called()
+    second.reset.assert_called_once_with()
+    excluded.reset.assert_not_called()
+    fluid.reset.assert_called_once_with([1])
diff --git a/tests/visualization/test_scene_exporter.py b/tests/visualization/test_scene_exporter.py
index dbf7945c5..d4f737c02 100644
--- a/tests/visualization/test_scene_exporter.py
+++ b/tests/visualization/test_scene_exporter.py
@@ -728,3 +728,24 @@ def test_rigid_groups_and_deformable_meshes_are_exported() -> None:
         ),
         atol=1.0e-6,
     )
+
+
+def test_fluid_point_overlay_is_detached_and_display_filtered():
+    import torch
+    from types import SimpleNamespace
+    from embodichain.lab.sim.cfg import FluidObjectCfg, FluidRenderCfg
+
+    sim = _EmptySimulation()
+    original = torch.tensor([[[0.0, 0, -1], [0.0, 0, 1]]])
+    sim._fluid_objects = {
+        "water": SimpleNamespace(
+            is_declared=False,
+            cfg=FluidObjectCfg(render=FluidRenderCfg(clip_below_z=0.0)),
+            data=SimpleNamespace(particle_pos_w=original),
+        )
+    }
+    exporter = SceneExporter(sim, VisualizationCfg(backend="viser", env_ids=[0]))
+    overlays = exporter._prepare_overlays(None)
+    assert len(overlays.point_clouds) == 1
+    np.testing.assert_array_equal(overlays.point_clouds[0].points, [[0, 0, 1]])
+    torch.testing.assert_close(original, torch.tensor([[[0.0, 0, -1], [0.0, 0, 1]]]))

From c0be5af3654cd7d0d89fa29474a18821d1fa61a8 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Wed, 14 Oct 2026 02:12:57 +0800
Subject: [PATCH 03/19] feat(gym): replay qualified Task Program trajectories
 with live fluids

Add typed fluid scene ownership, observations and row-local refill; gate batching on native SPH capabilities. Generate and qualify the pouring Task Program in MuJoCo before replaying measured states in an independent SPH Gym. Package physical assets and keep source acceptance separate from fluid metrics. Requires DexSim world-isolated SPH support (d2d36fbcb) for batched scenes.
---
 IMPLEMENTATION_PLAN.md                        |   104 +-
 agent_context/MAP.yaml                        |     8 +-
 .../topics/env-framework/env-framework.md     |     2 +
 .../topics/env-framework/fluid-replay.md      |    38 +
 .../topics/simulation-system/fluids.md        |    16 +-
 docs/source/api_reference/public_api.rst      |    45 +
 embodichain/lab/gym/envs/embodied_env.py      |     8 +
 embodichain/lab/gym/envs/fluid_replay.py      |   331 +
 embodichain/lab/gym/envs/managers/events.py   |    32 +
 .../lab/gym/envs/managers/observations.py     |   131 +
 .../lab/gym/utils/_component_composition.py   |    25 +-
 embodichain/lab/gym/utils/gym_utils.py        |    18 +
 .../lab/scripts/run_fluid_task_program.py     |   296 +
 embodichain/lab/sim/objects/fluid.py          |     4 +-
 embodichain/lab/sim/physics/newton.py         |    33 +-
 embodichain/lab/sim/sim_manager.py            |    10 +-
 .../franka_panda_fluid_replay.yaml            |    32 +
 .../manipulation/tableware/pour_cup/README.md |    32 +-
 .../tableware/pour_cup/assets/fluid_floor.obj |    22 +
 .../tableware/pour_cup/assets/fluid_table.obj |    22 +
 .../tableware/pour_cup/assets/mug_wall.obj    |  1540 +
 .../pour_cup/assets/receiver_wall.obj         | 73732 ++++++++++++++++
 .../tableware/pour_cup/assets/water_fill.npz  |   Bin 0 -> 180343 bytes
 .../tableware/pour_cup/env.fluid.yaml         |   265 +
 .../tableware/pour_cup/env.newton.yaml        |     2 +-
 .../manipulation/tableware/pour_cup/env.yaml  |     2 +-
 .../tableware/pour_cup/task.franka.fluid.yaml |    12 +
 .../manipulation/tableware/pour_cup.py        |    28 +
 setup.py                                      |     1 +
 tests/gym/envs/test_fluid_integration.py      |   236 +
 tests/gym/envs/test_fluid_replay_runtime.py   |   154 +
 tests/sim/objects/test_fluid_batch_runtime.py |   125 +
 tests/sim/objects/test_fluid_object.py        |     6 +-
 tests/test_task_program_package_data.py       |    14 +
 34 files changed, 77290 insertions(+), 36 deletions(-)
 create mode 100644 agent_context/topics/env-framework/fluid-replay.md
 create mode 100644 embodichain/lab/gym/envs/fluid_replay.py
 create mode 100644 embodichain/lab/scripts/run_fluid_task_program.py
 create mode 100644 embodichain_tasks/configs/components/embodiments/franka_panda_fluid_replay.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_floor.obj
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_table.obj
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/mug_wall.obj
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/receiver_wall.obj
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/water_fill.npz
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml
 create mode 100644 embodichain_tasks/embodichain_tasks/manipulation/tableware/pour_cup.py
 create mode 100644 tests/gym/envs/test_fluid_integration.py
 create mode 100644 tests/gym/envs/test_fluid_replay_runtime.py
 create mode 100644 tests/sim/objects/test_fluid_batch_runtime.py

diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
index f88cecf58..d83b5c4f4 100644
--- a/IMPLEMENTATION_PLAN.md
+++ b/IMPLEMENTATION_PLAN.md
@@ -16,7 +16,7 @@ Initial fill, fixed particle counts, Newton CUDA DFSPH, one-way coupling.
    same-environment baseline and migrated short test, then full pouring.
    Compare received/residual/spilled volume, settling, images, runtime and VRAM.
    A short test alone does not pass this gate. Record environment and evidence.
-3. **Gym integration (gated, do not start before gate 2 passes)**: YAML scene
+3. **Gym integration (complete after gate 2)**: YAML scene
    ownership, vectorized observations, partial reset, optional particle records.
    DexSim world-filtered SPH neighborhoods/boundaries and per-world solver reset
    are prerequisites; spacing worlds apart is not isolation.
@@ -47,7 +47,8 @@ will be represented as complete before full demo parity is established.
 - Rigid kinematic pose/twist writes and native PRE_SUBSTEP trajectory controls.
 - Independent rigid visual/collision geometry and pre-baked SDF policy.
 - Native render publication, render-only clipping, and detached Viser points.
-- No Gym modifications. Multi-environment fluid declarations fail explicitly.
+- This first Sim milestone initially rejected multiple environments; the
+  completed Gym phase below lifts that gate only for qualified native artifacts.
 
 ## Current qualification limits
 
@@ -90,12 +91,101 @@ correct particle snapshots but stale offscreen images, fixed by publishing via
 - GPU lifecycle test verifies declaration, state, native substep control,
   selected reset, repeated rollout, endpoint hold and cleanup.
 
-## Next milestone
+## Delivered milestones
 
-Sim and demo parity are the current delivered milestone. Gym remains unimplemented.
-Its next dependency is native world-filtered neighborhoods/boundaries, per-world
-DFSPH convergence and selected-world cache reset, followed by EC YAML/manager
-integration. Do not remove the single-environment gate before that qualification.
+Sim parity and two-stage Task Program -> fluid Gym integration are complete in
+isolated worktrees. Batch support remains gated by loaded native capabilities.
+Published native wheel/AOT qualification is a separate release task; no shared
+runtime or project dependency pin was changed.
 
 GPU memory evidence: `outputs/fluid-memory-comparison.json` (process samples
 at approximately 250 ms cadence; 4981 -> 4985 MiB).
+
+
+## Completed phase: batch SPH, then Task Program -> fluid Gym
+
+User confirmed the execution route: generate and validate a new trajectory with
+Task Program in a MuJoCo Gym environment; replay its recorded robot and cup
+states in a separate fluid Gym environment. This remains one-way coupling;
+there is no online rigid-fluid joint solver or synthetic grasp attachment.
+Generation acceptance and fluid physical metrics are recorded independently.
+
+Dependency worktree: `/home/dex/.codex/worktrees/fluid-sph-worlds/dexsim`,
+branch `codex/fluid-sph-worlds`, base `21bfad162518216fdd9cf313c70b15af94e6fa84`.
+Native implementation commit: `d2d36fbcb`.
+EC worktree remains this checkout, base Sim commit `bbd6928c1`.
+Native modifications belong to the existing SPH solver/kernels and backend reset
+interface. Compare: per-world solver instances (resource overhead), independent
+partition scheduling (larger redesign), or world-filtered batched kernels
+(selected, preserves ownership and batched stepping). No threading changes.
+
+First oracle: coincident replicated worlds must have independent particle
+neighbors, boundaries, gravity and convergence. Establish baseline failures
+before fixing kernels. Then selected-world reset must preserve all other rows.
+On green: enable capability-gated EC batch declaration and integrate Gym YAML,
+fluid observations/reset, and Task Program trajectory generation + fluid replay.
+On red: diagnose the failing kernel/contract in the dependency, without removing
+EC capability guards. Runtime tests must print/verify source package identity;
+the shared installed DexSim build is not modified.
+
+
+## Gym and native qualification results
+
+- Native source: world-filtered neighbors and SDF/volume-map boundaries,
+  per-world gravity and DFSPH convergence; canonical `E+1` selected reset.
+  Pure global legacy single-world particles remain supported. Mixed global/local
+  particles and global particles with multiple worlds fail explicitly.
+- Before fixes, overlapping worlds shared 16 instead of 8 neighbors and gravity;
+  convergence differed by 0.62 m/s and a foreign boundary displaced particles
+  by 0.08 m. New native regressions plus existing boundary checks: **14 passed**.
+- EC YAML declares fluids through physical environment ownership; packaged
+  relative source assets, fluid observations, container refill and capability
+  gated batching are implemented. `FluidReplayEnv` reuses normal Gym lifecycle,
+  with per-row clocks and partial reset. Robot roots/joint order are validated.
+  Action managers and policy dataset/trajectory recording are rejected.
+- Task Program source freshly generated in MuJoCo and accepted: lift
+  **0.1197915 m**, maximum tilt **66.4883 degrees**, return error **0.00223394 m**.
+  Source program hash:
+  `6cc3d7aa5a64c4cc86f8d4f60bf80dbad23fca368785e165164cd39f50b288cb`.
+- Full Gym replay: **2 x 76,538 particles**, **31 s**, **744 video frames** fully
+  decoded and inspected at lift/pour/return. Final received volume:
+  **141.98288 / 140.88261 mL**; all sampled particle states finite. Wall time
+  **905.33 s**. Source motions are measured states with one-way fluid coupling;
+  these results do not claim online robot-fluid force feedback.
+- Focused EC config, objects, packaging, context and docs checker tests:
+  **259 passed**. Later batch-count regression and refill reuse: **11 passed**.
+  Fresh-process live Gym independent completion/reset and Sim selected reset:
+  **2 passed**. Final coarse two-row CLI partial-reset smoke also passed.
+- Independent reviews corrected wheel asset omission, root pose adoption,
+  misleading action recording, dataset mapping compatibility and canonical
+  global reset masks. Real Gym tests additionally caught pre-resolution sim
+  versus env counts; both replay and CLI now use `EnvCfg.num_envs`.
+- API coverage **2503/2503**; source Task Program inspector passed all three
+  calls. A built EC wheel contains all six pour-cup assets. Native validation
+  uses staged JIT source, not a rebuilt release/AOT wheel.
+- Registered `sph_speed_dfsph_4096` benchmark, same RTX 5090 / Newton 1.6 /
+  Warp 1.17 configuration, 20 warmup + 100 measured steps: baseline mean
+  **3.05956 ms**, changed **3.17811 ms** (+3.87%); p95 **3.24373 / 3.38617 ms**.
+  Both runs finite with matching physical summary metrics. Single-run evidence,
+  not a statistical performance guarantee; no claim of multi-world speedup.
+- Runtime: isolated Python 3.11, Torch 2.10, Newton 1.6.0, Warp 1.17.0,
+  MuJoCo/MuJoCo-Warp 3.12.0; DexSim native binaries from local base build and
+  independently staged changed JIT source. Shared build and environments remain
+  unchanged. NoRender robot smoke teardown still logs native entity balance
+  `1024 != 1023`; tests complete, but repeated native resource-leak qualification
+  is not established by these short runs.
+- Context reviewed and revised: fluid lifecycle/capability, Gym replay and
+  native Newton world/reset ownership. Existing Task Program language/semantics
+  guidance remains accurate because those contracts are reused unchanged.
+
+Evidence (ignored local outputs): `outputs/fluid-task-program/`,
+`outputs/fluid-gym-full/fluid_gym.mp4`, `outputs/fluid-gym-full/fluid_gym_result.json`,
+`outputs/fluid-gym-full/contact-sheet.jpg`, `outputs/fluid-gym-final-smoke/`,
+`outputs/fluid-benchmark/`, `outputs/fluid-wheel/asset-validation.json`.
+
+Reproduction in this checkout uses the isolated native JIT runtime and virtual
+Python at `/home/dex/.codex/worktrees/fluid-sph-worlds/venv/bin/python`, with
+`DEXSIM_NEWTON_ARTIFACT_MODE=jit`; `python -m
+embodichain.lab.scripts.run_fluid_task_program --num-envs 2` runs both phases.
+Old pre-recorded tutorial trajectories lack qualification metadata and are
+intentionally rejected by the Gym adapter.
diff --git a/agent_context/MAP.yaml b/agent_context/MAP.yaml
index 67a985e17..8d4ba0879 100644
--- a/agent_context/MAP.yaml
+++ b/agent_context/MAP.yaml
@@ -44,7 +44,7 @@ topics:
   keywords: [BaseEnv, EmbodiedEnv, EnvCfg, register_env, list-task, run-env, target_control_frequency,
     sim_steps_per_control, step_dt, environment.component, embodiment.component, file-owned physics backend,
     physics_config, ControllerAction, EnvProfiler, startup_summary, dexsim_startup_info,
-    ExpertTrajectoryCfg, joint_command_mode, position_velocity, qpos qvel, expert action schema,
+    FluidReplayEnv, fluid Gym, 流体 Gym, ExpertTrajectoryCfg, joint_command_mode, position_velocity, qpos qvel, expert action schema,
     task_instruction, resolve_demo_instruction,
     show-task, task catalog, task gallery, 任务画廊, physical_objective, 物理目标]
   paths: [topics/env-framework/env-framework.md]
@@ -63,11 +63,13 @@ topics:
   - embodichain/lab/gym/envs/_startup_summary.py
   - embodichain/lab/gym/envs/embodied_env.py
   - embodichain/lab/gym/envs/expert_trajectory.py
+  - embodichain/lab/gym/envs/fluid_replay.py
+  - embodichain/lab/scripts/run_fluid_task_program.py
   - embodichain/lab/gym/envs/demo.py
   - embodichain/lab/gym/utils/profiler.py
   watch_paths: [embodichain/lab/gym/envs/base_env.py, embodichain/lab/gym/envs/embodied_env.py, embodichain/lab/gym/envs/expert_trajectory.py,
-    embodichain/lab/gym/envs/demo.py,
-    embodichain/lab/gym/utils/, embodichain/lab/gym/envs/task_program/, embodichain/cli/, embodichain/lab/scripts/run_env.py,
+    embodichain/lab/gym/envs/demo.py, embodichain/lab/gym/envs/fluid_replay.py,
+    embodichain/lab/scripts/run_fluid_task_program.py, embodichain/lab/gym/utils/, embodichain/lab/gym/envs/task_program/, embodichain/cli/, embodichain/lab/scripts/run_env.py,
     embodichain_tasks/, tests/gym/envs/, embodichain/lab/gym/envs/objectives/,
     embodichain/lab/scripts/evaluate_task_objective.py, tests/lab/scripts/test_evaluate_task_objective.py]
   related_topics: [simulation-system, manager-functor, sim-visualization, task-programs, data-pipeline]
diff --git a/agent_context/topics/env-framework/env-framework.md b/agent_context/topics/env-framework/env-framework.md
index e91c17e6b..61e6164ca 100644
--- a/agent_context/topics/env-framework/env-framework.md
+++ b/agent_context/topics/env-framework/env-framework.md
@@ -26,6 +26,8 @@ Lightweight RL environments have a separate owner in
 
 Read [configuration and registration](configuration.md) for task-first layout,
 component ownership, path resolution, config-owned IDs, and task listing.
+Read [fluid replay](fluid-replay.md) for Task Program generation in MuJoCo followed
+by independent SPH evaluation, fluid configuration and coordinate conventions.
 Read [execution](execution.md) for hooks, bridge acceptance, reset ordering,
 wrappers, and replay. Read [profiling](profiling.md) only for instrumentation.
 
diff --git a/agent_context/topics/env-framework/fluid-replay.md b/agent_context/topics/env-framework/fluid-replay.md
new file mode 100644
index 000000000..2a13d87a2
--- /dev/null
+++ b/agent_context/topics/env-framework/fluid-replay.md
@@ -0,0 +1,38 @@
+# Fluid Gym integration
+
+`EmbodiedEnvCfg.fluid_object` owns fixed-count fluid declarations. Physical
+components own these alongside rigid entities; `config_to_cfg()` resolves
+fluid point files and explicit relative rigid/visual mesh paths against the
+owning component. Spawn rigid references are resolved before fluid binding.
+
+`managers/observations.py` provides particle state, summary and volume-in-region
+observations. Particle arrays are world-frame; rigid `get_local_pose()` is
+arena-local. Region measurement and `events.reset_fluid_in_container()` add
+arena offsets before comparing or placing particles. Region volume is particle
+count times nominal spacing cubed, not a reconstructed-surface measurement.
+
+## Two-stage Task Program workflow
+
+`lab/scripts/run_fluid_task_program.py` generates the existing pour-cup Task
+Program in MuJoCo, requires program acceptance and measured cup lift/tilt/return,
+and stores provenance in trajectory metadata. A separate process/world runs
+`gym/envs/fluid_replay.py::FluidReplayEnv` with live SPH. The physical deployments
+remain separate files (`env.newton.yaml` versus `env.fluid.yaml`); the latter
+reuses DexSim's SPH solver configuration directly.
+
+Replay broadcasts one accepted source row across environments. It adopts the
+recorded fixed robot root, checks joint compatibility, interpolates measured
+joints/rigid poses on the physics grid, and publishes kinematic boundary twist.
+Each environment has its own clock and selected reset/refill. Newton substeps
+must be one; control intervals may contain multiple physics steps. Completion
+is truncation; reset is required once every row is exhausted.
+
+This is one-way state replay: policy actions do not drive the robot and fluid
+forces do not affect the recorded motion. Action managers, trajectory recording
+and active policy dataset recorders are rejected. Task Program acceptance is
+source provenance; fluid capture metrics remain separate measured outcomes.
+
+Production configuration/measurement checks are in
+`tests/gym/envs/test_fluid_integration.py`; live isolation checks are in
+`tests/sim/objects/test_fluid_batch_runtime.py`. Generated trajectories/videos
+are local qualification evidence, not packaged task assets.
diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
index f5ce42a68..98841531c 100644
--- a/agent_context/topics/simulation-system/fluids.md
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -6,11 +6,15 @@ existing `NewtonPhysicsCfg.solver_cfg` mapping with `solver_type: sph`.
 `SimulationManager.add_fluid_object()` declares through SpawnScene and binds
 `objects/fluid.py` in place at `prepare()`.
 
-The current qualification boundary is one Newton CUDA environment without
-gradients. Fixed particle counts and shared density/spacing are required.
+Fluids require Newton CUDA without gradients. Multiple environments require the
+native SPH world-isolation and selected-reset capability flags; older runtimes
+remain restricted to one environment. Fixed particle counts and shared
+density/spacing are required.
 The prepared particle scene has fixed topology: adding, replacing or removing
-rigid assets also requires a new scene. Batch Gym integration remains gated on demo parity and native SPH world
-isolation and selected-world reset; arena spacing does not prove isolation.
+rigid assets also requires a new scene. Arena spacing does not prove isolation:
+native kernels filter neighbors and boundaries by world and keep convergence
+statistics per world. State writes clear only selected solver caches through
+canonical reset masks, preserving shared boundary history.
 
 Fluid state uses world-frame `(instances, particles, 3|6)` Torch snapshots.
 Writes update Scene particle batches; resetting transforms a saved local
@@ -34,5 +38,7 @@ shape. Explicit `force_sdf=False` permits pre-baked mesh SDFs.
 
 Focused checks are `tests/sim/test_fluid_cfg.py`,
 `tests/sim/objects/test_fluid_object.py` and the serial CUDA lifecycle test
-`tests/sim/objects/test_fluid_runtime.py`. The pouring tutorial is an end-to-end
+`tests/sim/objects/test_fluid_runtime.py` and `test_fluid_batch_runtime.py`.
+For YAML declarations, refill observations and Task Program replay, see
+[fluid Gym integration](../env-framework/fluid-replay.md). The pouring tutorial is an end-to-end
 qualification workload, not a unit-test import target.
diff --git a/docs/source/api_reference/public_api.rst b/docs/source/api_reference/public_api.rst
index a6fcee2b2..5add03d88 100644
--- a/docs/source/api_reference/public_api.rst
+++ b/docs/source/api_reference/public_api.rst
@@ -387,6 +387,22 @@ embodichain.lab.task_program.integrations.simulation.handover
 
    ConfiguredHandOverPoseProvider
 
+embodichain.lab.gym.envs.fluid_replay
+-----------------------------------
+
+.. currentmodule:: embodichain.lab.gym.envs.fluid_replay
+
+One-way evaluation of qualified Task Program recordings with live SPH. Each
+row has an independent replay clock and reset. Policy action recording is
+unsupported because the measured source states drive motion.
+
+.. autosummary::
+
+   FluidReplayEnv
+
+.. autoclass:: FluidReplayEnv
+   :members:
+
 embodichain.lab.gym.envs.managers.action_manager
 ------------------------------------------------
 
@@ -606,6 +622,21 @@ embodichain.lab.scripts.run_env
    main
    preview
 
+embodichain.lab.scripts.run_fluid_task_program
+--------------------------------------------
+
+.. currentmodule:: embodichain.lab.scripts.run_fluid_task_program
+
+Generate and qualify the pour-cup Task Program in MuJoCo, then run fluid Gym
+replay in a separate world. The source trajectory and fluid metrics have
+separate acceptance records.
+
+.. autosummary::
+
+   main
+
+.. autofunction:: main
+
 embodichain.lab.sim
 -------------------
 
@@ -2487,6 +2518,20 @@ kernel policy; callers log and apply that seed to their own generators.
 .. autofunction:: resolve_seed
 
 
+embodichain_tasks.manipulation.tableware.pour_cup
+----------------------------------------------
+
+.. currentmodule:: embodichain_tasks.manipulation.tableware.pour_cup
+
+The registered Franka pouring replay deployment requires a previously
+qualified Task Program trajectory passed to the environment constructor.
+
+.. autosummary::
+
+   PourCupFluidReplayEnv
+
+.. autoclass:: PourCupFluidReplayEnv
+
 .. toctree::
    :hidden:
 
diff --git a/embodichain/lab/gym/envs/embodied_env.py b/embodichain/lab/gym/envs/embodied_env.py
index 34bb8229f..24cfac173 100644
--- a/embodichain/lab/gym/envs/embodied_env.py
+++ b/embodichain/lab/gym/envs/embodied_env.py
@@ -31,6 +31,8 @@
 import numpy as np
 import gymnasium as gym
 
+from embodichain.lab.sim.cfg.fluid import FluidObjectCfg
+
 from dataclasses import MISSING
 from typing import (
     TYPE_CHECKING,
@@ -198,6 +200,9 @@ class EnvLightCfg:
 
     articulation: List[ArticulationCfg] = []
 
+    fluid_object: list[FluidObjectCfg] = []
+    """Fixed-count fluids owned by the physical environment component."""
+
     events: Union[object, None] = None
     """Event settings. Defaults to None, in which case no events are applied through the event manager.
 
@@ -2676,6 +2681,9 @@ def _setup_interactive_objects(self) -> None:
         for cfg in self.cfg.rigid_object_group:
             self.sim.add_rigid_object_group(cfg=cfg)
 
+        for cfg in self.cfg.fluid_object:
+            self.sim.add_fluid_object(cfg)
+
     def preview_sensor_data(
         self,
         name: str,
diff --git a/embodichain/lab/gym/envs/fluid_replay.py b/embodichain/lab/gym/envs/fluid_replay.py
new file mode 100644
index 000000000..c814ca7bf
--- /dev/null
+++ b/embodichain/lab/gym/envs/fluid_replay.py
@@ -0,0 +1,331 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""One-way fluid Gym replay of an accepted Task Program recording."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from copy import deepcopy
+from typing import Any
+import math
+import numpy as np
+import torch
+from scipy.interpolate import PchipInterpolator
+from scipy.spatial.transform import Rotation, Slerp
+
+from embodichain.lab.gym.envs.embodied_env import EmbodiedEnv, EmbodiedEnvCfg
+from embodichain.lab.gym.utils.gym_utils import load_trajectory
+from embodichain.lab.sim.cfg import NewtonPhysicsCfg
+
+__all__ = ["FluidReplayEnv"]
+
+
+class FluidReplayEnv(EmbodiedEnv):
+    """Replay measured rigid states while continuing to solve fluid physics.
+
+    The source must have been generated and accepted by Task Program. This is
+    a replay/evaluation environment: policy actions do not control the robot.
+    Gym reset/step, sensors and configured observation/reward managers still
+    execute normally. No rigid-fluid force feedback is modeled.
+
+    Args:
+        cfg: Physical environment configuration, including fluid declarations.
+        trajectory: Accepted source recording (path or loaded trajectory dict).
+        speed: Positive source-time multiplier.
+        hold_seconds: Initial and final stationary intervals in simulation time.
+        kwargs: Forwarded environment constructor options.
+    """
+
+    def __init__(
+        self,
+        cfg: EmbodiedEnvCfg,
+        *,
+        trajectory: str | dict,
+        speed: float = 2.0,
+        hold_seconds: float = 1.0,
+        **kwargs: Any,
+    ) -> None:
+        if (
+            not math.isfinite(speed)
+            or speed <= 0
+            or not math.isfinite(hold_seconds)
+            or hold_seconds < 0
+        ):
+            raise ValueError(
+                "Replay speed must be positive and hold_seconds nonnegative."
+            )
+        if cfg.record_trajectory or cfg.actions is not None:
+            raise ValueError(
+                "Fluid replay does not support action managers or trajectory/action recording."
+            )
+        if (
+            not cfg.filter_dataset_saving
+            and cfg.dataset is not None
+            and any(
+                not key.startswith("_")
+                for key in (
+                    cfg.dataset
+                    if isinstance(cfg.dataset, Mapping)
+                    else vars(cfg.dataset)
+                )
+            )
+        ):
+            raise ValueError(
+                "Fluid replay does not support policy dataset recording; record measured fluid diagnostics separately."
+            )
+        self._source = load_trajectory(
+            deepcopy(trajectory) if isinstance(trajectory, dict) else trajectory
+        )
+        meta = self._source["meta"]
+        qualification = meta.get("fluid_source", {})
+        if (
+            qualification.get("kind") != "task_program"
+            or qualification.get("accepted") is not True
+            or not isinstance(qualification.get("program_hash"), str)
+            or not qualification["program_hash"]
+        ):
+            raise ValueError(
+                "Fluid replay requires a Task Program recording with accepted fluid_source qualification."
+            )
+        if int(meta["num_envs"]) != 1:
+            raise ValueError(
+                "The initial fluid replay adapter broadcasts one qualified source row."
+            )
+        count = int(meta["lengths"][0])
+        dt = float(meta["dt"])
+        if count < 2 or not math.isfinite(dt) or dt <= 0:
+            raise ValueError(
+                "Replay needs at least two source frames at a finite positive cadence."
+            )
+        self._source_times = np.arange(count, dtype=float) * dt
+        self._speed = speed
+        self._hold_seconds = hold_seconds
+        self.replay_duration = 2 * hold_seconds + self._source_times[-1] / speed
+        cfg = deepcopy(cfg)
+        physics = cfg.sim_cfg.physics_cfg
+        solver = getattr(physics, "solver_cfg", None)
+        solver_type = (
+            solver.get("solver_type")
+            if isinstance(solver, dict)
+            else getattr(solver, "solver_type", None)
+        )
+        if not isinstance(physics, NewtonPhysicsCfg) or solver_type != "sph":
+            raise ValueError("Fluid replay requires Newton SPH.")
+        if cfg.sim_cfg.physics_cfg.num_substeps != 1:
+            raise ValueError(
+                "Fluid Gym replay requires num_substeps=1; choose physics_dt for boundary cadence."
+            )
+        states = self._source["states"]
+        joints = states["robot", "qpos"][0, :count].cpu().numpy()
+        self._joint_curve = PchipInterpolator(self._source_times, joints)
+        cfg.robot.init_qpos = joints[0].tolist()
+        roots = states["robot", "root_pose"][0, :count].cpu().numpy()
+        root_rotations = Rotation.from_quat(roots[:, 3:]).as_matrix()
+        if not np.allclose(roots[:, :3], roots[0, :3], atol=1e-5) or not np.allclose(
+            root_rotations, root_rotations[0], atol=1e-5
+        ):
+            raise ValueError(
+                "Fluid replay currently supports fixed-root robot recordings only."
+            )
+        root_pose = np.eye(4, dtype=np.float32)
+        root_pose[:3, :3] = root_rotations[0]
+        root_pose[:3, 3] = roots[0, :3]
+        cfg.robot.init_local_pose = root_pose
+
+        self._rigid_curves = {}
+        objects = {item.uid: item for item in cfg.background + cfg.rigid_object}
+        for uid in meta.get("rigid_object_uids", []):
+            if uid not in objects:
+                continue
+            if objects[uid].body_type != "kinematic":
+                raise ValueError(f"Replayed rigid object {uid!r} must be kinematic.")
+            poses = states["rigid_objects", uid, "pose"][0, :count].cpu().numpy()
+            self._rigid_curves[uid] = (
+                PchipInterpolator(self._source_times, poses[:, :3]),
+                Slerp(self._source_times, Rotation.from_quat(poses[:, 3:])),
+            )
+            pose = np.eye(4, dtype=np.float32)
+            pose[:3, :3] = Rotation.from_quat(poses[0, 3:]).as_matrix()
+            pose[:3, 3] = poses[0, :3]
+            objects[uid].init_local_pose = pose
+        if not self._rigid_curves:
+            raise ValueError("No recorded rigid objects match the replay scene.")
+        self._replay_time = np.zeros(cfg.num_envs, dtype=np.float64)
+        self._previous_rigid = {}
+        super().__init__(cfg, **kwargs)
+        try:
+            if joints.shape[1] != self.robot.dof:
+                raise ValueError(
+                    "Source robot DOF count does not match the replay embodiment."
+                )
+            if "active_joint_ids" in meta and list(meta["active_joint_ids"]) != list(
+                self.active_joint_ids
+            ):
+                raise ValueError(
+                    "Source active joint order does not match the replay embodiment."
+                )
+            if "joint_names" in meta:
+                expected = [self.robot.joint_names[i] for i in self.active_joint_ids]
+                if list(meta["joint_names"]) != expected:
+                    raise ValueError(
+                        "Source joint names do not match the replay embodiment."
+                    )
+        except Exception:
+            self._cleanup_failed_initialization()
+            raise
+        self._replay_no_auto_reset = True
+
+    def step(
+        self, action: Any, **kwargs: Any
+    ) -> tuple[Any, torch.Tensor, torch.Tensor, torch.Tensor, dict]:
+        """Advance replay; reset is required after every row is exhausted."""
+        if np.all(self._replay_time >= self.replay_duration):
+            raise RuntimeError(
+                "Fluid replay is exhausted; reset before stepping again."
+            )
+        return super().step(action, **kwargs)
+
+    def check_truncated(self, obs: Any, info: dict) -> torch.Tensor:
+        """Signal each row's replay exhaustion independently."""
+        return super().check_truncated(obs, info) | torch.as_tensor(
+            self._replay_time >= self.replay_duration, device=self.device
+        )
+
+    def _step_action(self, action: Any) -> Any:
+        """Preserve the Gym lifecycle while motion comes from recorded states."""
+        return action
+
+    def _apply_replay_state(self, ids: list[int], dt: float) -> None:
+        if not ids:
+            return
+        source_times = np.clip(
+            (self._replay_time[ids] - self._hold_seconds) * self._speed,
+            0.0,
+            self._source_times[-1],
+        )
+        qpos = torch.as_tensor(
+            self._joint_curve(source_times), dtype=torch.float32, device=self.device
+        )
+        self.robot.set_qpos(qpos, env_ids=ids, target=False)
+        active = (self._replay_time[ids] >= self._hold_seconds) & (
+            self._replay_time[ids]
+            < self._hold_seconds + self._source_times[-1] / self._speed
+        )
+        qvel = (
+            self._joint_curve.derivative()(source_times) * self._speed * active[:, None]
+        )
+        self.robot.set_qvel(
+            torch.as_tensor(qvel, dtype=torch.float32, device=self.device),
+            env_ids=ids,
+            target=False,
+        )
+        if not hasattr(self, "_replay_offsets"):
+            self._replay_offsets = self.sim.arena_offsets.detach().cpu().numpy().copy()
+        offsets = self._replay_offsets[ids]
+        for uid, (positions, rotations) in self._rigid_curves.items():
+            poses = np.repeat(np.eye(4, dtype=np.float32)[None], len(ids), axis=0)
+            poses[:, :3, :3] = rotations(source_times).as_matrix()
+            poses[:, :3, 3] = positions(source_times) + offsets
+            previous = self._previous_rigid.get(uid)
+            if previous is None:
+                previous = np.repeat(
+                    np.eye(4, dtype=np.float32)[None], self.num_envs, axis=0
+                )
+                self._previous_rigid[uid] = previous
+                previous[ids] = poses
+            old = previous[ids].copy()
+            linear = (
+                (poses[:, :3, 3] - old[:, :3, 3]) / dt
+                if dt > 0
+                else np.zeros((len(ids), 3))
+            )
+            angular = (
+                Rotation.from_matrix(
+                    poses[:, :3, :3] @ old[:, :3, :3].transpose(0, 2, 1)
+                ).as_rotvec()
+                / dt
+                if dt > 0
+                else np.zeros((len(ids), 3))
+            )
+            self.sim.get_rigid_object(uid).set_kinematic_state(
+                torch.from_numpy(poses),
+                torch.from_numpy(linear),
+                torch.from_numpy(angular),
+                env_ids=ids,
+            )
+            previous[ids] = poses
+
+    def _advance_physics(self) -> None:
+        sensors = [
+            sensor for sensor in self.sensors.values() if sensor.requires_substep_update
+        ]
+        for sensor in sensors:
+            sensor.begin_control_step()
+        ids = list(range(self.num_envs))
+        for _ in range(self.cfg.sim_steps_per_control):
+            self._apply_replay_state(ids, self.physics_dt)
+            self.sim.update(step=1, render_final_step=False)
+            self._replay_time += self.physics_dt
+            for sensor in sensors:
+                sensor.update_physics_step(self.physics_dt)
+
+    def _initialize_episode(self, env_ids=None, **kwargs: Any) -> None:
+        super()._initialize_episode(env_ids=env_ids, **kwargs)
+        ids = (
+            list(range(self.num_envs))
+            if env_ids is None
+            else torch.as_tensor(env_ids).cpu().tolist()
+        )
+        self._replay_time[ids] = 0.0
+        self._apply_replay_state(ids, 0.0)
+        # Reset-mode events may have randomized the container; refill only the
+        # selected rows from its measured world pose and the local fill template.
+        for cfg in self.cfg.fluid_object:
+            if cfg.spawn.reference_uid is None:
+                continue
+            from embodichain.lab.gym.envs.managers.cfg import SceneEntityCfg
+            from embodichain.lab.gym.envs.managers.events import (
+                reset_fluid_in_container,
+            )
+
+            reset_fluid_in_container(
+                self,
+                ids,
+                [
+                    SceneEntityCfg(uid=cfg.uid),
+                    SceneEntityCfg(uid=cfg.spawn.reference_uid),
+                ],
+            )
+
+    @property
+    def source_qualification(self) -> dict:
+        """Return independent Task Program provenance and source acceptance."""
+        return deepcopy(self._source["meta"]["fluid_source"])
+
+    def get_info(self, **kwargs: Any) -> dict:
+        """Return normal Gym info with row-local replay time and completion."""
+        info = super().get_info(**kwargs)
+        info["fluid_replay_time_s"] = torch.as_tensor(
+            self._replay_time.copy(), device=self.device
+        )
+        info["fluid_replay_finished"] = torch.as_tensor(
+            self._replay_time >= self.replay_duration, device=self.device
+        )
+        info["fluid_source_accepted"] = torch.ones(
+            self.num_envs, dtype=torch.bool, device=self.device
+        )
+        return info
diff --git a/embodichain/lab/gym/envs/managers/events.py b/embodichain/lab/gym/envs/managers/events.py
index 2de46336d..0416d90ec 100644
--- a/embodichain/lab/gym/envs/managers/events.py
+++ b/embodichain/lab/gym/envs/managers/events.py
@@ -56,6 +56,7 @@
 
 
 __all__ = [
+    "reset_fluid_in_container",
     "apply_expansion_profile_reset",
     "replace_assets_from_group",
     "prepare_extra_attr",
@@ -809,3 +810,34 @@ def remove_rigid_constraint(
         name: Base constraint name to remove.
     """
     env.sim.remove_rigid_constraint(name, env_ids=env_ids)
+
+
+def reset_fluid_in_container(
+    env: EmbodiedEnv,
+    env_ids: Sequence[int] | torch.Tensor,
+    entity_cfgs: list[SceneEntityCfg],
+) -> None:
+    """Refill selected rows from the declared local template after container reset.
+
+    Args:
+        env: Owning environment.
+        env_ids: Selected environment indices.
+        entity_cfgs: Fluid and rigid container selections, in that order.
+    """
+    from embodichain.lab.sim.spawn.descriptors import _pose_from_cfg
+    from embodichain.utils.math import quat_from_matrix
+
+    if len(entity_cfgs) != 2:
+        raise ValueError("Expected fluid and container selections.")
+    fluid = env.sim.get_fluid_object(entity_cfgs[0].uid)
+    container = env.sim.get_rigid_object(entity_cfgs[1].uid)
+    if fluid is None or container is None:
+        raise ValueError("Unknown fluid or container.")
+    ids = torch.as_tensor(env_ids, device=env.device, dtype=torch.long)
+    local = torch.as_tensor(
+        _pose_from_cfg(fluid.cfg), dtype=torch.float32, device=env.device
+    )
+    world = container.get_local_pose(to_matrix=True)[ids] @ local
+    world[:, :3, 3] += env.sim.arena_offsets[ids]
+    pose = torch.cat((world[:, :3, 3], quat_from_matrix(world[:, :3, :3])), dim=-1)
+    fluid.reset_to_pose(pose, ids)
diff --git a/embodichain/lab/gym/envs/managers/observations.py b/embodichain/lab/gym/envs/managers/observations.py
index 45374c9ef..211f6c74a 100644
--- a/embodichain/lab/gym/envs/managers/observations.py
+++ b/embodichain/lab/gym/envs/managers/observations.py
@@ -1179,3 +1179,134 @@ def __call__(
         self._cache[uid] = result.clone()
 
         return result
+
+
+def get_fluid_particle_state(
+    env: EmbodiedEnv, obs: EnvObs, entity_cfg: SceneEntityCfg
+) -> torch.Tensor:
+    """Return opt-in world-frame particle state ``(E, N, 6)``.
+
+    Args:
+        env: Owning environment.
+        obs: Current observations, unused.
+        entity_cfg: Fluid selection.
+
+    Returns:
+        Independent positions/velocities; no full-particle recording is enabled implicitly.
+    """
+    del obs
+    fluid = env.sim.get_fluid_object(entity_cfg.uid)
+    if fluid is None:
+        raise ValueError(f"Unknown fluid {entity_cfg.uid!r}.")
+    return fluid.data.particle_state_w
+
+
+def get_fluid_summary(
+    env: EmbodiedEnv, obs: EnvObs, entity_cfg: SceneEntityCfg
+) -> torch.Tensor:
+    """Return mass, nominal volume, mean speed, RMS speed and finite fraction.
+
+    Args:
+        env: Owning environment.
+        obs: Current observations, unused.
+        entity_cfg: Fluid selection.
+
+    Returns:
+        ``(E, 5)`` GPU tensor in kg, cubic metres and metres/second.
+    """
+    state = get_fluid_particle_state(env, obs, entity_cfg)
+    fluid = env.sim.get_fluid_object(entity_cfg.uid)
+    speeds = state[..., 3:].norm(dim=-1)
+    return torch.stack(
+        (
+            speeds.new_full((env.num_envs,), fluid.particle_mass * fluid.num_particles),
+            speeds.new_full((env.num_envs,), fluid.nominal_volume),
+            speeds.mean(-1),
+            speeds.square().mean(-1).sqrt(),
+            torch.isfinite(state).all(-1).float().mean(-1),
+        ),
+        dim=-1,
+    )
+
+
+def get_fluid_volume_in_region(
+    env: EmbodiedEnv,
+    obs: EnvObs,
+    entity_cfgs: list[SceneEntityCfg],
+    lower: list[float],
+    upper: list[float],
+    sphere_center: list[float] | None = None,
+    sphere_radius: float | None = None,
+) -> torch.Tensor:
+    """Measure nominal fluid volume inside a rigid-local region.
+
+    Args:
+        env: Owning environment.
+        obs: Current observations, unused.
+        entity_cfgs: Fluid followed by the rigid reference object.
+        lower: Inclusive local AABB lower corner, metres.
+        upper: Inclusive local AABB upper corner, metres.
+        sphere_center: Optional local sphere centre intersected with the AABB.
+        sphere_radius: Positive sphere radius, metres, paired with sphere_center.
+
+    Returns:
+        ``(E, 1)`` contained rest volume in cubic metres.
+    """
+    from embodichain.utils.math import matrix_from_quat
+    import math
+
+    if len(entity_cfgs) != 2:
+        raise ValueError(
+            "Region measurement requires fluid and rigid-reference selections."
+        )
+    fluid = env.sim.get_fluid_object(entity_cfgs[0].uid)
+    rigid = env.sim.get_rigid_object(entity_cfgs[1].uid)
+    if fluid is None or rigid is None:
+        raise ValueError("Unknown fluid or region reference.")
+    bounds = torch.as_tensor([lower, upper], dtype=torch.float32, device=env.device)
+    if (
+        bounds.shape != (2, 3)
+        or not torch.isfinite(bounds).all()
+        or not (bounds[1] > bounds[0]).all()
+    ):
+        raise ValueError("Region bounds must be finite increasing 3D corners.")
+    pose = rigid.get_local_pose()
+    origin_w = pose[:, :3] + env.sim.arena_offsets
+    local = torch.bmm(
+        fluid.data.particle_pos_w - origin_w[:, None], matrix_from_quat(pose[:, 3:])
+    )
+    inside = ((local >= bounds[0]) & (local <= bounds[1])).all(-1)
+    if (sphere_center is None) != (sphere_radius is None):
+        raise ValueError("sphere_center and sphere_radius must be supplied together.")
+    if sphere_center is not None:
+        center = torch.as_tensor(sphere_center, dtype=torch.float32, device=env.device)
+        if (
+            center.shape != (3,)
+            or not torch.isfinite(center).all()
+            or not math.isfinite(sphere_radius)
+            or sphere_radius <= 0
+        ):
+            raise ValueError("Sphere needs a finite centre and positive radius.")
+        inside &= (local - center).square().sum(-1) <= sphere_radius**2
+    return inside.sum(-1, keepdim=True).float() * fluid.cfg.particle_spacing**3
+
+
+__all__ = [
+    "get_object_pose",
+    "get_rigid_object_pose",
+    "get_object_body_scale",
+    "get_object_uid",
+    "get_rigid_object_velocity",
+    "normalize_robot_joint_data",
+    "get_sensor_pose_in_robot_frame",
+    "get_sensor_intrinsics",
+    "compute_semantic_mask",
+    "get_robot_eef_pose",
+    "target_position",
+    "compute_exteroception",
+    "get_rigid_object_physics_attributes",
+    "get_articulation_joint_drive",
+    "get_fluid_particle_state",
+    "get_fluid_summary",
+    "get_fluid_volume_in_region",
+]
diff --git a/embodichain/lab/gym/utils/_component_composition.py b/embodichain/lab/gym/utils/_component_composition.py
index 5ffa6ff81..b33d0d402 100644
--- a/embodichain/lab/gym/utils/_component_composition.py
+++ b/embodichain/lab/gym/utils/_component_composition.py
@@ -59,10 +59,29 @@
         "rigid_object",
         "rigid_object_group",
         "articulation",
+        "fluid_object",
     }
 )
 
 
+def _resolve_physical_source_paths(simulation: dict, base_dir: Path) -> dict:
+    """Anchor file-backed fluid sources and explicitly relative visual meshes."""
+    result = deepcopy(dict(simulation))
+    for field in ("background", "rigid_object"):
+        for item in result.get(field, []):
+            for key in ("shape", "visual_shape"):
+                shape = item.get(key) or {}
+                path = shape.get("fpath")
+                if isinstance(path, str) and path.startswith(("./", "../")):
+                    shape["fpath"] = str((base_dir / path).resolve())
+    for fluid in result.get("fluid_object", []):
+        spawn = fluid.get("spawn", {})
+        path = spawn.get("file_path")
+        if isinstance(path, str) and not Path(path).is_absolute():
+            spawn["file_path"] = str((base_dir / path).resolve())
+    return result
+
+
 @dataclass(frozen=True, slots=True)
 class _ResolvedGymComponents:
     """One Gym config after its reusable components are expanded."""
@@ -221,7 +240,9 @@ def _resolve_environment_component(
         for key, value in component.items()
         if key not in {"environment_id", "simulation"}
     }
-    environment_values.update(deepcopy(dict(simulation)))
+    environment_values.update(
+        _resolve_physical_source_paths(dict(simulation), component_path.parent)
+    )
 
     deployment_physics_fields = sorted(
         {"physics", "physics_config"}.intersection(resolved)
@@ -328,7 +349,7 @@ def _resolve_scene_component(
         required=frozenset(),
         optional=_SCENE_SIMULATION_FIELDS,
     )
-    return deepcopy(dict(simulation))
+    return _resolve_physical_source_paths(dict(simulation), component_path.parent)
 
 
 def _physical_scene_uids(
diff --git a/embodichain/lab/gym/utils/gym_utils.py b/embodichain/lab/gym/utils/gym_utils.py
index 426421046..42cd873d3 100644
--- a/embodichain/lab/gym/utils/gym_utils.py
+++ b/embodichain/lab/gym/utils/gym_utils.py
@@ -737,6 +737,17 @@ class ComponentCfg:
         ]
         env_cfg.light.indirect = config["light"].get("indirect", None)
 
+    # Fluids use one typed declaration path for inline and component scenes.
+    from embodichain.lab.sim.cfg import FluidObjectCfg
+    from embodichain.lab.gym.utils._component_composition import (
+        _resolve_physical_source_paths,
+    )
+
+    config = _resolve_physical_source_paths(config, base_dir)
+    env_cfg.fluid_object = [
+        FluidObjectCfg.from_dict(item) for item in config.get("fluid_object", [])
+    ]
+
     # parser background objects config
     if "background" in config:
         for obj_dict in config["background"]:
@@ -885,6 +896,12 @@ class ComponentCfg:
                 )
                 obs_params_modified["params"]["entity_cfg"] = entity_cfg
 
+            if "entity_cfgs" in obs_params_modified["params"]:
+                obs_params_modified["params"]["entity_cfgs"] = [
+                    SceneEntityCfg(**item)
+                    for item in obs_params_modified["params"]["entity_cfgs"]
+                ]
+
             # Find the function from multiple modules using the utility function
             obs_func = find_function_from_modules(
                 obs_params["func"],
@@ -897,6 +914,7 @@ class ComponentCfg:
                 mode=obs_params_modified["mode"],
                 name=obs_params_modified["name"],
                 params=obs_params_modified["params"],
+                extra=deepcopy(obs_params_modified.get("extra", {})),
             )
 
             setattr(env_cfg.observations, obs_name, observation)
diff --git a/embodichain/lab/scripts/run_fluid_task_program.py b/embodichain/lab/scripts/run_fluid_task_program.py
new file mode 100644
index 000000000..15b8e5492
--- /dev/null
+++ b/embodichain/lab/scripts/run_fluid_task_program.py
@@ -0,0 +1,296 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Generate a qualified Task Program trajectory, then evaluate fluid Gym replay."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+import subprocess
+import sys
+import time
+import numpy as np
+import torch
+from scipy.spatial.transform import Rotation
+
+from embodichain.lab.gym.utils.gym_utils import config_to_cfg, load_trajectory
+from embodichain.lab.gym.utils.registration import make, discover_task_packages
+from embodichain.lab.gym.envs.demo import execute_demo_episode
+from embodichain.lab.sim import SimulationManager
+from embodichain.lab.sim.cfg import RenderCfg
+from embodichain.utils.config_paths import resolve_config_path
+from embodichain.utils.utility import load_config
+
+__all__ = ["main"]
+
+_TASK = "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/"
+
+
+def _generate(source_path: Path, output: Path) -> None:
+    declaration = load_config(str(source_path))
+    cfg = config_to_cfg(declaration, source_path=source_path)
+    if cfg.task_program is None:
+        raise ValueError("Source deployment must select a Task Program.")
+    cfg.num_envs = 1
+    cfg.sim_cfg.headless = True
+    cfg.sim_cfg.render_cfg = RenderCfg(renderer="hybrid")
+    cfg.enable_sensor = False
+    if hasattr(cfg.events, "record_demo"):
+        delattr(cfg.events, "record_demo")
+    cfg.record_trajectory = True
+    env = make(declaration["id"], cfg=cfg)
+    try:
+        env.reset(seed=0)
+        result = execute_demo_episode(env)
+        report = result.to_metadata()
+        (output / "task_program_result.json").write_text(
+            json.dumps(report, indent=2) + "\n"
+        )
+        if not result.completed or not result.all_success:
+            raise RuntimeError("Task Program failed; fluid replay will not start.")
+        path = output / "task_program_trajectory.pt"
+        env.save_trajectory(str(path))
+        data = load_trajectory(path)
+        poses = data["states"]["rigid_objects", "cup", "pose"][0].cpu().numpy()
+        lift = float(np.max(poses[:, 2]) - poses[0, 2])
+        z_axis = Rotation.from_quat(poses[:, 3:]).as_matrix()[:, :, 2]
+        tilt = float(np.rad2deg(np.arccos(np.clip(z_axis[:, 2], -1, 1))).max())
+        return_error = float(np.linalg.norm(poses[-1, :3] - poses[0, :3]))
+        accepted = lift >= 0.05 and tilt >= 45.0 and return_error <= 0.03
+        qualification = {
+            "kind": "task_program",
+            "accepted": accepted,
+            "source_config": str(source_path),
+            "program_hash": data["meta"].get("program_hash"),
+            "lift_m": lift,
+            "max_tilt_deg": tilt,
+            "return_error_m": return_error,
+            "task_program_completed": result.completed,
+        }
+        data["meta"]["fluid_source"] = qualification
+        torch.save(data, path)
+        (output / "source_qualification.json").write_text(
+            json.dumps(qualification, indent=2) + "\n"
+        )
+        if not accepted:
+            raise RuntimeError(
+                "Measured cup lift/tilt/return failed source qualification."
+            )
+        print(json.dumps(qualification), flush=True)
+    finally:
+        env.close(exit_process=False)
+    del env
+    SimulationManager.flush_cleanup_queue()
+
+
+def _replay(args, trajectory: Path, output: Path) -> None:
+    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
+
+    path = resolve_config_path(args.replay_task).resolve()
+    cfg = config_to_cfg(load_config(str(path)), source_path=path)
+    cfg.num_envs = args.num_envs
+    cfg.sim_cfg.headless = True
+    cfg.sim_cfg.render_cfg = RenderCfg(renderer=args.renderer)
+    cfg.enable_sensor = args.renderer != "no-render"
+    if args.particle_spacing is not None:
+        # Qualification smoke mode uses a coarser initial fill, explicitly recorded.
+        fluid = cfg.fluid_object[0]
+        from embodichain.lab.sim.cfg import FluidSpawnCfg
+
+        fluid.particle_spacing = args.particle_spacing
+        fluid.particle_radius = 0.3 * args.particle_spacing
+        fluid.spawn = FluidSpawnCfg(
+            kind="cylinder", radius=0.028, height=0.07, reference_uid="cup"
+        )
+        cfg.sim_cfg.physics_cfg.solver_cfg["particle_support_radius"] = (
+            2.25 * args.particle_spacing
+        )
+    env = FluidReplayEnv(cfg, trajectory=str(trajectory), speed=args.speed)
+    writer = None
+    try:
+        obs, info = env.reset(seed=0)
+        if cfg.enable_sensor:
+            import imageio.v2 as imageio
+
+            writer = imageio.get_writer(
+                output / "fluid_gym.mp4",
+                fps=round(1 / env.step_dt),
+                codec="libx264",
+                quality=8,
+            )
+        duration = (
+            env.replay_duration
+            if args.seconds is None
+            else min(args.seconds, env.replay_duration)
+        )
+        steps = int(np.ceil(duration / env.step_dt))
+        action = torch.zeros(
+            (env.num_envs, len(env.active_joint_ids)), device=env.device
+        )
+        samples = []
+        started = time.perf_counter()
+        for index in range(steps):
+            obs, reward, terminated, truncated, info = env.step(action)
+            if args.partial_reset_step is not None and index == args.partial_reset_step:
+                before = (
+                    env.sim.get_fluid_object("water").data.particle_state_w[1].clone()
+                )
+                env.reset(
+                    options={
+                        "reset_ids": torch.tensor([0], device=env.device),
+                        "save_data": False,
+                    }
+                )
+                torch.testing.assert_close(
+                    env.sim.get_fluid_object("water").data.particle_state_w[1],
+                    before,
+                    rtol=0,
+                    atol=0,
+                )
+            if writer is not None:
+                writer.append_data(
+                    env.sensors["overview"]
+                    .get_data()["color"][0, :, :, :3]
+                    .cpu()
+                    .numpy()
+                )
+            if index % max(1, round(1 / env.step_dt)) == 0 or index == steps - 1:
+                from embodichain.lab.gym.envs.managers.observations import (
+                    get_fluid_summary,
+                    get_fluid_volume_in_region,
+                )
+                from embodichain.lab.gym.envs.managers.cfg import SceneEntityCfg
+
+                summary = get_fluid_summary(env, obs, SceneEntityCfg(uid="water"))
+                received = get_fluid_volume_in_region(
+                    env,
+                    obs,
+                    [SceneEntityCfg(uid="water"), SceneEntityCfg(uid="receiver")],
+                    lower=[-0.1, -0.1, 0.0045],
+                    upper=[0.1, 0.1, 0.0675],
+                    sphere_center=[0, 0, 0.088],
+                    sphere_radius=0.093,
+                )
+                if not torch.isfinite(summary).all() or not torch.all(
+                    summary[:, -1] == 1
+                ):
+                    raise RuntimeError("Fluid state became non-finite.")
+                sample = {
+                    "time_s": info["fluid_replay_time_s"].cpu().tolist(),
+                    "received_ml": (received[:, 0] * 1e6).cpu().tolist(),
+                    "summary": summary.cpu().tolist(),
+                }
+                samples.append(sample)
+                print(json.dumps(sample), flush=True)
+        report = {
+            "source": env.source_qualification,
+            "coupling": "one-way measured Task Program state replay",
+            "num_envs": env.num_envs,
+            "particle_count_per_env": env.sim.get_fluid_object("water").num_particles,
+            "partial_reset_step": args.partial_reset_step,
+            "replay_complete": info["fluid_replay_finished"].cpu().tolist(),
+            "wall_seconds": time.perf_counter() - started,
+            "samples": samples,
+        }
+        (output / "fluid_gym_result.json").write_text(
+            json.dumps(report, indent=2) + "\n"
+        )
+    finally:
+        if writer is not None:
+            writer.close()
+        env.close(exit_process=False)
+    del env
+    SimulationManager.flush_cleanup_queue()
+
+
+def main() -> None:
+    """Run Task Program source generation and independent fluid evaluation."""
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--source-task", default=_TASK + "task.franka.newton.yaml")
+    parser.add_argument("--replay-task", default=_TASK + "task.franka.fluid.yaml")
+    parser.add_argument(
+        "--output", type=Path, default=Path("outputs/fluid-task-program")
+    )
+    parser.add_argument("--trajectory", type=Path)
+    parser.add_argument("--num-envs", type=int, default=2)
+    parser.add_argument("--speed", type=float, default=2.0)
+    parser.add_argument(
+        "--renderer", choices=["no-render", "rt", "hybrid"], default="rt"
+    )
+    parser.add_argument("--seconds", type=float)
+    parser.add_argument("--particle-spacing", type=float)
+    parser.add_argument("--partial-reset-step", type=int)
+    parser.add_argument(
+        "--phase",
+        choices=["all", "generate", "replay"],
+        default="all",
+        help=argparse.SUPPRESS,
+    )
+    args = parser.parse_args()
+    import math
+
+    if args.num_envs < 1 or not math.isfinite(args.speed) or args.speed <= 0:
+        parser.error("num-envs and speed must be positive")
+    if args.seconds is not None and (
+        not math.isfinite(args.seconds) or args.seconds <= 0
+    ):
+        parser.error("seconds must be finite and positive")
+    if args.particle_spacing is not None and (
+        not math.isfinite(args.particle_spacing) or args.particle_spacing <= 0
+    ):
+        parser.error("particle-spacing must be finite and positive")
+    if args.partial_reset_step is not None and (
+        args.num_envs < 2 or args.partial_reset_step < 0
+    ):
+        parser.error(
+            "partial-reset-step requires at least two environments and a nonnegative step"
+        )
+    output = args.output.resolve()
+    output.mkdir(parents=True, exist_ok=True)
+    discover_task_packages()
+    source = resolve_config_path(args.source_task).resolve()
+    if args.phase == "generate":
+        _generate(source, output)
+        return
+    trajectory = (
+        args.trajectory.resolve()
+        if args.trajectory is not None
+        else output / "task_program_trajectory.pt"
+    )
+    if args.trajectory is None and args.phase == "all":
+        bootstrap = "import json,runpy,sys; sys.path[:]=json.loads(sys.argv.pop(1)); runpy.run_module('embodichain.lab.scripts.run_fluid_task_program',run_name='__main__')"
+        subprocess.run(
+            [
+                sys.executable,
+                "-c",
+                bootstrap,
+                json.dumps(sys.path),
+                "--phase",
+                "generate",
+                "--source-task",
+                str(source),
+                "--output",
+                str(output),
+            ],
+            check=True,
+        )
+    _replay(args, trajectory, output)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/embodichain/lab/sim/objects/fluid.py b/embodichain/lab/sim/objects/fluid.py
index 727cd8b40..29a37ff9d 100644
--- a/embodichain/lab/sim/objects/fluid.py
+++ b/embodichain/lab/sim/objects/fluid.py
@@ -77,7 +77,7 @@ def __init__(
         pose: np.ndarray,
         device: torch.device,
         *,
-        on_write: Callable[[], None],
+        on_write: Callable[[Sequence[int]], None],
     ) -> None:
         self.cfg = deepcopy(cfg)
         self.uid = cfg.uid
@@ -186,7 +186,7 @@ def write_particle_state_to_sim(
             raise ValueError("Fluid state must be finite.")
         if not ids:
             return
-        self._on_write()
+        self._on_write(ids)
         data._state._apply_nodal_state(state[..., :3], state[..., 3:], ids)
 
     def reset(self, env_ids: Sequence[int] | None = None) -> None:
diff --git a/embodichain/lab/sim/physics/newton.py b/embodichain/lab/sim/physics/newton.py
index 688af307f..7cf5bcd51 100644
--- a/embodichain/lab/sim/physics/newton.py
+++ b/embodichain/lab/sim/physics/newton.py
@@ -19,6 +19,7 @@
 
 import importlib
 from typing import TYPE_CHECKING
+from collections.abc import Sequence
 import weakref
 
 import warp as wp
@@ -131,11 +132,26 @@ def sync_render_state(self, result: "dexsim.scene.Scene") -> None:
             simulation_time=backend.sim_time,
         )
 
-    def invalidate_fluid_state(self) -> None:
-        """Invalidate single-world SPH history after an explicit state write.
+    @property
+    def supports_fluid_worlds(self) -> bool:
+        """Whether the loaded SPH artifact supports isolated worlds and reset."""
+        if self.solver_type != "sph":
+            return False
+        from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
+
+        solver = ensure_solver_available("sph")
+        return bool(
+            getattr(solver, "supports_world_isolation", False)
+            and getattr(solver, "supports_world_reset", False)
+        )
 
-        DFSPH rebuilds neighbors, density and Jacobi scratch on each step.
-        Optional SDF-penalty body history is reset to the current pose.
+    def invalidate_fluid_state(self, env_ids: Sequence[int] | None = None) -> None:
+        """Clear selected SPH history after an explicit state write.
+
+        Args:
+            env_ids: Local worlds to clear, or all local worlds when omitted.
+                Shared boundary history remains unchanged. Legacy single-world
+                artifacts rebaseline their optional SDF history directly.
         """
         from dexsim.engine.newton_physics.backend_registry import get_newton_backend
 
@@ -143,6 +159,15 @@ def invalidate_fluid_state(self) -> None:
         if backend is None or backend.solver_type != "sph":
             raise RuntimeError("Prepared SPH backend required.")
         backend.invalidate_cuda_graph("fluid state write")
+        if getattr(backend.solver, "supports_world_reset", False):
+            mask = [
+                env_ids is None or i in env_ids
+                for i in range(backend.model.world_count)
+            ] + [False]
+            backend.clear_solver_state(
+                wp.array(mask, dtype=wp.bool, device=backend.model.device)
+            )
+            return
         boundary = backend.solver.sdf_boundary
         if boundary is not None and boundary._body_q_prev is not None:
             boundary._body_q_prev.assign(backend.state_0.body_q)
diff --git a/embodichain/lab/sim/sim_manager.py b/embodichain/lab/sim/sim_manager.py
index 485f6a25c..3b285c6f5 100644
--- a/embodichain/lab/sim/sim_manager.py
+++ b/embodichain/lab/sim/sim_manager.py
@@ -2731,9 +2731,11 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
 
         if self.physics.name != "newton" or self.device.type != "cuda":
             raise NotImplementedError("Fluid objects require Newton on CUDA.")
-        if self.num_envs != 1:
+        if self.num_envs != 1 and not getattr(
+            self.physics, "supports_fluid_worlds", False
+        ):
             raise NotImplementedError(
-                "Fluid world isolation is not yet qualified; num_envs must be 1."
+                "Batch fluids require a DexSim SPH build with world isolation and selected-world reset."
             )
         if (
             self._active_newton_solver_type != "sph"
@@ -2813,8 +2815,8 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
         self.notify_visualization_topology_changed()
         return fluid
 
-    def _invalidate_fluid_state(self) -> None:
-        self.physics.invalidate_fluid_state()
+    def _invalidate_fluid_state(self, env_ids: Sequence[int] | None = None) -> None:
+        self.physics.invalidate_fluid_state(env_ids=env_ids)
         if self._render_state_published is not None:
             self._render_state_published = False
 
diff --git a/embodichain_tasks/configs/components/embodiments/franka_panda_fluid_replay.yaml b/embodichain_tasks/configs/components/embodiments/franka_panda_fluid_replay.yaml
new file mode 100644
index 000000000..cd05135d5
--- /dev/null
+++ b/embodichain_tasks/configs/components/embodiments/franka_panda_fluid_replay.yaml
@@ -0,0 +1,32 @@
+embodiment_id: franka_panda_fluid_replay
+simulation:
+  class_type: FrankaPanda
+  robot_type: panda
+  uid: FrankaPanda
+  attrs:
+    collision_props:
+      backend: newton
+      has_particle_collision: false
+sensor:
+- sensor_type: Camera
+  uid: overview
+  width: 640
+  height: 480
+  intrinsics:
+  - 550.0
+  - 550.0
+  - 320.0
+  - 240.0
+  extrinsics:
+    eye:
+    - -1.4
+    - -1.4
+    - 1.2
+    target:
+    - -0.43
+    - -0.03
+    - 0.47
+    up:
+    - 0
+    - 0
+    - 1
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
index 10e5cc3d7..69c7fca66 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
@@ -8,9 +8,33 @@
 离屏相机采集前使用 `sim.sync_render_state()` 发布状态，裁剪仅影响显示副本。
 多子步时杯壁轨迹通过 `register_kinematic_rigid_trajectory()` 在 solver 子步采样。
 
-当前支持单个 Newton CUDA 环境、固定粒子数、单向耦合。场景准备后物理拓扑冻结。
-Gym 批量环境及部分 reset 尚未接入；需先完成当前 demo 的完整效果验收，
-再补齐底层 SPH world 隔离和按环境复位。`IMPLEMENTATION_PLAN.md` 记录进度。
+当前支持 Newton CUDA、固定粒子数和单向耦合。场景准备后物理拓扑冻结。
+多环境要求 DexSim SPH 提供 world 隔离及选择性重置能力；旧运行时仍限制为单环境。
+`IMPLEMENTATION_PLAN.md` 记录分阶段验证结果。
+
+## Task Program → 流体 Gym
+
+```bash
+python -m embodichain.lab.scripts.run_fluid_task_program --num-envs 2
+```
+
+默认先在 MuJoCo 中执行现有 Task Program，验证真实杯子抬升、倾倒和放回，
+再启动独立 SPH Gym 回放其关节与杯子实测状态。输出包含源轨迹、源验收报告、
+流体接水量统计和 `fluid_gym.mp4`。复用本工具生成的已验收轨迹可传
+`--trajectory outputs/fluid-task-program/task_program_trajectory.pt`。
+`--renderer no-render --seconds 0.125 --particle-spacing 0.006 --partial-reset-step 1`
+用于低成本双环境局部重置检查；粗粒度检查不代表完整液体效果。
+
+`env.fluid.yaml` 拥有 SPH 参数、刚体边界、初始粒子和流体观测；
+`task.franka.fluid.yaml` 选择环境和机器人组件。粒子文件相对环境配置解析，
+杯子、边界网格和初始粒子随任务包发布。求解器配置直接使用 DexSim SPHSolverCfg。
+
+`FluidReplayEnv` 支持标准 reset/step、按行时钟与流体重置、summary 和区域体积观测。
+它从单行合格源轨迹广播到多个环境，要求固定机器人根位姿和单个 Newton 子步。
+机器人动作由源状态决定，因此拒绝 action manager 和策略 action 数据录制。
+流体不会反向影响机械臂；Task Program 源验收与流体接水效果分别报告。
+本次实测依赖隔离的 DexSim JIT 构建、Newton 1.6 / Warp 1.17；发布 wheel 尚未验收。
+
 
 
 使用真实 Franka Panda 机型的仿真模型，从侧面夹住正常大小的马克杯杯把，
@@ -21,7 +45,7 @@ Gym 批量环境及部分 reset 尚未接入；需先完成当前 demo 的完整
 - **DFSPH 带水演示**：教程脚本回放已验证的 MuJoCo 机械臂和杯子轨迹，
   同时通过 EmbodiChain 的 Newton 后端模拟液体。
 
-这是临时演示，未修改 `embodichain/` 生产代码。液体采用单向耦合：移动杯壁
+原始教程入口保留用于效果对照；生产接入位于 `embodichain/` 生产代码。液体采用单向耦合：移动杯壁
 影响水，水不反作用于机械臂；DFSPH 场景不重新求解夹持接触。
 
 ## 直接运行带水演示
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_floor.obj b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_floor.obj
new file mode 100644
index 000000000..cda0ffe0a
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_floor.obj
@@ -0,0 +1,22 @@
+# https://github.com/mikedh/trimesh
+v -1.25000000 -1.25000000 -0.05000000
+v -1.25000000 -1.25000000 0.05000000
+v -1.25000000 1.25000000 -0.05000000
+v -1.25000000 1.25000000 0.05000000
+v 1.25000000 -1.25000000 -0.05000000
+v 1.25000000 -1.25000000 0.05000000
+v 1.25000000 1.25000000 -0.05000000
+v 1.25000000 1.25000000 0.05000000
+f 2 4 1
+f 5 2 1
+f 1 4 3
+f 3 5 1
+f 2 8 4
+f 6 2 5
+f 6 8 2
+f 4 8 3
+f 7 5 3
+f 3 8 7
+f 7 6 5
+f 8 6 7
+
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_table.obj b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_table.obj
new file mode 100644
index 000000000..8f42220e6
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_table.obj
@@ -0,0 +1,22 @@
+# https://github.com/mikedh/trimesh
+v -0.21000000 -0.35000000 -0.25000000
+v -0.21000000 -0.35000000 0.25000000
+v -0.21000000 0.35000000 -0.25000000
+v -0.21000000 0.35000000 0.25000000
+v 0.21000000 -0.35000000 -0.25000000
+v 0.21000000 -0.35000000 0.25000000
+v 0.21000000 0.35000000 -0.25000000
+v 0.21000000 0.35000000 0.25000000
+f 2 4 1
+f 5 2 1
+f 1 4 3
+f 3 5 1
+f 2 8 4
+f 6 2 5
+f 6 8 2
+f 4 8 3
+f 7 5 3
+f 3 8 7
+f 7 6 5
+f 8 6 7
+
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/mug_wall.obj b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/mug_wall.obj
new file mode 100644
index 000000000..ba21407af
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/mug_wall.obj
@@ -0,0 +1,1540 @@
+# https://github.com/mikedh/trimesh
+v 0.00000000 0.00000000 -0.05000000
+v 0.03500000 0.00000000 -0.05000000
+v 0.04250000 0.00000000 0.05000000
+v 0.03850000 0.00000000 0.05000000
+v 0.03100000 0.00000000 -0.04400000
+v 0.00000000 0.00000000 -0.04400000
+v 0.03495784 0.00171737 -0.05000000
+v 0.04244881 0.00208538 0.05000000
+v 0.03845363 0.00188911 0.05000000
+v 0.03096266 0.00152110 -0.04400000
+v 0.03483147 0.00343060 -0.05000000
+v 0.04229535 0.00416573 0.05000000
+v 0.03831461 0.00377366 0.05000000
+v 0.03085073 0.00303853 -0.04400000
+v 0.03462118 0.00513557 -0.05000000
+v 0.04204000 0.00623605 0.05000000
+v 0.03808330 0.00564912 0.05000000
+v 0.03066447 0.00454864 -0.04400000
+v 0.03432748 0.00682816 -0.05000000
+v 0.04168337 0.00829134 0.05000000
+v 0.03776023 0.00751098 0.05000000
+v 0.03040434 0.00604780 -0.04400000
+v 0.03395109 0.00850431 -0.05000000
+v 0.04122633 0.01032666 0.05000000
+v 0.03734620 0.00935474 0.05000000
+v 0.03007097 0.00753239 -0.04400000
+v 0.03349291 0.01015996 -0.05000000
+v 0.04066996 0.01233710 0.05000000
+v 0.03684220 0.01117596 0.05000000
+v 0.02966515 0.00899882 -0.04400000
+v 0.03295404 0.01179114 -0.05000000
+v 0.04001562 0.01431782 0.05000000
+v 0.03624945 0.01297026 0.05000000
+v 0.02918787 0.01044359 -0.04400000
+v 0.03233578 0.01339392 -0.05000000
+v 0.03926488 0.01626405 0.05000000
+v 0.03556936 0.01473331 0.05000000
+v 0.02864027 0.01186319 -0.04400000
+v 0.03163963 0.01496443 -0.05000000
+v 0.03841954 0.01817109 0.05000000
+v 0.03480359 0.01646087 0.05000000
+v 0.02802367 0.01325421 -0.04400000
+v 0.03086724 0.01649889 -0.05000000
+v 0.03748165 0.02003436 0.05000000
+v 0.03395397 0.01814877 0.05000000
+v 0.02733956 0.01461330 -0.04400000
+v 0.03002050 0.01799360 -0.05000000
+v 0.03645347 0.02184937 0.05000000
+v 0.03302255 0.01979296 0.05000000
+v 0.02658959 0.01593719 -0.04400000
+v 0.02910144 0.01944496 -0.05000000
+v 0.03533746 0.02361173 0.05000000
+v 0.03201158 0.02138945 0.05000000
+v 0.02577556 0.01722268 -0.04400000
+v 0.02811226 0.02084948 -0.05000000
+v 0.03413632 0.02531722 0.05000000
+v 0.03092349 0.02293442 0.05000000
+v 0.02489943 0.01846668 -0.04400000
+v 0.02705537 0.02220376 -0.05000000
+v 0.03285294 0.02696171 0.05000000
+v 0.02976090 0.02442414 0.05000000
+v 0.02396332 0.01966619 -0.04400000
+v 0.02593329 0.02350456 -0.05000000
+v 0.03149042 0.02854126 0.05000000
+v 0.02852662 0.02585502 0.05000000
+v 0.02296948 0.02081833 -0.04400000
+v 0.02474874 0.02474874 -0.05000000
+v 0.03005204 0.03005204 0.05000000
+v 0.02722361 0.02722361 0.05000000
+v 0.02192031 0.02192031 -0.04400000
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diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/receiver_wall.obj b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/receiver_wall.obj
new file mode 100644
index 000000000..ddc8d747b
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/receiver_wall.obj
@@ -0,0 +1,73732 @@
+# https://github.com/mikedh/trimesh
+v 0.00000000 0.00000000 0.00000000
+v 0.04080441 0.00000000 0.00000000
+v 0.04299167 0.00000000 0.00104762
+v 0.04504855 0.00000000 0.00209524
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diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/water_fill.npz b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/water_fill.npz
new file mode 100644
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HcmV?d00001

diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
new file mode 100644
index 000000000..2cd48c31a
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
@@ -0,0 +1,265 @@
+environment_id: pour_cup_fluid
+physics: newton
+num_envs: 2
+arena_space: 2.5
+max_episode_steps: 2000
+max_episodes: 1
+physics_config:
+  physics_dt: 0.0006944444444444445
+  num_substeps: 1
+  collision_cfg: null
+  solver_cfg:
+    solver_type: sph
+    sph_method: dfsph
+    particle_support_radius: 0.003375
+    max_neighbors: 192
+    viscosity: 0.0006
+    surface_tension_coefficient: 0.002
+    surface_normal_threshold: 0.08
+    pressure_relaxation: 0.2
+    divergence_max_iterations: 10
+    density_max_iterations: 20
+    max_velocity: 2.0
+    volume_map_boundary_enabled: true
+    volume_map_resolution: 192
+    volume_map_quadrature_samples: 6
+    volume_map_volume_scale: 0.8
+    sdf_boundary_enabled: false
+simulation:
+  light:
+    direct:
+    - uid: main_light
+      light_type: sun
+      color:
+      - 1.0
+      - 1.0
+      - 1.0
+      intensity: 4.0
+      direction:
+      - -0.3
+      - 0.2
+      - -1.0
+    - uid: fill_light
+      light_type: rect
+      color:
+      - 1.0
+      - 1.0
+      - 1.0
+      intensity: 6.0
+      init_pos:
+      - -0.5
+      - -0.4
+      - 1.2
+      direction:
+      - 0.0
+      - 0.0
+      - -1.0
+      rect_width: 0.7
+      rect_height: 0.7
+  background:
+  - uid: cup_platform
+    body_type: kinematic
+    shape:
+      shape_type: Mesh
+      fpath: ./assets/fluid_table.obj
+      collision:
+        approximation: sdf
+        force_sdf: false
+        sdf_resolution: 192
+        sdf_narrow_band_range:
+        - -0.025
+        - 0.025
+        sdf_padding: 0.012
+        sdf_texture_format: uint16
+      visual_material:
+        uid: fluid_table
+        base_color:
+        - 0.23
+        - 0.29
+        - 0.34
+        - 1.0
+        roughness: 0.6
+        metallic: 0.5
+        ior: 1.0
+    init_pos:
+    - -0.91
+    - -0.08
+    - 0.25
+    attrs:
+      collision_props:
+        backend: newton
+        has_particle_collision: true
+      material_props:
+        backend: newton
+        dynamic_friction: 0.2
+  - uid: fluid_floor
+    body_type: kinematic
+    shape:
+      shape_type: Mesh
+      fpath: ./assets/fluid_floor.obj
+      collision:
+        approximation: sdf
+        force_sdf: false
+        sdf_resolution: 192
+        sdf_narrow_band_range:
+        - -0.025
+        - 0.025
+        sdf_padding: 0.012
+        sdf_texture_format: uint16
+    init_pos:
+    - -0.5
+    - 0
+    - -0.055
+    attrs:
+      collision_props:
+        backend: newton
+        has_particle_collision: true
+      material_props:
+        backend: newton
+        dynamic_friction: 0.2
+    render_enabled: false
+  - uid: cup
+    body_type: kinematic
+    shape:
+      shape_type: Mesh
+      fpath: ./assets/mug_wall.obj
+      collision:
+        approximation: sdf
+        force_sdf: false
+        sdf_resolution: 192
+        sdf_narrow_band_range:
+        - -0.025
+        - 0.025
+        sdf_padding: 0.012
+        sdf_texture_format: uint16
+    init_pos:
+    - -0.75
+    - 0
+    - 0.552
+    attrs:
+      collision_props:
+        backend: newton
+        has_particle_collision: true
+      material_props:
+        backend: newton
+        dynamic_friction: 0.2
+    visual_shape:
+      shape_type: Mesh
+      fpath: ./assets/cup.glb
+  - uid: receiver
+    body_type: kinematic
+    shape:
+      shape_type: Mesh
+      fpath: ./assets/receiver_wall.obj
+      collision:
+        approximation: sdf
+        force_sdf: false
+        sdf_resolution: 192
+        sdf_narrow_band_range:
+        - -0.025
+        - 0.025
+        sdf_padding: 0.012
+        sdf_texture_format: uint16
+      visual_material:
+        uid: fluid_receiver
+        base_color:
+        - 0.82
+        - 0.85
+        - 0.88
+        - 1.0
+        roughness: 0.22
+        metallic: 0.5
+        ior: 1.0
+    init_pos:
+    - -0.755
+    - -0.137
+    - 0.5
+    attrs:
+      collision_props:
+        backend: newton
+        has_particle_collision: true
+      material_props:
+        backend: newton
+        dynamic_friction: 0.2
+  fluid_object:
+  - uid: water
+    density: 1000.0
+    particle_spacing: 0.0015
+    particle_radius: 0.00045
+    spawn:
+      kind: points
+      file_path: ./assets/water_fill.npz
+      reference_uid: cup
+    render:
+      mode: mesh_surface
+      render_radius: 0.00045
+      clip_below_z: 0.49
+      material:
+        base_color:
+        - 0.9452768
+        - 0.982207
+        - 1.0
+        - 1.0
+        roughness: 0.025
+        metallic: 0.0
+        ior: 1.333
+        transmission: 0.96
+        volume_thickness: 0.04
+        volume_attenuation_distance: 0.6
+        volume_attenuation_color:
+        - 0.8
+        - 0.92
+        - 1.0
+      mesh_surface:
+        gpu_enabled: true
+        cell_size: 0.0008100000000000001
+        max_res: 512
+        kernel_radius: 0.001515
+        iso_density: 0.57
+        density_smooth_iters: 2
+        aabb_trim_fraction: 0.0
+        min_component_tris: 32
+        laplacian_iters: 4
+        laplacian_alpha: 0.11
+        update_period: 1
+env:
+  sim_steps_per_control: 60
+  control_parts:
+  - arm
+  - eef
+  observations:
+    water_summary:
+      func: get_fluid_summary
+      mode: add
+      name: fluid/summary
+      params:
+        entity_cfg:
+          uid: water
+      extra:
+        shape:
+        - 5
+    received_volume:
+      func: get_fluid_volume_in_region
+      mode: add
+      name: fluid/received_volume
+      params:
+        entity_cfgs:
+        - uid: water
+        - uid: receiver
+        lower:
+        - -0.1
+        - -0.1
+        - 0.0045000000000000005
+        upper:
+        - 0.1
+        - 0.1
+        - 0.0675
+        sphere_center:
+        - 0
+        - 0
+        - 0.088
+        sphere_radius: 0.093
+      extra:
+        shape:
+        - 1
+  dataset: {}
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
index 8a083eabf..4a8fa4f92 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
@@ -60,7 +60,7 @@ simulation:
     - uid: cup
       shape:
         shape_type: Mesh
-        fpath: PourCupDemo/cup.glb
+        fpath: ./assets/cup.glb
         compute_uv: true
         collision:
           approximation: convex_decomposition
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
index d3991e278..17b38e068 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
@@ -31,7 +31,7 @@ simulation:
     - uid: cup
       shape:
         shape_type: Mesh
-        fpath: PourCupDemo/cup.glb
+        fpath: ./assets/cup.glb
         compute_uv: true
         collision:
           approximation: convex_decomposition
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml
new file mode 100644
index 000000000..57635c5dd
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml
@@ -0,0 +1,12 @@
+id: PourCupFluidReplay-Franka-v1
+seed: 0
+enable_sensor: false
+environment:
+  component: env.fluid.yaml
+embodiment:
+  component: ../../../../components/embodiments/franka_panda_fluid_replay.yaml
+  overrides:
+    init_rot:
+    - 0
+    - 0
+    - 180
diff --git a/embodichain_tasks/embodichain_tasks/manipulation/tableware/pour_cup.py b/embodichain_tasks/embodichain_tasks/manipulation/tableware/pour_cup.py
new file mode 100644
index 000000000..e3d81473a
--- /dev/null
+++ b/embodichain_tasks/embodichain_tasks/manipulation/tableware/pour_cup.py
@@ -0,0 +1,28 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Fluid replay deployment for Task Program-generated pouring trajectories."""
+
+from __future__ import annotations
+from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
+from embodichain.lab.gym.utils.registration import register_env
+
+__all__ = ["PourCupFluidReplayEnv"]
+
+
+@register_env("PourCupFluidReplay-Franka-v1", max_episode_steps=2000)
+class PourCupFluidReplayEnv(FluidReplayEnv):
+    """Evaluate a qualified Franka pouring trajectory with live SPH fluids."""
diff --git a/setup.py b/setup.py
index 9421ed02c..1d3e9c740 100644
--- a/setup.py
+++ b/setup.py
@@ -137,6 +137,7 @@ def main():
                 "**/*.yaml",
                 "**/*.yml",
                 "**/README.md",
+                "tasks/manipulation/tableware/pour_cup/assets/*",
             ],
         },
         cmdclass=cmdclass,
diff --git a/tests/gym/envs/test_fluid_integration.py b/tests/gym/envs/test_fluid_integration.py
new file mode 100644
index 000000000..6c80d895d
--- /dev/null
+++ b/tests/gym/envs/test_fluid_integration.py
@@ -0,0 +1,236 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Fluid Gym configuration, measurement and replay acceptance contracts."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+import numpy as np
+import pytest
+import torch
+from embodichain.lab.gym.utils.gym_utils import config_to_cfg
+from embodichain.utils.utility import load_config
+from embodichain.lab.gym.envs.managers.cfg import SceneEntityCfg
+from embodichain.lab.gym.envs.managers.observations import (
+    get_fluid_summary,
+    get_fluid_volume_in_region,
+)
+from embodichain.lab.sim.cfg import FluidObjectCfg
+
+pytestmark = pytest.mark.no_sim
+ROOT = Path(__file__).resolve().parents[3]
+TASK = (
+    ROOT
+    / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
+)
+
+
+def test_fluid_deployment_resolves_sources_and_observation_entities():
+    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    assert len(cfg.fluid_object) == 1
+    fluid = cfg.fluid_object[0]
+    assert isinstance(fluid, FluidObjectCfg)
+    assert Path(fluid.spawn.file_path).is_absolute()
+    assert fluid.spawn.sample(fluid.particle_spacing).shape == (76538, 3)
+    assert cfg.observations.received_volume.extra["shape"] == [1]
+    assert all(
+        isinstance(item, SceneEntityCfg)
+        for item in cfg.observations.received_volume.params["entity_cfgs"]
+    )
+    cup = next(item for item in cfg.background if item.uid == "cup")
+    assert Path(cup.visual_shape.fpath).is_absolute()
+    assert cup.shape.collision.force_sdf is False
+
+
+def test_fluid_component_rejects_duplicate_inline_ownership():
+    config = load_config(TASK)
+    config["fluid_object"] = []
+    with pytest.raises(ValueError, match="owns|duplicate|must not"):
+        config_to_cfg(config, source_path=TASK)
+
+
+def test_region_volume_uses_reference_pose_and_keeps_state_unchanged():
+    state = torch.tensor(
+        [
+            [[1.0, 2.0, 3.0, 0.0, 0.0, 0.0], [1.5, 2.0, 3.0, 0.0, 0.0, 0.0]],
+            [[10.0, 2.0, 3.0, 0.0, 0.0, 0.0], [10.5, 2.0, 3.0, 0.0, 0.0, 0.0]],
+        ]
+    )
+    poses = torch.tensor(
+        [[1.0, 2.0, 3.0, 0.0, 0.0, 0.0, 1.0], [1.0, 2.0, 3.0, 0.0, 0.0, 0.0, 1.0]]
+    )
+    fluid = SimpleNamespace(
+        data=SimpleNamespace(particle_state_w=state, particle_pos_w=state[..., :3]),
+        cfg=SimpleNamespace(particle_spacing=0.1),
+        particle_mass=0.001,
+        num_particles=2,
+        nominal_volume=0.002,
+    )
+    rigid = SimpleNamespace(get_local_pose=lambda: poses)
+    env = SimpleNamespace(
+        num_envs=2,
+        device=torch.device("cpu"),
+        sim=SimpleNamespace(
+            get_fluid_object=lambda _: fluid,
+            get_rigid_object=lambda _: rigid,
+            arena_offsets=torch.tensor([[0.0, 0, 0], [9.0, 0, 0]]),
+        ),
+    )
+    before = state.clone()
+    volume = get_fluid_volume_in_region(
+        env,
+        {},
+        [SceneEntityCfg(uid="water"), SceneEntityCfg(uid="receiver")],
+        [-0.1] * 3,
+        [0.1] * 3,
+    )
+    torch.testing.assert_close(volume, torch.full((2, 1), 0.001))
+    torch.testing.assert_close(
+        get_fluid_summary(env, {}, SceneEntityCfg(uid="water"))[:, 4], torch.ones(2)
+    )
+    torch.testing.assert_close(state, before)
+
+
+def test_fluid_replay_rejects_unqualified_recording_before_world_creation():
+    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
+    from tensordict import TensorDict
+
+    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    source = {
+        "meta": {"num_envs": 1, "num_steps": 2, "lengths": [2]},
+        "states": TensorDict({}, batch_size=[1, 2]),
+        "actions": torch.zeros(1, 2, 9),
+    }
+    with pytest.raises(ValueError, match="Task Program"):
+        FluidReplayEnv(cfg, trajectory=source)
+
+
+def _qualified_source(root_position=(0.0, 0.0, 0.0)):
+    from tensordict import TensorDict
+
+    root = torch.tensor([*root_position, 0.0, 0.0, 0.0, 1.0]).repeat(1, 2, 1)
+    cup = torch.tensor([-0.75, 0.0, 0.552, 0.0, 0.0, 0.0, 1.0]).repeat(1, 2, 1)
+    return {
+        "meta": {
+            "num_envs": 1,
+            "num_steps": 2,
+            "lengths": [2],
+            "dt": 0.05,
+            "rigid_object_uids": ["cup"],
+            "fluid_source": {
+                "kind": "task_program",
+                "accepted": True,
+                "program_hash": "test-program",
+            },
+        },
+        "states": TensorDict(
+            {
+                "robot": TensorDict(
+                    {"qpos": torch.zeros(1, 2, 9), "root_pose": root}, batch_size=[1, 2]
+                ),
+                "rigid_objects": TensorDict(
+                    {"cup": TensorDict({"pose": cup}, batch_size=[1, 2])},
+                    batch_size=[1, 2],
+                ),
+            },
+            batch_size=[1, 2],
+        ),
+        "actions": torch.zeros(1, 2, 9),
+    }
+
+
+def test_replay_adopts_source_root_before_scene_creation(monkeypatch):
+    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
+    from embodichain.lab.gym.envs.embodied_env import EmbodiedEnv
+
+    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    cfg.dataset = {}  # Empty mapping is a supported Python configuration.
+    captured = []
+
+    def initialize(self, cfg, **kwargs):
+        captured.append(cfg)
+        self.robot = SimpleNamespace(dof=9)
+        self.sim = SimpleNamespace(physics=SimpleNamespace(solver_type="sph"))
+
+    monkeypatch.setattr(EmbodiedEnv, "__init__", initialize)
+    env = FluidReplayEnv(cfg, trajectory=_qualified_source((1.0, 2.0, 3.0)))
+    assert len(env._replay_time) == cfg.num_envs == 2
+    np.testing.assert_array_equal(captured[0].robot.init_local_pose[:3, 3], [1, 2, 3])
+
+
+@pytest.mark.parametrize("kind", ["trajectory", "actions", "dataset"])
+def test_replay_rejects_misleading_policy_recording(kind):
+    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
+
+    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    if kind == "trajectory":
+        cfg.record_trajectory = True
+    elif kind == "actions":
+        cfg.actions = SimpleNamespace(control="unused")
+    else:
+        cfg.dataset = SimpleNamespace(recorder="unused")
+    with pytest.raises(ValueError, match="recording"):
+        FluidReplayEnv(cfg, trajectory=_qualified_source())
+
+
+def test_replay_rejects_moving_root_before_scene_creation():
+    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
+
+    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    source = _qualified_source()
+    source["states"]["robot", "root_pose"][0, 1, 0] = 0.2
+    with pytest.raises(ValueError, match="fixed-root"):
+        FluidReplayEnv(cfg, trajectory=source)
+
+
+def test_replay_exhaustion_requires_reset():
+    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
+
+    env = object.__new__(FluidReplayEnv)
+    env._replay_time = np.array([2.0, 2.1])
+    env.replay_duration = 2.0
+    with pytest.raises(RuntimeError, match="reset"):
+        env.step(None)
+
+
+def test_refill_transforms_selected_container_pose_into_world_frame():
+    from embodichain.lab.gym.envs.managers.events import reset_fluid_in_container
+
+    recorded = []
+    fluid = SimpleNamespace(
+        cfg=FluidObjectCfg(uid="water", init_pos=(0, 0, 0.1)),
+        reset_to_pose=lambda pose, ids: recorded.append((pose, ids)),
+    )
+    poses = torch.eye(4).repeat(2, 1, 1)
+    poses[:, :3, 3] = torch.tensor([1.0, 2.0, 3.0])
+    env = SimpleNamespace(
+        device=torch.device("cpu"),
+        sim=SimpleNamespace(
+            get_fluid_object=lambda _: fluid,
+            get_rigid_object=lambda _: SimpleNamespace(
+                get_local_pose=lambda **_: poses
+            ),
+            arena_offsets=torch.tensor([[0.0, 0, 0], [9.0, 0, 0]]),
+        ),
+    )
+    reset_fluid_in_container(
+        env, [1], [SceneEntityCfg(uid="water"), SceneEntityCfg(uid="cup")]
+    )
+    pose, ids = recorded[0]
+    torch.testing.assert_close(pose[:, :3], torch.tensor([[10.0, 2.0, 3.1]]))
+    assert ids.tolist() == [1]
diff --git a/tests/gym/envs/test_fluid_replay_runtime.py b/tests/gym/envs/test_fluid_replay_runtime.py
new file mode 100644
index 000000000..bf0a88bdf
--- /dev/null
+++ b/tests/gym/envs/test_fluid_replay_runtime.py
@@ -0,0 +1,154 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Live Gym reset and exhaustion with a small synthetic source recording."""
+
+from __future__ import annotations
+
+from pathlib import Path
+import subprocess
+import sys
+import numpy as np
+import pytest
+import torch
+from tensordict import TensorDict
+from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
+from embodichain.lab.gym.utils.gym_utils import config_to_cfg
+from embodichain.lab.sim import SimulationManager
+from embodichain.lab.sim.cfg import FluidSpawnCfg, FluidRenderCfg, RenderCfg
+from embodichain.utils.utility import load_config
+
+pytestmark = [pytest.mark.gpu, pytest.mark.subprocess_sim]
+
+
+class _FluidReplayRuntime:
+    def setup_method(self):
+        from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
+
+        if not getattr(ensure_solver_available("sph"), "supports_world_reset", False):
+            pytest.skip("requires world-isolated SPH build")
+        task = (
+            Path(__file__).resolve().parents[3]
+            / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
+        )
+        cfg = config_to_cfg(load_config(task), source_path=task)
+        cfg.sim_cfg.render_cfg = RenderCfg(renderer="no-render")
+        cfg.sim_cfg.headless = True
+        cfg.enable_sensor = False
+        cfg.sim_steps_per_control = 1
+        cfg.sim_cfg.physics_dt = 0.001
+        solver = cfg.sim_cfg.physics_cfg.solver_cfg
+        solver.update(
+            volume_map_boundary_enabled=False,
+            sdf_boundary_enabled=False,
+            particle_support_radius=0.0225,
+        )
+        for obj in cfg.background:
+            obj.shape.collision.sdf_resolution = 16
+        fluid = cfg.fluid_object[0]
+        fluid.particle_spacing = 0.01
+        fluid.particle_radius = 0.003
+        fluid.spawn = FluidSpawnCfg(
+            kind="box", size=(0.02, 0.02, 0.02), reference_uid="cup"
+        )
+        fluid.render = FluidRenderCfg(mode="none")
+        root = torch.tensor([0, 0, 0, 0, 0, 0, 1.0]).repeat(1, 2, 1)
+        cup = torch.tensor([-0.75, 0, 0.552, 0, 0, 0, 1.0]).repeat(1, 2, 1)
+        qpos = torch.tensor(cfg.robot.init_qpos).reshape(1, 1, -1).repeat(1, 2, 1)
+        # Synthetic provenance exercises the replay contract, not source acceptance.
+        source = {
+            "meta": {
+                "num_envs": 1,
+                "num_steps": 2,
+                "lengths": [2],
+                "dt": 0.004,
+                "rigid_object_uids": ["cup"],
+                "fluid_source": {
+                    "kind": "task_program",
+                    "accepted": True,
+                    "program_hash": "synthetic-test",
+                },
+            },
+            "states": TensorDict(
+                {
+                    "robot": {"qpos": qpos, "root_pose": root},
+                    "rigid_objects": {"cup": {"pose": cup}},
+                },
+                batch_size=[1, 2],
+            ),
+            "actions": torch.zeros(1, 2, qpos.shape[-1]),
+        }
+        self.env = FluidReplayEnv(cfg, trajectory=source, speed=1.0, hold_seconds=0.0)
+
+    def teardown_method(self):
+        if hasattr(self, "env"):
+            self.env.close(exit_process=False)
+            del self.env
+        SimulationManager.flush_cleanup_queue()
+
+    def test_partial_reset_preserves_other_world_then_finishes_independently(self):
+        env = self.env
+        env.reset(seed=0)
+        action = torch.zeros(
+            (env.num_envs, len(env.active_joint_ids)), device=env.device
+        )
+        env.step(action)
+        env.step(action)
+        before = env.sim.get_fluid_object("water").data.particle_state_w.clone()
+        env.reset(
+            options={
+                "reset_ids": torch.tensor([0], device=env.device),
+                "save_data": False,
+            }
+        )
+        torch.testing.assert_close(
+            env.sim.get_fluid_object("water").data.particle_state_w[1],
+            before[1],
+            rtol=0,
+            atol=0,
+        )
+        np.testing.assert_allclose(env._replay_time, [0, 0.002])
+        env.step(action)
+        _, _, _, truncated, info = env.step(action)
+        assert info["fluid_replay_finished"].tolist() == [False, True]
+        assert truncated.tolist() == [False, True]
+        env.step(action)
+        env.step(action)
+        with pytest.raises(RuntimeError, match="exhausted"):
+            env.step(action)
+
+
+def test_fluid_replay_reset_and_exhaustion_in_fresh_process():
+    from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
+
+    if not getattr(ensure_solver_available("sph"), "supports_world_reset", False):
+        pytest.skip("requires world-isolated SPH build")
+    result = subprocess.run(
+        [sys.executable, str(Path(__file__).resolve())],
+        capture_output=True,
+        text=True,
+        timeout=180,
+    )
+    assert result.returncode == 0, result.stdout + result.stderr
+
+
+if __name__ == "__main__":
+    worker = _FluidReplayRuntime()
+    try:
+        worker.setup_method()
+        worker.test_partial_reset_preserves_other_world_then_finishes_independently()
+    finally:
+        worker.teardown_method()
diff --git a/tests/sim/objects/test_fluid_batch_runtime.py b/tests/sim/objects/test_fluid_batch_runtime.py
new file mode 100644
index 000000000..99c6ea2d2
--- /dev/null
+++ b/tests/sim/objects/test_fluid_batch_runtime.py
@@ -0,0 +1,125 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Batch fluid declarations and row-local reset through the public manager."""
+
+from __future__ import annotations
+
+from pathlib import Path
+import subprocess
+import sys
+import pytest
+import torch
+from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
+from embodichain.lab.sim.cfg import (
+    FluidObjectCfg,
+    FluidSpawnCfg,
+    FluidRenderCfg,
+    NewtonPhysicsCfg,
+    RenderCfg,
+)
+
+pytestmark = [pytest.mark.gpu, pytest.mark.subprocess_sim]
+
+
+def _run_fluid_partial_reset():
+    from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
+
+    if not getattr(ensure_solver_available("sph"), "supports_world_reset", False):
+        pytest.skip("requires world-isolated SPH build")
+    sim = SimulationManager(
+        SimulationManagerCfg(
+            headless=True,
+            device="cuda",
+            num_envs=2,
+            arena_space=1,
+            physics_dt=0.001,
+            render_cfg=RenderCfg(renderer="no-render"),
+            physics_cfg=NewtonPhysicsCfg(
+                num_substeps=1,
+                collision_cfg=None,
+                solver_cfg={
+                    "solver_type": "sph",
+                    "sph_method": "dfsph",
+                    "volume_map_boundary_enabled": False,
+                },
+            ),
+        )
+    )
+    fluid = None
+    try:
+        fluid = sim.add_fluid_object(
+            FluidObjectCfg(
+                uid="water",
+                particle_spacing=0.01,
+                init_pos=(0.0, 0.0, 1.0),
+                spawn=FluidSpawnCfg(kind="box", size=(0.04,) * 3),
+                render=FluidRenderCfg(mode="none"),
+            )
+        )
+        sim.prepare()
+        initial = fluid.data.default_particle_state_w
+        offsets = torch.as_tensor(
+            sim.arena_offsets, device=sim.device, dtype=torch.float32
+        )
+        torch.testing.assert_close(
+            initial[:, :, :3].mean(1),
+            offsets + torch.tensor([0.0, 0.0, 1.0], device=sim.device),
+        )
+        changed = initial[[1]].clone()
+        changed[..., 3] = 1
+        fluid.write_particle_state_to_sim(changed, [1])
+        sim.update(step=3, render_final_step=False)
+        before = fluid.data.particle_state_w
+        density = sim.get_fluid_diagnostics("water")["particle_density"]
+        fluid.reset([1])
+        torch.testing.assert_close(
+            fluid.data.particle_state_w[0], before[0], rtol=0, atol=0
+        )
+        torch.testing.assert_close(
+            fluid.data.particle_state_w[1], initial[1], rtol=0, atol=0
+        )
+        torch.testing.assert_close(
+            sim.get_fluid_diagnostics("water")["particle_density"][0],
+            density[0],
+            rtol=0,
+            atol=0,
+        )
+        assert not sim.get_fluid_diagnostics("water")["particle_density"][1].any()
+        sim.update(step=1, render_final_step=False)
+        assert torch.isfinite(fluid.data.particle_state_w).all()
+    finally:
+        fluid = None
+        sim.destroy(exit_process=False)
+        SimulationManager.flush_cleanup_queue()
+
+
+def test_fluid_partial_reset_preserves_other_world():
+    from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
+
+    if not getattr(ensure_solver_available("sph"), "supports_world_reset", False):
+        pytest.skip("requires world-isolated SPH build")
+    result = subprocess.run(
+        [sys.executable, str(Path(__file__).resolve())],
+        capture_output=True,
+        text=True,
+        timeout=120,
+    )
+    assert result.returncode == 0, result.stdout + result.stderr
+
+
+if __name__ == "__main__":
+    _run_fluid_partial_reset()
diff --git a/tests/sim/objects/test_fluid_object.py b/tests/sim/objects/test_fluid_object.py
index 317f3c808..d5b34c43b 100644
--- a/tests/sim/objects/test_fluid_object.py
+++ b/tests/sim/objects/test_fluid_object.py
@@ -57,7 +57,7 @@ def fluid():
         points,
         np.eye(4, dtype=np.float32),
         torch.device("cpu"),
-        on_write=lambda: None,
+        on_write=lambda ids: None,
     )
     result._initialize_spawn_declaration(2)
     result.attach_spawn_handles(
@@ -124,7 +124,7 @@ def test_integer_initial_velocity_is_normalized():
         points,
         np.eye(4, dtype=np.float32),
         torch.device("cpu"),
-        on_write=lambda: None,
+        on_write=lambda ids: None,
     )
     fluid._initialize_spawn_declaration(1)
     fluid.attach_spawn_handles([SimpleNamespace(index=0, particle_count=1)])
@@ -140,7 +140,7 @@ def test_fluid_write_respects_render_frame_cache_scope(inside_frame):
 
     calls = []
     sim = SimpleNamespace(
-        physics=SimpleNamespace(invalidate_fluid_state=lambda: None),
+        physics=SimpleNamespace(invalidate_fluid_state=lambda **kwargs: None),
         _render_state_published=True if inside_frame else None,
         sync_render_state=lambda: calls.append("publish"),
     )
diff --git a/tests/test_task_program_package_data.py b/tests/test_task_program_package_data.py
index 57a790e60..28a350d21 100644
--- a/tests/test_task_program_package_data.py
+++ b/tests/test_task_program_package_data.py
@@ -98,6 +98,19 @@
 }
 _RESOURCE_PATHS = frozenset(
     {
+        *(
+            Path("tasks/manipulation/tableware/pour_cup/assets") / name
+            for name in (
+                "cup.glb",
+                "fluid_floor.obj",
+                "fluid_table.obj",
+                "mug_wall.obj",
+                "receiver_wall.obj",
+                "water_fill.npz",
+            )
+        ),
+        Path("tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"),
+        Path("tasks/manipulation/tableware/pour_cup/env.fluid.yaml"),
         *_PROGRAMS,
         *_DEPLOYMENTS,
         Path("tasks/manipulation/repeated_pick_place/catalog.yaml"),
@@ -233,6 +246,7 @@ def test_setup_stages_all_official_task_programs(
         "**/*.yaml",
         "**/*.yml",
         "**/README.md",
+        "tasks/manipulation/tableware/pour_cup/assets/*",
     ]
     expected_outputs = {
         Path("embodichain_tasks") / "configs" / relative_path

From 952575bcff37b016f8f8f2ac3b288f5858b89a99 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Wed, 14 Oct 2026 02:17:19 +0800
Subject: [PATCH 04/19] docs(fluid): record Gym qualification and finalize API
 entries

---
 IMPLEMENTATION_PLAN.md                                   | 9 ++++++++-
 .../api_reference/embodichain/embodichain.lab.gym.rst    | 2 +-
 docs/source/api_reference/public_api.rst                 | 6 +++---
 3 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
index d83b5c4f4..d0d0c05dc 100644
--- a/IMPLEMENTATION_PLAN.md
+++ b/IMPLEMENTATION_PLAN.md
@@ -155,11 +155,18 @@ the shared installed DexSim build is not modified.
 - Focused EC config, objects, packaging, context and docs checker tests:
   **259 passed**. Later batch-count regression and refill reuse: **11 passed**.
   Fresh-process live Gym independent completion/reset and Sim selected reset:
-  **2 passed**. Final coarse two-row CLI partial-reset smoke also passed.
+  **2 passed**. Final coarse two-row CLI partial-reset and single-row override
+  smoke checks also passed.
 - Independent reviews corrected wheel asset omission, root pose adoption,
   misleading action recording, dataset mapping compatibility and canonical
   global reset masks. Real Gym tests additionally caught pre-resolution sim
   versus env counts; both replay and CLI now use `EnvCfg.num_envs`.
+- Sphinx dummy build succeeded. The full run reported 811 warnings versus
+  the earlier 800: one new duplicate fluid field entry was corrected, and
+  ten additional warnings concern optional Pinocchio/type imports in the
+  isolated environment. The post-fix incremental build also succeeded, with
+  1123 warnings from repeated existing API indexing/type resolution and no
+  fluid-specific warning entries. This is not a warning-free documentation build.
 - API coverage **2503/2503**; source Task Program inspector passed all three
   calls. A built EC wheel contains all six pour-cup assets. Native validation
   uses staged JIT source, not a rebuilt release/AOT wheel.
diff --git a/docs/source/api_reference/embodichain/embodichain.lab.gym.rst b/docs/source/api_reference/embodichain/embodichain.lab.gym.rst
index 9d2ceb04f..cb372c9dc 100644
--- a/docs/source/api_reference/embodichain/embodichain.lab.gym.rst
+++ b/docs/source/api_reference/embodichain/embodichain.lab.gym.rst
@@ -64,7 +64,7 @@ Embodied Environment Classes
 
 .. autoclass:: EmbodiedEnvCfg
     :members:
-    :exclude-members: __init__, class_type
+    :exclude-members: __init__, class_type, fluid_object
 
     Configuration class for embodied environments with extended settings for lighting, observation management,
     and advanced simulation features.
diff --git a/docs/source/api_reference/public_api.rst b/docs/source/api_reference/public_api.rst
index 5add03d88..b8c0196aa 100644
--- a/docs/source/api_reference/public_api.rst
+++ b/docs/source/api_reference/public_api.rst
@@ -388,7 +388,7 @@ embodichain.lab.task_program.integrations.simulation.handover
    ConfiguredHandOverPoseProvider
 
 embodichain.lab.gym.envs.fluid_replay
------------------------------------
+-------------------------------------
 
 .. currentmodule:: embodichain.lab.gym.envs.fluid_replay
 
@@ -623,7 +623,7 @@ embodichain.lab.scripts.run_env
    preview
 
 embodichain.lab.scripts.run_fluid_task_program
---------------------------------------------
+----------------------------------------------
 
 .. currentmodule:: embodichain.lab.scripts.run_fluid_task_program
 
@@ -2519,7 +2519,7 @@ kernel policy; callers log and apply that seed to their own generators.
 
 
 embodichain_tasks.manipulation.tableware.pour_cup
-----------------------------------------------
+-------------------------------------------------
 
 .. currentmodule:: embodichain_tasks.manipulation.tableware.pour_cup
 

From d5b93bcf887c58e24bf1bb5b803afc9b1bce0a5d Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Wed, 14 Oct 2026 14:09:29 +0800
Subject: [PATCH 05/19] feat(gym): record fluid replay camera views per
 environment

Use Hybrid by default with FastRT support, and route replay videos through the configured record_camera event. Add separate environment buffers, selected save/discard and configurable cadence to record_camera_data. Explicitly commit the final replay recording. Qualified two complete 744-frame Hybrid videos, both renderer smoke runs, row-local recording reset, 128 focused tests and the live Gym reset regression.
---
 IMPLEMENTATION_PLAN.md                        |  38 ++++++
 .../topics/env-framework/fluid-replay.md      |  12 ++
 .../topics/manager-functor/manager-functor.md |  15 +++
 embodichain/lab/gym/envs/managers/record.py   | 107 +++++++++++++---
 .../lab/scripts/run_fluid_task_program.py     |  41 +++---
 .../manipulation/tableware/pour_cup/README.md |  10 +-
 .../tableware/pour_cup/env.fluid.yaml         |  19 +++
 tests/gym/envs/managers/test_record.py        | 102 +++++++++++++++
 tests/gym/envs/test_fluid_integration.py      |   4 +
 tests/gym/envs/test_fluid_replay_runtime.py   |   1 +
 .../scripts/test_run_fluid_task_program.py    | 121 ++++++++++++++++++
 11 files changed, 428 insertions(+), 42 deletions(-)
 create mode 100644 tests/lab/scripts/test_run_fluid_task_program.py

diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
index d0d0c05dc..945b09a36 100644
--- a/IMPLEMENTATION_PLAN.md
+++ b/IMPLEMENTATION_PLAN.md
@@ -196,3 +196,41 @@ Python at `/home/dex/.codex/worktrees/fluid-sph-worlds/venv/bin/python`, with
 embodichain.lab.scripts.run_fluid_task_program --num-envs 2` runs both phases.
 Old pre-recorded tutorial trajectories lack qualification metadata and are
 intentionally rejected by the Gym adapter.
+
+
+## Rendering follow-up: Hybrid / FastRT and record cameras
+
+User requested Hybrid or FastRT rendering and record-camera views for every
+environment. The fluid deployment now owns a `record_camera` interval event
+using `record_camera_data(per_env=True, fps=None)`. The runner defaults to
+Hybrid, supports FastRT, redirects recorder output to `<output>/videos`, and
+commits the final recording explicitly before close. Independent recorder
+buffers/counters preserve unselected rows during partial reset. Capture renders
+the current camera group before fetching frames, since interval events precede
+observation rendering. The previous script-owned, environment-zero ImageIO
+writer is removed.
+
+Validation:
+- Focused recorder, finalization, launcher/config and context tests: **128 passed**.
+- Live Gym row-reset/exhaustion regression: **1 passed**.
+- Hybrid and FastRT short camera runs both generated two valid 640x480,
+  24-fps videos. Full Hybrid replay generated **two separate 744-frame,
+  31-second videos**. Every frame decoded; both arena views were inspected at
+  lift, pour and return. Source acceptance and final fluid statistics are
+  unchanged: received **141.98288 / 140.88261 mL**, all sampled states finite.
+- Full Hybrid run wall time: **908.71 s**, including final video persistence.
+  This run is functional qualification, not a rendering performance benchmark.
+- Live partial-reset recording smoke: after discarding row 0's first two
+  frames, row 0 saved one new frame while row 1 retained all three frames.
+  The unchanged row's particle state also passed the exact preservation check.
+- API coverage **2503/2503**, Black 26.3.1, syntax/headers, context and diff checks
+  passed. General recording ownership was revised in manager-functor context;
+  fluid deployment usage was revised in env-framework context. Existing render
+  lifecycle guidance remains accurate because capture uses its public group API.
+
+Evidence: `outputs/fluid-gym-hybrid-record/videos/episode_0_fluid_gym_env0.mp4`,
+`episode_0_fluid_gym_env1.mp4` in the same directory, and
+`outputs/fluid-gym-hybrid-record/record-camera-validation.json` /
+`record-camera-contact-sheet.jpg`. Backend smoke evidence is in
+`outputs/fluid-record-fastrt-smoke/`; row-selection evidence is in
+`outputs/fluid-record-partial-smoke/`.
diff --git a/agent_context/topics/env-framework/fluid-replay.md b/agent_context/topics/env-framework/fluid-replay.md
index 2a13d87a2..85444ed22 100644
--- a/agent_context/topics/env-framework/fluid-replay.md
+++ b/agent_context/topics/env-framework/fluid-replay.md
@@ -32,6 +32,18 @@ forces do not affect the recorded motion. Action managers, trajectory recording
 and active policy dataset recorders are rejected. Task Program acceptance is
 source provenance; fluid capture metrics remain separate measured outcomes.
 
+## Rendering and recording
+
+The pouring environment selects `hybrid`; the runner also accepts `fast-rt`.
+Its interval `record_camera` event uses the existing `record_camera_data`
+functor with `per_env=True` and `fps=None`, recording every environment view at
+control frequency. The runner only redirects the output directory and sets
+the recording limit to the resolved environment count. It explicitly commits
+at normal completion before close, which otherwise discards pending frames.
+Selected-row recording and camera consumption are owned by the
+[recording functor](../manager-functor/manager-functor.md#camera-recording).
+NoRender qualification removes the recorder before constructing the scene.
+
 Production configuration/measurement checks are in
 `tests/gym/envs/test_fluid_integration.py`; live isolation checks are in
 `tests/sim/objects/test_fluid_batch_runtime.py`. Generated trajectories/videos
diff --git a/agent_context/topics/manager-functor/manager-functor.md b/agent_context/topics/manager-functor/manager-functor.md
index a17474895..8d6895fac 100644
--- a/agent_context/topics/manager-functor/manager-functor.md
+++ b/agent_context/topics/manager-functor/manager-functor.md
@@ -9,6 +9,7 @@
 | Observation manager + built-in functors | `managers/observation_manager.py`, `managers/observations.py` |
 | Reward manager + built-in functors | `managers/reward_manager.py`, `managers/rewards.py` |
 | Event manager + built-in functors | `managers/event_manager.py`, `managers/events.py` |
+| Episode camera recording events | `managers/record.py` |
 | Action manager + built-in terms | `managers/action_manager.py`, `managers/actions.py` |
 | Dataset manager + built-in recorders | `managers/dataset_manager.py`, `managers/datasets.py`, `managers/async_datasets.py` |
 | Randomization functors (event sub-type) | `managers/randomization/` (spatial, visual, physics, geometry) |
@@ -106,6 +107,20 @@ Event and observation calls have per-functor profiling through
 `ManagerBase._call_functor()`; reward/action/dataset calls do not yet use that
 helper. See [profiling](../env-framework/profiling.md).
 
+### Camera recording
+
+`record_camera_data` is an interval event, independent of policy dataset
+recording. It renders its camera group before reading the buffers because
+interval events precede observation rendering. Views use arena-relative camera
+extrinsics. `per_env=True` retains separate owned CPU frames and episode counters;
+selected save/discard only touches those rows. The default remains a tiled video.
+`fps=None` follows control frequency for an every-step recording event.
+
+Reset commits or discards frames via `save_and_clear` / `discard_and_clear`.
+Close discards uncommitted frames, so a host ending without reset must explicitly
+commit its last episode. Source and regression tests live in `record.py` and
+`tests/gym/envs/managers/test_record.py`.
+
 ## Change sites and validation
 
 Change the selected manager's dispatch for scheduling changes; change its
diff --git a/embodichain/lab/gym/envs/managers/record.py b/embodichain/lab/gym/envs/managers/record.py
index 6f89c79b6..e166519bc 100644
--- a/embodichain/lab/gym/envs/managers/record.py
+++ b/embodichain/lab/gym/envs/managers/record.py
@@ -17,6 +17,7 @@
 from __future__ import annotations
 
 import os
+import math
 import random
 import threading
 from collections import deque
@@ -36,23 +37,19 @@
 
 
 class record_camera_data(Functor):
-    """Record camera data in the environment. The camera is usually setup with third-person view, and
-    is used to record the scene during the episode. It is helpful for debugging and visualization.
-
-    Note:
-        Currently, the functor is implemented in `interval' mode such that, it can only save the
-        recorded frames when in :meth:`env.step()` function call. For example:
-        ```python
-        env.step()
-        # perform multiple steps in the same episode
-        env.reset()
-        env.step()  # the video of the first episode will be saved here.
-        ```
-        The final episode frames will not be saved in the current implementation.
-        We may improve it in the future.
+    """Capture a third-person camera through an interval event.
+
+    By default, views are tiled into one video at 20 fps. Set ``per_env=True``
+    to save one video per environment, with independent episode counters and
+    selected-row save/discard. ``fps=None`` uses the environment's control
+    frequency; use an explicit fps when capturing less often than every step.
+
+    Frames are committed by :meth:`save_and_clear` before reset. Closing the
+    environment discards uncommitted frames, so callers ending without reset
+    must explicitly commit the last recording.
     """
 
-    def __init__(self, cfg: FunctorCfg, env: EmbodiedEnv):
+    def __init__(self, cfg: FunctorCfg, env: EmbodiedEnv) -> None:
         """Initialize the functor.
 
         Args:
@@ -64,6 +61,14 @@ def __init__(self, cfg: FunctorCfg, env: EmbodiedEnv):
         """
         super().__init__(cfg, env)
 
+        self._per_env = cfg.params.get("per_env", False)
+        fps = cfg.params.get("fps", 20.0)
+        self._fps = float(env.control_frequency if fps is None else fps)
+        if not math.isfinite(self._fps) or self._fps <= 0:
+            raise ValueError("Recording fps must be finite and positive.")
+        self._env_frames: dict[int, list[np.ndarray]] = {}
+        self._env_episode_ids: dict[int, int] = {}
+
         # extract the used quantities (to enable type-hinting)
         self._name = cfg.params.get("name", "default")
         resolution = cfg.params.get("resolution", (640, 480))
@@ -144,19 +149,58 @@ def save_and_clear(self, env_ids: Union[torch.Tensor, None] = None) -> None:
         frames are saved before the episode is reset. This avoids the issue where the
         final episode's frames are lost because the save previously relied on detecting
         a reset inside :meth:`__call__`.
+
+        Args:
+            env_ids: Selected rows for separate videos, or all rows when None.
+                Tiled recording saves the whole shared frame buffer.
         """
         self._ensure_open()
+        if getattr(self, "_per_env", False):
+            for env_id in self._selected_recorded_envs(env_ids):
+                frames = self._env_frames[env_id]
+                if not frames:
+                    continue
+                episode = self._env_episode_ids.get(env_id, 0)
+                images_to_video(
+                    frames,
+                    self._save_path,
+                    f"episode_{episode}_{self._name}_env{env_id}",
+                    fps=self._fps,
+                )
+                self._env_episode_ids[env_id] = episode + 1
+                self._env_frames[env_id] = []
+            return
         if len(self._frames) > 0:
             video_name = f"episode_{self._current_episode}_{self._name}"
-            images_to_video(self._frames, self._save_path, video_name, fps=20)
+            images_to_video(
+                self._frames, self._save_path, video_name, fps=getattr(self, "_fps", 20)
+            )
 
             self._current_episode += 1
             self._frames = []
 
     def discard_and_clear(self, env_ids: Union[torch.Tensor, None] = None) -> None:
-        """Discard recorded frames without creating an episode video."""
+        """Discard frames without creating an episode video.
+
+        Args:
+            env_ids: Selected rows for separate videos, or all rows when None.
+                Tiled recording discards the whole shared frame buffer.
+        """
+        if getattr(self, "_per_env", False):
+            for env_id in self._selected_recorded_envs(env_ids):
+                self._env_frames[env_id] = []
+            return
         self._frames = []
 
+    def _selected_recorded_envs(self, env_ids: torch.Tensor | None) -> list[int]:
+        if env_ids is None:
+            return list(self._env_frames)
+        return [
+            int(env_id)
+            for env_id in torch.as_tensor(env_ids).reshape(-1).cpu().tolist()
+            if int(env_id) in self._env_frames
+        ]
+
     def finalize(self) -> None:
         """Discard uncommitted frames and close the recorder exactly once."""
         if not hasattr(self, "_finalize_lock"):
@@ -188,14 +232,39 @@ def __call__(
         ),
         max_env_num: int = 16,
         save_path: str = "./outputs/videos",
-    ):
+        per_env: bool = False,
+        fps: float | None = 20.0,
+    ) -> None:
+        """Capture up to ``max_env_num`` arena-relative views at this step.
+
+        Args:
+            env: Owning environment.
+            env_ids: Event rows; all camera rows are captured at a global interval.
+            name: Camera name, configured at initialization.
+            resolution: Image width and height, configured at initialization.
+            eye: Arena-relative camera position.
+            target: Arena-relative look-at position.
+            up: Camera up direction.
+            intrinsics: Pinhole intrinsics.
+            max_env_num: Maximum number of environment views to capture.
+            save_path: Output directory, configured at initialization.
+            per_env: Whether to save separate environment videos.
+            fps: Video cadence, or None for the environment control frequency.
+        """
         self._ensure_open()
+        # Interval events run before observation rendering. Render this group
+        # from the current physics state before reading its reusable buffers.
+        env.sim.render_camera_group([self.camera.group_id])
         self.camera.update(fetch_only=True)
         data = self.camera.get_data()
         rgb = data["color"]
 
-        num_frames = max(rgb.shape[0], max_env_num)
+        num_frames = min(rgb.shape[0], max_env_num)
         rgb = rgb[:num_frames]
+        if self._per_env:
+            for env_id, frame in enumerate(rgb[..., :3].detach().cpu().numpy()):
+                self._env_frames.setdefault(env_id, []).append(frame.copy())
+            return
         rgb = self._draw_frames_into_one_image(rgb)[..., :3].cpu().numpy()
         self._frames.append(rgb)
 
diff --git a/embodichain/lab/scripts/run_fluid_task_program.py b/embodichain/lab/scripts/run_fluid_task_program.py
index 15b8e5492..cfa44b6d4 100644
--- a/embodichain/lab/scripts/run_fluid_task_program.py
+++ b/embodichain/lab/scripts/run_fluid_task_program.py
@@ -106,7 +106,12 @@ def _replay(args, trajectory: Path, output: Path) -> None:
     cfg.num_envs = args.num_envs
     cfg.sim_cfg.headless = True
     cfg.sim_cfg.render_cfg = RenderCfg(renderer=args.renderer)
-    cfg.enable_sensor = args.renderer != "no-render"
+    if args.renderer == "no-render":
+        cfg.enable_sensor = False
+        cfg.events.record_camera = None
+    else:
+        cfg.events.record_camera.params["save_path"] = str(output / "videos")
+        cfg.events.record_camera.params["max_env_num"] = cfg.num_envs
     if args.particle_spacing is not None:
         # Qualification smoke mode uses a coarser initial fill, explicitly recorded.
         fluid = cfg.fluid_object[0]
@@ -121,18 +126,8 @@ def _replay(args, trajectory: Path, output: Path) -> None:
             2.25 * args.particle_spacing
         )
     env = FluidReplayEnv(cfg, trajectory=str(trajectory), speed=args.speed)
-    writer = None
     try:
         obs, info = env.reset(seed=0)
-        if cfg.enable_sensor:
-            import imageio.v2 as imageio
-
-            writer = imageio.get_writer(
-                output / "fluid_gym.mp4",
-                fps=round(1 / env.step_dt),
-                codec="libx264",
-                quality=8,
-            )
         duration = (
             env.replay_duration
             if args.seconds is None
@@ -162,13 +157,6 @@ def _replay(args, trajectory: Path, output: Path) -> None:
                     rtol=0,
                     atol=0,
                 )
-            if writer is not None:
-                writer.append_data(
-                    env.sensors["overview"]
-                    .get_data()["color"][0, :, :, :3]
-                    .cpu()
-                    .numpy()
-                )
             if index % max(1, round(1 / env.step_dt)) == 0 or index == steps - 1:
                 from embodichain.lab.gym.envs.managers.observations import (
                     get_fluid_summary,
@@ -197,10 +185,23 @@ def _replay(args, trajectory: Path, output: Path) -> None:
                 }
                 samples.append(sample)
                 print(json.dumps(sample), flush=True)
+        videos = []
+        if args.renderer != "no-render":
+            # Closing discards uncommitted recordings. Commit the last episode
+            # explicitly, including short qualification runs without a reset.
+            env.event_manager.get_functor("record_camera").save_and_clear()
+            name = cfg.events.record_camera.params["name"]
+            videos = [
+                str(Path("videos") / f"episode_0_{name}_env{env_id}.mp4")
+                for env_id in range(env.num_envs)
+            ]
         report = {
             "source": env.source_qualification,
             "coupling": "one-way measured Task Program state replay",
             "num_envs": env.num_envs,
+            "renderer": args.renderer,
+            "videos": videos,
+            "video_fps": env.control_frequency if videos else None,
             "particle_count_per_env": env.sim.get_fluid_object("water").num_particles,
             "partial_reset_step": args.partial_reset_step,
             "replay_complete": info["fluid_replay_finished"].cpu().tolist(),
@@ -211,8 +212,6 @@ def _replay(args, trajectory: Path, output: Path) -> None:
             json.dumps(report, indent=2) + "\n"
         )
     finally:
-        if writer is not None:
-            writer.close()
         env.close(exit_process=False)
     del env
     SimulationManager.flush_cleanup_queue()
@@ -230,7 +229,7 @@ def main() -> None:
     parser.add_argument("--num-envs", type=int, default=2)
     parser.add_argument("--speed", type=float, default=2.0)
     parser.add_argument(
-        "--renderer", choices=["no-render", "rt", "hybrid"], default="rt"
+        "--renderer", choices=["hybrid", "fast-rt", "no-render"], default="hybrid"
     )
     parser.add_argument("--seconds", type=float)
     parser.add_argument("--particle-spacing", type=float)
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
index 69c7fca66..a56e8aabe 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
@@ -15,12 +15,18 @@
 ## Task Program → 流体 Gym
 
 ```bash
-python -m embodichain.lab.scripts.run_fluid_task_program --num-envs 2
+python -m embodichain.lab.scripts.run_fluid_task_program --num-envs 2 --renderer hybrid
 ```
 
 默认先在 MuJoCo 中执行现有 Task Program，验证真实杯子抬升、倾倒和放回，
 再启动独立 SPH Gym 回放其关节与杯子实测状态。输出包含源轨迹、源验收报告、
-流体接水量统计和 `fluid_gym.mp4`。复用本工具生成的已验收轨迹可传
+流体接水量统计，以及 `videos/episode_0_fluid_gym_env0.mp4`、
+`videos/episode_0_fluid_gym_env1.mp4` 等每环境独立视频。
+默认使用 `hybrid`，也支持 `--renderer fast-rt`。
+录制通过环境 `record_camera` 事件调用现有 `record_camera_data`，
+配置 `per_env: true`、`fps: null`，每个控制步采集各 arena 的相对视角，
+按实际控制频率（本配置 24 fps）保存。脚本结束前显式保存最后一段视频。
+复用本工具生成的已验收轨迹可传
 `--trajectory outputs/fluid-task-program/task_program_trajectory.pt`。
 `--renderer no-render --seconds 0.125 --particle-spacing 0.006 --partial-reset-step 1`
 用于低成本双环境局部重置检查；粗粒度检查不代表完整液体效果。
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
index 2cd48c31a..d697f368f 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
@@ -1,5 +1,7 @@
 environment_id: pour_cup_fluid
 physics: newton
+render_cfg:
+  renderer: hybrid
 num_envs: 2
 arena_space: 2.5
 max_episode_steps: 2000
@@ -224,6 +226,23 @@ simulation:
         update_period: 1
 env:
   sim_steps_per_control: 60
+  events:
+    record_camera:
+      func: record_camera_data
+      mode: interval
+      interval_step: 1
+      is_global: true
+      params:
+        name: fluid_gym
+        resolution: [640, 480]
+        intrinsics: [550.0, 550.0, 320.0, 240.0]
+        eye: [-1.4, -1.4, 1.2]
+        target: [-0.43, -0.03, 0.47]
+        up: [0.0, 0.0, 1.0]
+        max_env_num: 16
+        per_env: true
+        fps: null
+        save_path: outputs/fluid-task-program/videos
   control_parts:
   - arm
   - eef
diff --git a/tests/gym/envs/managers/test_record.py b/tests/gym/envs/managers/test_record.py
index 6b0f2fad2..f5e6e477e 100644
--- a/tests/gym/envs/managers/test_record.py
+++ b/tests/gym/envs/managers/test_record.py
@@ -16,6 +16,13 @@
 
 from __future__ import annotations
 
+from unittest.mock import Mock
+import numpy as np
+import pytest
+import torch
+
+import embodichain.lab.gym.envs.managers.record as record_module
+
 from embodichain.lab.gym.envs.managers import FunctorCfg
 from embodichain.lab.gym.envs.managers.record import record_camera_data
 from embodichain.lab.sim.sensors import CameraCfg
@@ -24,6 +31,15 @@
 class _CameraHandle:
     group_id = 7
 
+    def __init__(self) -> None:
+        self.rgb = torch.zeros(2, 4, 6, 4, dtype=torch.uint8)
+
+    def update(self, **kwargs) -> None:
+        pass
+
+    def get_data(self) -> dict:
+        return {"color": self.rgb}
+
 
 class _Simulation:
     def __init__(self) -> None:
@@ -34,8 +50,13 @@ def add_sensor(self, sensor_cfg: CameraCfg) -> _CameraHandle:
         self.sensor_cfg = sensor_cfg
         return self.camera
 
+    def render_camera_group(self, group_ids: list[int]) -> None:
+        assert group_ids == [self.camera.group_id]
+
 
 class _Environment:
+    control_frequency = 24.0
+
     def __init__(self) -> None:
         self.sim = _Simulation()
         self.camera_group_ids: list[int] = []
@@ -52,3 +73,84 @@ def test_record_camera_is_marked_for_visualization() -> None:
 
     assert env.sim.sensor_cfg is not None
     assert env.sim.sensor_cfg.visualization_role == "record"
+
+
+def _make_recorder(**params):
+    env = _Environment()
+    params = {"name": "views", **params}
+    cfg = FunctorCfg(func=record_camera_data, params=params)
+    return env, record_camera_data(cfg, env)
+
+
+def test_per_environment_capture_copies_views_and_saves_at_control_frequency(
+    monkeypatch,
+):
+    env, recorder = _make_recorder(per_env=True, fps=None)
+    # Distinct pixels identify the arena view in each saved video.
+    env.sim.camera.rgb[0, ..., :3] = 11
+    env.sim.camera.rgb[1, ..., :3] = 71
+    recorder(env, None, name="views", per_env=True, fps=None)
+    env.sim.camera.rgb.fill_(0)  # Camera buffers are reused after acquisition.
+    save_video = Mock()
+    monkeypatch.setattr(record_module, "images_to_video", save_video)
+
+    recorder.save_and_clear()
+    recorder.finalize()
+    assert save_video.call_count == 2
+    for env_id, (expected, call) in enumerate(zip((11, 71), save_video.call_args_list)):
+        assert call.args[2] == f"episode_0_views_env{env_id}"
+        np.testing.assert_array_equal(call.args[0][0], np.full((4, 6, 3), expected))
+        assert call.kwargs["fps"] == 24.0
+
+
+def test_per_environment_reset_and_discard_preserve_unselected_recordings(monkeypatch):
+    env, recorder = _make_recorder(per_env=True)
+    save_video = Mock()
+    monkeypatch.setattr(record_module, "images_to_video", save_video)
+    recorder(env, None, name="views")
+    recorder.save_and_clear(torch.tensor([0]))
+    recorder(env, None, name="views")
+    recorder.discard_and_clear(torch.tensor([0]))
+    recorder.save_and_clear(torch.tensor([1]))
+    assert [
+        (call.args[2], len(call.args[0])) for call in save_video.call_args_list
+    ] == [("episode_0_views_env0", 1), ("episode_0_views_env1", 2)]
+    recorder(env, None, name="views")
+    recorder.save_and_clear(torch.tensor([0]))
+    assert save_video.call_args.args[2] == "episode_1_views_env0"
+    recorder.finalize()
+    assert not recorder._env_frames[1]
+
+
+def test_failed_per_environment_save_keeps_frames_for_retry(monkeypatch):
+    env, recorder = _make_recorder(per_env=True)
+    recorder(env, None, name="views")
+    save_video = Mock(side_effect=OSError("disk full"))
+    monkeypatch.setattr(record_module, "images_to_video", save_video)
+    with pytest.raises(OSError, match="disk full"):
+        recorder.save_and_clear(torch.tensor([1]))
+    save_video.side_effect = None
+    recorder.save_and_clear(torch.tensor([1]))
+    assert save_video.call_args.args[2] == "episode_0_views_env1"
+    assert len(save_video.call_args.args[0]) == 1
+
+
+def test_camera_recording_honors_explicit_environment_limit(monkeypatch):
+    env, recorder = _make_recorder(per_env=True)
+    env.sim.camera.rgb = torch.zeros(17, 4, 6, 4, dtype=torch.uint8)
+    recorder(env, None, name="views", max_env_num=17)
+    save_video = Mock()
+    monkeypatch.setattr(record_module, "images_to_video", save_video)
+    recorder.save_and_clear()
+    assert save_video.call_count == 17
+    env, recorder = _make_recorder()
+    recorder(env, None, name="views", max_env_num=1)
+    assert recorder._frames[0].shape == (4, 6, 3)
+
+
+@pytest.mark.parametrize("fps", [0, -1, float("nan"), float("inf")])
+def test_invalid_recording_fps_fails_before_camera_creation(fps):
+    env = _Environment()
+    with pytest.raises(ValueError, match="fps"):
+        record_camera_data(FunctorCfg(params={"fps": fps}), env)
+    assert env.sim.sensor_cfg is None
diff --git a/tests/gym/envs/test_fluid_integration.py b/tests/gym/envs/test_fluid_integration.py
index 6c80d895d..7d9861b02 100644
--- a/tests/gym/envs/test_fluid_integration.py
+++ b/tests/gym/envs/test_fluid_integration.py
@@ -42,6 +42,10 @@
 
 def test_fluid_deployment_resolves_sources_and_observation_entities():
     cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    assert cfg.sim_cfg.render_cfg.renderer == "hybrid"
+    assert cfg.events.record_camera.params["per_env"] is True
+    assert cfg.events.record_camera.params["fps"] is None
+    assert cfg.events.record_camera.interval_step == 1
     assert len(cfg.fluid_object) == 1
     fluid = cfg.fluid_object[0]
     assert isinstance(fluid, FluidObjectCfg)
diff --git a/tests/gym/envs/test_fluid_replay_runtime.py b/tests/gym/envs/test_fluid_replay_runtime.py
index bf0a88bdf..89a37511e 100644
--- a/tests/gym/envs/test_fluid_replay_runtime.py
+++ b/tests/gym/envs/test_fluid_replay_runtime.py
@@ -48,6 +48,7 @@ def setup_method(self):
         cfg.sim_cfg.render_cfg = RenderCfg(renderer="no-render")
         cfg.sim_cfg.headless = True
         cfg.enable_sensor = False
+        cfg.events.record_camera = None
         cfg.sim_steps_per_control = 1
         cfg.sim_cfg.physics_dt = 0.001
         solver = cfg.sim_cfg.physics_cfg.solver_cfg
diff --git a/tests/lab/scripts/test_run_fluid_task_program.py b/tests/lab/scripts/test_run_fluid_task_program.py
new file mode 100644
index 000000000..876f7c73e
--- /dev/null
+++ b/tests/lab/scripts/test_run_fluid_task_program.py
@@ -0,0 +1,121 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Fluid replay launcher renderer and final recording commit contracts."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import Mock
+import sys
+import pytest
+import torch
+
+from embodichain.lab.scripts import run_fluid_task_program as runner
+
+pytestmark = pytest.mark.no_sim
+TASK = (
+    Path(__file__).resolve().parents[3]
+    / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
+)
+
+
+@pytest.mark.parametrize("renderer", [None, "hybrid", "fast-rt"])
+def test_launcher_selects_supported_renderers(renderer, monkeypatch, tmp_path):
+    args = [
+        "fluid",
+        "--trajectory",
+        str(tmp_path / "source.pt"),
+        "--output",
+        str(tmp_path),
+    ]
+    if renderer is not None:
+        args += ["--renderer", renderer]
+    monkeypatch.setattr(sys, "argv", args)
+    monkeypatch.setattr(runner, "discover_task_packages", lambda: None)
+    replay = Mock()
+    monkeypatch.setattr(runner, "_replay", replay)
+    runner.main()
+    assert replay.call_args.args[0].renderer == (renderer or "hybrid")
+
+
+def test_final_episode_is_committed_through_record_camera(monkeypatch, tmp_path):
+    from embodichain.lab.gym.envs import fluid_replay
+    from embodichain.lab.gym.envs.managers import observations
+
+    recorder = SimpleNamespace(save_and_clear=Mock())
+    created = []
+
+    class Environment:
+        def __init__(self, cfg, **kwargs):
+            created.append(cfg)
+            self.num_envs = cfg.num_envs
+            self.device = torch.device("cpu")
+            self.step_dt = 1 / 24
+            self.control_frequency = 24
+            self.replay_duration = self.step_dt
+            self.active_joint_ids = [0]
+            self.event_manager = SimpleNamespace(get_functor=lambda name: recorder)
+            self.sim = SimpleNamespace(
+                get_fluid_object=lambda uid: SimpleNamespace(num_particles=8)
+            )
+            self.source_qualification = {"accepted": True}
+            self.close = Mock()
+
+        def reset(self, **kwargs):
+            return {}, {}
+
+        def step(self, action):
+            info = {
+                "fluid_replay_time_s": torch.full((self.num_envs,), self.step_dt),
+                "fluid_replay_finished": torch.ones(self.num_envs, dtype=torch.bool),
+            }
+            return {}, None, None, None, info
+
+    monkeypatch.setattr(fluid_replay, "FluidReplayEnv", Environment)
+    monkeypatch.setattr(
+        observations,
+        "get_fluid_summary",
+        lambda env, *args: torch.ones(env.num_envs, 5),
+    )
+    monkeypatch.setattr(
+        observations,
+        "get_fluid_volume_in_region",
+        lambda env, *args, **kwargs: torch.zeros(env.num_envs, 1),
+    )
+    args = argparse.Namespace(
+        replay_task=str(TASK),
+        num_envs=3,
+        renderer="fast-rt",
+        particle_spacing=None,
+        speed=2,
+        seconds=None,
+        partial_reset_step=None,
+    )
+    runner._replay(args, tmp_path / "source.pt", tmp_path)
+    recorder.save_and_clear.assert_called_once_with()
+    cfg = created[0]
+    assert cfg.events.record_camera.params["max_env_num"] == 3
+    assert cfg.events.record_camera.params["save_path"] == str(tmp_path / "videos")
+    result = json.loads((tmp_path / "fluid_gym_result.json").read_text())
+    assert result["renderer"] == "fast-rt"
+    assert result["video_fps"] == 24
+    assert result["videos"] == [
+        f"videos/episode_0_fluid_gym_env{i}.mp4" for i in range(3)
+    ]

From fa3db6abe0740d926c8f7a2493df526fcd7dc3e6 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Wed, 14 Oct 2026 16:22:15 +0800
Subject: [PATCH 06/19] feat(sim): generate fluid fills from local volume
 profiles

Sample a declared capped revolved profile and fill level on a deterministic lattice. Keep solid clearance separate from the free surface and retain the local template in memory for reset. Remove the default water_fill particle asset, resample the same recipe for spacing overrides, and preserve explicit point imports. Qualify complete two-environment Hybrid pouring and per-environment recordings.
---
 IMPLEMENTATION_PLAN.md                        |  60 ++++++++
 agent_context/MAP.yaml                        |   5 +-
 .../topics/env-framework/fluid-replay.md      |   5 +
 .../topics/simulation-system/fluids.md        |  16 ++-
 embodichain/compute/geometry/particles.py     |  54 ++++++-
 .../lab/scripts/run_fluid_task_program.py     |  14 +-
 embodichain/lab/sim/cfg/fluid.py              |  14 +-
 .../manipulation/tableware/pour_cup/README.md |  26 +++-
 .../tableware/pour_cup/assets/water_fill.npz  | Bin 180343 -> 0 bytes
 .../tableware/pour_cup/env.fluid.yaml         |   8 +-
 setup.py                                      |   3 +-
 tests/compute/test_particles.py               | 133 ++++++++++++++++++
 tests/gym/envs/test_fluid_integration.py      |   5 +-
 tests/gym/envs/test_fluid_replay_runtime.py   |  30 ++--
 .../scripts/test_run_fluid_task_program.py    |  24 ++++
 tests/sim/test_fluid_cfg.py                   |  28 ++++
 tests/test_task_program_package_data.py       |   8 +-
 17 files changed, 403 insertions(+), 30 deletions(-)
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/water_fill.npz
 create mode 100644 tests/compute/test_particles.py

diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
index 945b09a36..d19e98a4d 100644
--- a/IMPLEMENTATION_PLAN.md
+++ b/IMPLEMENTATION_PLAN.md
@@ -234,3 +234,63 @@ Evidence: `outputs/fluid-gym-hybrid-record/videos/episode_0_fluid_gym_env0.mp4`,
 `record-camera-contact-sheet.jpg`. Backend smoke evidence is in
 `outputs/fluid-record-fastrt-smoke/`; row-selection evidence is in
 `outputs/fluid-record-partial-smoke/`.
+
+
+## Completed follow-up: file-free procedural fluid initialization
+
+Default pouring now declares a capped revolved cavity profile and fill level.
+`compute/geometry/particles.py` samples it on the existing deterministic cubic
+lattice, respecting side-wall distance and end-cap clearance. The free-surface
+clip is separate from solid clearance. Geometric configuration is converted to
+local points once during declaration, with no disk cache or particle artifact;
+existing scene binding and selected reset reuse the memory template. Direct
+initialization and the existing stationary replay hold are retained; this change
+does not introduce an unused settled-state cache or a second stepping owner.
+
+`water_fill.npz` is removed, and package-data patterns include only task mesh
+assets (OBJ/GLB). Explicit points/NPZ imports remain supported. Volume-only
+filters and launcher spacing overrides are rejected for fixed point imports.
+The runner's spacing override preserves the configured volume instead of
+substituting a cylinder. New reports include the source recipe, spacing and
+density for reproducibility.
+
+For this cup: profile `[-0.044, 0.031] -> [0.05, 0.0385]` (Z, radius),
+fill level 0.91, clearance 0.003 m, spacing 0.0015 m. The standard cell-centre
+lattice produces **76,607 particles / 258.548625 ml**, versus the previous
+**76,538 / 258.31575 ml** (nominal volume +0.0901%). Point layouts differ because
+sampling now consistently follows source bounds and physical wall clearance.
+
+Acceptance: numerical geometry/volume/spacing and configuration tests; package
+asset removal; real batched initialization/reset without resampling; complete
+Hybrid pouring with record cameras for both environments; finite state and
+received-volume comparison with the previous run.
+
+Validation completed:
+- Geometry/config/object/packaging/task/context regression: **294 passed**.
+  Final launcher/packaging checks after the fixed-point override guard: **7 passed**.
+  Context routing checks after registering profile sampling: **40 passed**.
+- Real batched Gym reset/exhaustion and Sim selected-reset regressions:
+  **2 passed**. Gym tests replace the sampler with a failing stub during full
+  and selected reset, proving reset reuses the memory template.
+- Cold-built wheel contains five pour-cup mesh assets and no `water_fill.npz`.
+  Points/NPZ import tests continue to pass. API coverage **2503/2503**, Black
+  26.3.1, context validation and diff whitespace checks pass.
+- Complete Hybrid replay: **2 x 76,607 particles**, both rows completed;
+  all sampled states finite; two independent **744-frame / 31-second / 24-fps**
+  videos decoded fully and visually checked through lift, pour and return.
+  Wall time **914.49 s**. Final received volumes **141.89175 / 141.39900 ml**,
+  relative changes **-0.06418% / +0.36654%** versus the previous saved-point run.
+- FastRT 6-mm spacing / partial-reset smoke uses the same configured profile
+  and fill level, generating **1,225** particles per environment. Untouched
+  particle state is identical at reset; record-camera frame counts are **1 / 3**
+  for reset/untouched rows. This coarse case validates protocol and finite state,
+  not liquid fidelity at the qualified 1.5-mm resolution.
+- Context revised in simulation fluids and the consuming Gym guide; routing now
+  includes profile sampling and its compute owner. Existing solver, Task Program
+  and recording contracts remain unchanged.
+
+Evidence: `outputs/fluid-gym-procedural-full/initialization-comparison.json`,
+`record-camera-validation.json`, `record-camera-contact-sheet.jpg` and both
+`videos/episode_0_fluid_gym_env*.mp4` in that output directory. The coarse check
+is in `outputs/fluid-procedural-coarse/`; package inspection is in
+`outputs/fluid-procedural-validation/wheel-assets.json`.
diff --git a/agent_context/MAP.yaml b/agent_context/MAP.yaml
index 8d4ba0879..46728758c 100644
--- a/agent_context/MAP.yaml
+++ b/agent_context/MAP.yaml
@@ -13,7 +13,7 @@ topics:
     NRDCfg, DLSS, NRD, frame_time_delta_ms, explicit physics stepping, arena, DexSim, PhysicsBackendCfg,
     DefaultPhysicsCfg, NewtonPhysicsCfg, AutoSolverCfg, DexUniSolverCfg, DexUni, runtime control, deformable, particle set,
     ArticulationJointKinematics, get_parent_joint_chain, asset_physics_mode, joint_drive_props, enable_gravity,
-    fixed-cadence playback, FluidObjectCfg, FluidObject, SPHSolverCfg, fluid, 流体, quaternion, xyzw]
+    fixed-cadence playback, FluidObjectCfg, FluidSpawnCfg, FluidObject, SPHSolverCfg, revolved_volume, fill_level, 粒子采样, fluid, 流体, quaternion, xyzw]
   paths: [topics/simulation-system/simulation-system.md]
   source_of_truth:
   - embodichain/lab/sim/motion/__init__.py
@@ -22,6 +22,7 @@ topics:
   - embodichain/cli/_visualization.py
   - embodichain/lab/sim/__init__.py
   - embodichain/lab/sim/cfg/
+  - embodichain/compute/geometry/particles.py
   - embodichain/lab/sim/physics/
   - embodichain/lab/sim/spawn/
   - embodichain/lab/sim/common.py
@@ -33,7 +34,7 @@ topics:
   - embodichain/lab/sim/objects/gizmo.py
   - embodichain/lab/gym/envs/base_env.py
   watch_paths: [embodichain/lab/sim/sim_manager.py, embodichain/lab/sim/cfg/, embodichain/lab/sim/physics/,
-    embodichain/lab/sim/spawn/, embodichain/lab/sim/objects/, tests/sim/]
+    embodichain/lab/sim/spawn/, embodichain/lab/sim/objects/, embodichain/compute/geometry/particles.py, tests/compute/test_particles.py, tests/sim/]
   related_topics: [env-framework, robot-system, sensor-system, sim-visualization, ik-solvers, motion-planning,
     atomic-actions, rl-learning, configclass-pattern, robot-workspace]
   status: active
diff --git a/agent_context/topics/env-framework/fluid-replay.md b/agent_context/topics/env-framework/fluid-replay.md
index 85444ed22..d50775fe2 100644
--- a/agent_context/topics/env-framework/fluid-replay.md
+++ b/agent_context/topics/env-framework/fluid-replay.md
@@ -4,6 +4,11 @@
 components own these alongside rigid entities; `config_to_cfg()` resolves
 fluid point files and explicit relative rigid/visual mesh paths against the
 owning component. Spawn rigid references are resolved before fluid binding.
+The default pour deployment generates its fill from a local revolved profile;
+no particle asset file is required. The runner's spacing override resamples the
+same volume recipe and rejects fixed point imports. Fill definitions and
+in-memory template ownership are documented under
+[simulation fluids](../simulation-system/fluids.md).
 
 `managers/observations.py` provides particle state, summary and volume-in-region
 observations. Particle arrays are world-frame; rigid `get_local_pose()` is
diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
index 98841531c..f909defd5 100644
--- a/agent_context/topics/simulation-system/fluids.md
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -27,8 +27,20 @@ Registered host controls disable graph replay and rewind selected clocks on
 object reset.
 
 A spawn reference is a declared rigid body's initial pose, not a persistent
-attachment. Mesh volume sources must describe closed cavities, not cup walls.
-Sampling math belongs to `compute/geometry/particles.py`.
+attachment. The source may be an explicit point import, a primitive, a capped
+Z-axis revolved profile, or a closed fill-volume mesh. Profiles contain ordered
+`(local Z, radius)` pairs. `fill_level` clips the source's full Z extent before
+sampling; solid-wall clearance does not inset that free-surface plane. Mesh
+volume sources must describe closed cavities, not cup walls.
+
+Sampling math belongs to `compute/geometry/particles.py`, independent of lab.
+The manager samples once at declaration and retains the local template in
+memory; replica binding and selected reset never reopen or regenerate a source.
+Changing spacing or fill level before construction regenerates the point count;
+prepared topology remains fixed. Points/NPZ imports already contain fixed
+sampling and reject volume-only profile/fill-level controls. The default pour
+scene uses a profile source with no particle file or disk cache. Its existing
+stationary replay hold provides physical settling after direct initialization.
 
 Native rendering is published once through the existing Newton render bridge.
 Display clipping acts only on copied positions; Viser consumes detached point
diff --git a/embodichain/compute/geometry/particles.py b/embodichain/compute/geometry/particles.py
index 4f5d9a9a9..efa1ca6c8 100644
--- a/embodichain/compute/geometry/particles.py
+++ b/embodichain/compute/geometry/particles.py
@@ -33,18 +33,26 @@ def sample_volume_particles(
     radius: float = 0.025,
     height: float = 0.05,
     clearance: float = 0.0,
+    profile: Sequence[Sequence[float]] | None = None,
+    fill_level: float = 1.0,
     mesh_path: str | None = None,
     max_particles: int = 1_000_000,
 ) -> np.ndarray:
-    """Sample an origin-centred primitive or closed mesh on a cubic lattice.
+    """Sample a local primitive, revolved profile or closed mesh on a cubic lattice.
 
     Args:
-        kind: ``box``, ``sphere``, ``cylinder`` (Z axis), or ``mesh_volume``.
+        kind: ``box``, ``sphere``, ``cylinder`` (Z axis),
+            ``revolved_volume`` (Z-axis profile), or ``mesh_volume``.
         spacing: Positive lattice spacing, metres.
         size: Full box dimensions, metres.
         radius: Sphere or cylinder radius, metres.
         height: Full cylinder height, metres.
         clearance: Inward distance from the volume boundary, metres.
+        profile: For ``revolved_volume``, at least two ``(z, radius)`` pairs
+            with strictly increasing Z and nonnegative radii, in local metres.
+            Linear segments define the side wall; both ends are capped.
+        fill_level: Fraction of the source's full Z extent to fill, in ``(0, 1]``.
+            The free-surface cutoff is independent of solid-wall clearance.
         mesh_path: Closed fill-volume mesh for ``mesh_volume``.
         max_particles: Maximum candidate-grid size before allocation.
 
@@ -55,9 +63,28 @@ def sample_volume_particles(
         raise ValueError("spacing must be finite and positive.")
     if not math.isfinite(clearance) or clearance < 0:
         raise ValueError("clearance must be finite and nonnegative.")
+    if not math.isfinite(fill_level) or not 0 < fill_level <= 1:
+        raise ValueError("fill_level must be finite and in (0, 1].")
     if type(max_particles) is not int or max_particles <= 0:
         raise ValueError("max_particles must be a positive integer.")
     mesh = None
+    section = None
+    if kind == "revolved_volume":
+        section = np.asarray(profile, dtype=float)
+        if (
+            section.ndim != 2
+            or section.shape[1] != 2
+            or len(section) < 2
+            or not np.isfinite(section).all()
+            or np.any(np.diff(section[:, 0]) <= 0)
+            or np.any(section[:, 1] < 0)
+            or not np.any(section[:, 1] > 0)
+        ):
+            raise ValueError(
+                "profile requires finite (z, radius) pairs with increasing Z and nonnegative radii."
+            )
+    elif profile is not None:
+        raise ValueError("profile is only valid for revolved_volume.")
     if kind == "mesh_volume":
         import trimesh
 
@@ -76,12 +103,19 @@ def sample_volume_particles(
             extent = np.array([2 * radius] * 3)
         elif kind == "cylinder":
             extent = np.array([2 * radius, 2 * radius, height])
+        elif section is not None:
+            max_radius = section[:, 1].max()
+            extent = np.array([2 * max_radius, 2 * max_radius, np.ptp(section[:, 0])])
         else:
             raise ValueError(f"Unsupported fluid volume {kind!r}.")
         if extent.shape != (3,) or not np.isfinite(extent).all() or (extent <= 0).any():
             raise ValueError("Volume dimensions must be finite and positive.")
         lower, upper = -extent / 2, extent / 2
+        if section is not None:
+            lower[2], upper[2] = section[0, 0], section[-1, 0]
+    surface_z = lower[2] + fill_level * (upper[2] - lower[2])
     lower, upper = lower + clearance, upper - clearance
+    upper[2] = min(upper[2], surface_z)
     counts = [max(0, math.ceil(float(v) / spacing)) for v in upper - lower]
     if not all(counts):
         raise ValueError("clearance leaves an empty fill volume.")
@@ -94,6 +128,22 @@ def sample_volume_particles(
         keep &= np.linalg.norm(points, axis=1) < radius - clearance
     elif kind == "cylinder":
         keep &= np.linalg.norm(points[:, :2], axis=1) < radius - clearance
+    elif section is not None:
+        radial = np.linalg.norm(points[:, :2], axis=1)
+        keep &= radial < np.interp(points[:, 2], section[:, 0], section[:, 1])
+        if clearance:
+            # Rotational symmetry reduces side-wall distance to the meridian
+            # plane. Include all segments, including steep or concave profiles.
+            meridian = np.column_stack((points[:, 2], radial))
+            distance2 = np.full(len(points), np.inf)
+            for start, end in zip(section[:-1], section[1:]):
+                segment = end - start
+                t = np.clip(
+                    (meridian - start) @ segment / np.dot(segment, segment), 0, 1
+                )
+                delta = meridian - (start + t[:, None] * segment)
+                distance2 = np.minimum(distance2, np.einsum("ij,ij->i", delta, delta))
+            keep &= distance2 >= clearance**2
     elif mesh is not None:
         import trimesh
 
diff --git a/embodichain/lab/scripts/run_fluid_task_program.py b/embodichain/lab/scripts/run_fluid_task_program.py
index cfa44b6d4..f0a149312 100644
--- a/embodichain/lab/scripts/run_fluid_task_program.py
+++ b/embodichain/lab/scripts/run_fluid_task_program.py
@@ -113,15 +113,14 @@ def _replay(args, trajectory: Path, output: Path) -> None:
         cfg.events.record_camera.params["save_path"] = str(output / "videos")
         cfg.events.record_camera.params["max_env_num"] = cfg.num_envs
     if args.particle_spacing is not None:
-        # Qualification smoke mode uses a coarser initial fill, explicitly recorded.
+        # Resample the configured fill volume at the smoke-test resolution.
         fluid = cfg.fluid_object[0]
-        from embodichain.lab.sim.cfg import FluidSpawnCfg
-
+        if fluid.spawn.kind == "points":
+            raise ValueError(
+                "particle-spacing override requires a volume source; imported points have fixed sampling."
+            )
         fluid.particle_spacing = args.particle_spacing
         fluid.particle_radius = 0.3 * args.particle_spacing
-        fluid.spawn = FluidSpawnCfg(
-            kind="cylinder", radius=0.028, height=0.07, reference_uid="cup"
-        )
         cfg.sim_cfg.physics_cfg.solver_cfg["particle_support_radius"] = (
             2.25 * args.particle_spacing
         )
@@ -203,6 +202,9 @@ def _replay(args, trajectory: Path, output: Path) -> None:
             "videos": videos,
             "video_fps": env.control_frequency if videos else None,
             "particle_count_per_env": env.sim.get_fluid_object("water").num_particles,
+            "fluid_source": cfg.fluid_object[0].spawn.to_dict(),
+            "particle_spacing_m": cfg.fluid_object[0].particle_spacing,
+            "density_kg_m3": cfg.fluid_object[0].density,
             "partial_reset_step": args.partial_reset_step,
             "replay_complete": info["fluid_replay_finished"].cpu().tolist(),
             "wall_seconds": time.perf_counter() - started,
diff --git a/embodichain/lab/sim/cfg/fluid.py b/embodichain/lab/sim/cfg/fluid.py
index f73ecd055..b1c560872 100644
--- a/embodichain/lab/sim/cfg/fluid.py
+++ b/embodichain/lab/sim/cfg/fluid.py
@@ -39,13 +39,19 @@ class FluidSpawnCfg:
     ``reference_uid`` resolves a declared rigid body's initial pose once.
     """
 
-    kind: Literal["points", "box", "sphere", "cylinder", "mesh_volume"] = "points"
+    kind: Literal[
+        "points", "box", "sphere", "cylinder", "revolved_volume", "mesh_volume"
+    ] = "points"
     positions: Any = None
     file_path: str | None = None
     size: tuple[float, float, float] = (0.05, 0.05, 0.05)
     radius: float = 0.025
     height: float = 0.05
     clearance: float = 0.0
+    profile: tuple[tuple[float, float], ...] | None = None
+    """Piecewise-linear ``(local Z, radius)`` pairs for a capped revolved fill volume."""
+    fill_level: float = 1.0
+    """Filled fraction of the complete source Z extent; only for volume sources."""
     reference_uid: str | None = None
     initial_velocity: tuple[float, float, float] = (0.0, 0.0, 0.0)
     max_particles: int = 1_000_000
@@ -67,6 +73,10 @@ def sample(self, spacing: float) -> np.ndarray:
         if type(self.max_particles) is not int or self.max_particles <= 0:
             raise ValueError("max_particles must be a positive integer.")
         if self.kind == "points":
+            if self.profile is not None or self.fill_level != 1.0:
+                raise ValueError(
+                    "points sources cannot apply profile or fill_level; use a volume source."
+                )
             if (self.positions is None) == (self.file_path is None):
                 raise ValueError(
                     "points requires exactly one of positions or file_path."
@@ -86,6 +96,8 @@ def sample(self, spacing: float) -> np.ndarray:
                 radius=self.radius,
                 height=self.height,
                 clearance=self.clearance,
+                profile=self.profile,
+                fill_level=self.fill_level,
                 mesh_path=self.file_path,
                 max_particles=self.max_particles,
             )
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
index a56e8aabe..3c72f4a1e 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
@@ -32,8 +32,30 @@ python -m embodichain.lab.scripts.run_fluid_task_program --num-envs 2 --renderer
 用于低成本双环境局部重置检查；粗粒度检查不代表完整液体效果。
 
 `env.fluid.yaml` 拥有 SPH 参数、刚体边界、初始粒子和流体观测；
-`task.franka.fluid.yaml` 选择环境和机器人组件。粒子文件相对环境配置解析，
-杯子、边界网格和初始粒子随任务包发布。求解器配置直接使用 DexSim SPHSolverCfg。
+`task.franka.fluid.yaml` 选择环境和机器人组件。杯子与边界网格随任务包发布，
+水体在构建场景时直接采样生成，默认不读取或保存粒子文件。
+求解器配置直接使用 DexSim SPHSolverCfg。
+
+初始水体由以下配置描述（单位为米）：
+
+```yaml
+spawn:
+  kind: revolved_volume
+  reference_uid: cup
+  profile: [[-0.044, 0.031], [0.05, 0.0385]]  # [杯子局部 Z, 腔体内半径]
+  fill_level: 0.91
+  clearance: 0.003
+```
+
+截面绕局部 Z 轴旋转并封闭两端。`fill_level` 指完整腔体高度的比例，
+其液面裁剪与固体壁面间隙分别处理；它不表示稳定后的体积百分比。
+1.5 mm 规则网格生成 76,607 个粒子，名义体积约 258.55 ml。
+修改 `particle_spacing` 或 `fill_level` 后，在下一次构建时重新生成点集。
+`--particle-spacing` 也使用同一截面重新采样，便于粗分辨率检查。
+
+局部点集只生成一次并保存在内存，reset 根据杯子位姿和 arena 偏移恢复选中环境；
+回放起始的静置阶段继续用于液体稳定化。NPZ 仍可作为外部点集导入格式，
+点集导入模式的采样分辨率固定，不接受脚本的间距覆盖。
 
 `FluidReplayEnv` 支持标准 reset/step、按行时钟与流体重置、summary 和区域体积观测。
 它从单行合格源轨迹广播到多个环境，要求固定机器人根位姿和单个 Newton 子步。
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/water_fill.npz b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/water_fill.npz
deleted file mode 100644
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diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
index d697f368f..54d1e6c4f 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
@@ -189,8 +189,12 @@ simulation:
     particle_spacing: 0.0015
     particle_radius: 0.00045
     spawn:
-      kind: points
-      file_path: ./assets/water_fill.npz
+      kind: revolved_volume
+      profile:
+      - [-0.044, 0.031]
+      - [0.05, 0.0385]
+      fill_level: 0.91
+      clearance: 0.003
       reference_uid: cup
     render:
       mode: mesh_surface
diff --git a/setup.py b/setup.py
index 1d3e9c740..dc26e6efd 100644
--- a/setup.py
+++ b/setup.py
@@ -137,7 +137,8 @@ def main():
                 "**/*.yaml",
                 "**/*.yml",
                 "**/README.md",
-                "tasks/manipulation/tableware/pour_cup/assets/*",
+                "tasks/manipulation/tableware/pour_cup/assets/*.obj",
+                "tasks/manipulation/tableware/pour_cup/assets/*.glb",
             ],
         },
         cmdclass=cmdclass,
diff --git a/tests/compute/test_particles.py b/tests/compute/test_particles.py
new file mode 100644
index 000000000..04855da01
--- /dev/null
+++ b/tests/compute/test_particles.py
@@ -0,0 +1,133 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Numerical volume sampling for file-free particle initialization."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from embodichain.compute.geometry.particles import sample_volume_particles
+
+pytestmark = pytest.mark.no_sim
+
+
+def test_constant_revolved_profile_matches_cylinder():
+    actual = sample_volume_particles(
+        "revolved_volume",
+        0.004,
+        profile=[[-0.02, 0.015], [0.02, 0.015]],
+        clearance=0.001,
+    )
+    expected = sample_volume_particles(
+        "cylinder", 0.004, radius=0.015, height=0.04, clearance=0.001
+    )
+    np.testing.assert_array_equal(actual, expected)
+
+
+def test_tapered_fill_volume_and_mass_converge_with_resolution():
+    # A linear wall allows an independent integral of its inward offset.
+    profile = [[-0.04, 0.02], [0.04, 0.03]]
+    clearance = 0.002
+    fill_level = 0.8
+    slope = (0.03 - 0.02) / 0.08
+    lower_z = -0.04 + clearance
+    surface_z = -0.04 + fill_level * 0.08
+    lower_radius = 0.02 + slope * clearance - clearance * np.sqrt(1 + slope**2)
+    depth = surface_z - lower_z
+    expected_volume = np.pi * (
+        lower_radius**2 * depth
+        + lower_radius * slope * depth**2
+        + slope**2 * depth**3 / 3
+    )
+    counts = []
+    for spacing in (0.002, 0.001):
+        points = sample_volume_particles(
+            "revolved_volume",
+            spacing,
+            profile=profile,
+            clearance=clearance,
+            fill_level=fill_level,
+        )
+        radius = np.linalg.norm(points[:, :2], axis=1)
+        wall_distance = (0.02 + slope * (points[:, 2] + 0.04) - radius) / np.sqrt(
+            1 + slope**2
+        )
+        assert np.all(wall_distance >= clearance - 1e-8)
+        assert np.all(points[:, 2] > lower_z)
+        assert np.all(points[:, 2] < surface_z)
+        assert len(points) * spacing**3 == pytest.approx(expected_volume, rel=0.025)
+        counts.append(len(points))
+    assert counts[1] / counts[0] == pytest.approx(8, rel=0.025)
+
+
+def test_lower_fill_level_keeps_lattice_origin_and_has_no_solid_free_surface_margin():
+    kwargs = dict(size=(0.08, 0.08, 0.1), clearance=0.02)
+    full = sample_volume_particles("box", 0.01, **kwargs)
+    half = sample_volume_particles("box", 0.01, fill_level=0.5, **kwargs)
+    np.testing.assert_array_equal(half, full[full[:, 2] < 0])
+    assert half[:, 2].max() == pytest.approx(-0.005)
+
+
+def test_multi_segment_profile_is_deterministic_and_can_narrow_again():
+    profile = [[-0.03, 0.02], [0.0, 0.01], [0.03, 0.025]]
+    kwargs = dict(profile=profile, clearance=0.001)
+    points = sample_volume_particles("revolved_volume", 0.004, **kwargs)
+    np.testing.assert_array_equal(
+        points, sample_volume_particles("revolved_volume", 0.004, **kwargs)
+    )
+    assert np.all(
+        np.linalg.norm(points[:, :2], axis=1)
+        < np.interp(points[:, 2], np.array(profile)[:, 0], np.array(profile)[:, 1])
+    )
+    assert points.flags.c_contiguous
+
+
+@pytest.mark.parametrize(
+    "profile",
+    [
+        None,
+        [],
+        [[0, 1]],
+        [[0, 1], [0, 2]],
+        [[1, 1], [0, 2]],
+        [[0, -1], [1, 1]],
+        [[0, 0], [1, 0]],
+        [[0, 1], [float("nan"), 1]],
+        [[0, 1, 2], [1, 1, 2]],
+    ],
+)
+def test_invalid_profiles_fail_before_grid_allocation(profile):
+    with pytest.raises(ValueError, match="profile"):
+        sample_volume_particles("revolved_volume", 0.001, profile=profile)
+
+
+@pytest.mark.parametrize("fill_level", [0, -0.1, 1.01, float("inf"), float("nan")])
+def test_invalid_fill_levels_are_rejected(fill_level):
+    with pytest.raises(ValueError, match="fill_level"):
+        sample_volume_particles("box", 0.01, fill_level=fill_level)
+
+
+def test_profile_source_obeys_candidate_limit_before_allocation():
+    with pytest.raises(ValueError, match="max_particles"):
+        sample_volume_particles(
+            "revolved_volume", 1e-9, profile=[[0, 1], [1, 1]], max_particles=100
+        )
+
+
+def test_profile_on_another_volume_is_not_ignored():
+    with pytest.raises(ValueError, match="profile"):
+        sample_volume_particles("box", 0.01, profile=[[0, 1], [1, 1]])
diff --git a/tests/gym/envs/test_fluid_integration.py b/tests/gym/envs/test_fluid_integration.py
index 7d9861b02..9a98fce08 100644
--- a/tests/gym/envs/test_fluid_integration.py
+++ b/tests/gym/envs/test_fluid_integration.py
@@ -49,8 +49,9 @@ def test_fluid_deployment_resolves_sources_and_observation_entities():
     assert len(cfg.fluid_object) == 1
     fluid = cfg.fluid_object[0]
     assert isinstance(fluid, FluidObjectCfg)
-    assert Path(fluid.spawn.file_path).is_absolute()
-    assert fluid.spawn.sample(fluid.particle_spacing).shape == (76538, 3)
+    assert fluid.spawn.kind == "revolved_volume"
+    assert fluid.spawn.file_path is None
+    assert fluid.spawn.sample(fluid.particle_spacing).shape == (76607, 3)
     assert cfg.observations.received_volume.extra["shape"] == [1]
     assert all(
         isinstance(item, SceneEntityCfg)
diff --git a/tests/gym/envs/test_fluid_replay_runtime.py b/tests/gym/envs/test_fluid_replay_runtime.py
index 89a37511e..91ad4755d 100644
--- a/tests/gym/envs/test_fluid_replay_runtime.py
+++ b/tests/gym/envs/test_fluid_replay_runtime.py
@@ -19,6 +19,7 @@
 from __future__ import annotations
 
 from pathlib import Path
+from unittest.mock import patch
 import subprocess
 import sys
 import numpy as np
@@ -63,7 +64,10 @@ def setup_method(self):
         fluid.particle_spacing = 0.01
         fluid.particle_radius = 0.003
         fluid.spawn = FluidSpawnCfg(
-            kind="box", size=(0.02, 0.02, 0.02), reference_uid="cup"
+            kind="revolved_volume",
+            profile=((-0.01, 0.01), (0.01, 0.01)),
+            fill_level=0.8,
+            reference_uid="cup",
         )
         fluid.render = FluidRenderCfg(mode="none")
         root = torch.tensor([0, 0, 0, 0, 0, 0, 1.0]).repeat(1, 2, 1)
@@ -102,19 +106,29 @@ def teardown_method(self):
 
     def test_partial_reset_preserves_other_world_then_finishes_independently(self):
         env = self.env
-        env.reset(seed=0)
+        with patch.object(
+            FluidSpawnCfg,
+            "sample",
+            side_effect=AssertionError("reset must reuse memory"),
+        ):
+            env.reset(seed=0)
         action = torch.zeros(
             (env.num_envs, len(env.active_joint_ids)), device=env.device
         )
         env.step(action)
         env.step(action)
         before = env.sim.get_fluid_object("water").data.particle_state_w.clone()
-        env.reset(
-            options={
-                "reset_ids": torch.tensor([0], device=env.device),
-                "save_data": False,
-            }
-        )
+        with patch.object(
+            FluidSpawnCfg,
+            "sample",
+            side_effect=AssertionError("reset must reuse memory"),
+        ):
+            env.reset(
+                options={
+                    "reset_ids": torch.tensor([0], device=env.device),
+                    "save_data": False,
+                }
+            )
         torch.testing.assert_close(
             env.sim.get_fluid_object("water").data.particle_state_w[1],
             before[1],
diff --git a/tests/lab/scripts/test_run_fluid_task_program.py b/tests/lab/scripts/test_run_fluid_task_program.py
index 876f7c73e..92e7a4aa0 100644
--- a/tests/lab/scripts/test_run_fluid_task_program.py
+++ b/tests/lab/scripts/test_run_fluid_task_program.py
@@ -115,7 +115,31 @@ def step(self, action):
     assert cfg.events.record_camera.params["save_path"] == str(tmp_path / "videos")
     result = json.loads((tmp_path / "fluid_gym_result.json").read_text())
     assert result["renderer"] == "fast-rt"
+    assert result["fluid_source"]["kind"] == "revolved_volume"
+    assert result["fluid_source"]["file_path"] is None
+    assert result["particle_spacing_m"] == 0.0015
     assert result["video_fps"] == 24
     assert result["videos"] == [
         f"videos/episode_0_fluid_gym_env{i}.mp4" for i in range(3)
     ]
+
+
+def test_spacing_override_rejects_fixed_points_before_scene_creation(
+    monkeypatch, tmp_path
+):
+    from embodichain.lab.gym.utils.gym_utils import config_to_cfg
+    from embodichain.lab.sim.cfg import FluidSpawnCfg
+    from embodichain.utils.utility import load_config
+
+    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    cfg.fluid_object[0].spawn = FluidSpawnCfg(positions=[[0, 0, 0]])
+    monkeypatch.setattr(runner, "config_to_cfg", lambda *args, **kwargs: cfg)
+    args = argparse.Namespace(
+        replay_task=str(TASK),
+        num_envs=2,
+        renderer="hybrid",
+        particle_spacing=0.006,
+        speed=2,
+    )
+    with pytest.raises(ValueError, match="fixed sampling"):
+        runner._replay(args, tmp_path / "unused.pt", tmp_path)
diff --git a/tests/sim/test_fluid_cfg.py b/tests/sim/test_fluid_cfg.py
index bc146470b..77ff9f368 100644
--- a/tests/sim/test_fluid_cfg.py
+++ b/tests/sim/test_fluid_cfg.py
@@ -173,3 +173,31 @@ def test_closed_mesh_sampling_matches_equivalent_box(tmp_path):
     actual = sample_volume_particles("mesh_volume", 0.01, mesh_path=str(mesh))
     expected = sample_volume_particles("box", 0.01, size=(0.04,) * 3)
     np.testing.assert_allclose(actual, expected)
+
+
+def test_profile_source_round_trip_rebuilds_without_a_particle_file():
+    cfg = FluidObjectCfg.from_dict(
+        {
+            "uid": "water",
+            "spawn": {
+                "kind": "revolved_volume",
+                "profile": [[-0.02, 0.01], [0.03, 0.02]],
+                "fill_level": 0.8,
+                "clearance": 0.001,
+                "reference_uid": "cup",
+            },
+        }
+    )
+    restored = FluidObjectCfg.from_dict(cfg.to_dict())
+    assert restored.spawn.file_path is None
+    points = restored.spawn.sample(0.004)
+    np.testing.assert_array_equal(points, cfg.spawn.sample(0.004))
+    assert points.dtype == np.float32
+    assert points[:, 2].max() < -0.02 + 0.8 * 0.05
+    assert len(restored.spawn.sample(0.002)) > len(points)
+
+
+@pytest.mark.parametrize("extra", [{"profile": [[0, 1], [1, 1]]}, {"fill_level": 0.5}])
+def test_points_reject_geometry_filters_instead_of_silently_ignoring_them(extra):
+    with pytest.raises(ValueError, match="points sources"):
+        FluidSpawnCfg(positions=[[0, 0, 0]], **extra).sample(0.001)
diff --git a/tests/test_task_program_package_data.py b/tests/test_task_program_package_data.py
index 28a350d21..746a2f4f2 100644
--- a/tests/test_task_program_package_data.py
+++ b/tests/test_task_program_package_data.py
@@ -106,7 +106,6 @@
                 "fluid_table.obj",
                 "mug_wall.obj",
                 "receiver_wall.obj",
-                "water_fill.npz",
             )
         ),
         Path("tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"),
@@ -246,13 +245,18 @@ def test_setup_stages_all_official_task_programs(
         "**/*.yaml",
         "**/*.yml",
         "**/README.md",
-        "tasks/manipulation/tableware/pour_cup/assets/*",
+        "tasks/manipulation/tableware/pour_cup/assets/*.obj",
+        "tasks/manipulation/tableware/pour_cup/assets/*.glb",
     ]
     expected_outputs = {
         Path("embodichain_tasks") / "configs" / relative_path
         for relative_path in _RESOURCE_PATHS
     }
     assert expected_outputs <= staged_config_package.relative_outputs
+    assert not (
+        staged_config_package.build_lib
+        / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/water_fill.npz"
+    ).exists()
 
 
 def test_staged_task_program_resources_decode_through_installed_paths(

From fc3f1baa479239ffcfe4c94015d1f9c991d803d4 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Wed, 14 Oct 2026 18:01:59 +0800
Subject: [PATCH 07/19] fix(fluid): render visible water in recorded Gym views

---
 IMPLEMENTATION_PLAN.md                        |  59 ++++++
 .../topics/env-framework/fluid-replay.md      |  14 +-
 .../topics/simulation-system/fluids.md        |   8 +-
 .../lab/scripts/run_fluid_task_program.py     |  13 +-
 embodichain/lab/sim/cfg/fluid.py              |   2 +
 embodichain/lab/sim/sim_manager.py            |  10 +-
 .../manipulation/tableware/pour_cup/README.md |  12 +-
 .../tableware/pour_cup/env.fluid.yaml         |   8 +-
 .../tutorials/sim/franka_dfsph_pour_demo.py   |   2 +-
 tests/gym/envs/test_fluid_integration.py      |   2 +-
 .../scripts/test_run_fluid_task_program.py    |  41 ++++-
 tests/sim/objects/test_fluid_visibility.py    | 170 ++++++++++++++++++
 tests/sim/test_fluid_cfg.py                   |  20 +++
 13 files changed, 338 insertions(+), 23 deletions(-)
 create mode 100644 tests/sim/objects/test_fluid_visibility.py

diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
index d19e98a4d..d2e927a58 100644
--- a/IMPLEMENTATION_PLAN.md
+++ b/IMPLEMENTATION_PLAN.md
@@ -294,3 +294,62 @@ Evidence: `outputs/fluid-gym-procedural-full/initialization-comparison.json`,
 `videos/episode_0_fluid_gym_env*.mp4` in that output directory. The coarse check
 is in `outputs/fluid-procedural-coarse/`; package inspection is in
 `outputs/fluid-procedural-validation/wheel-assets.json`.
+
+
+## Completed fix: liquid visibility in recorded cameras
+
+The user reported no fluid in the supplied videos. Reproduction confirmed that
+the prior Hybrid recordings qualified particle state and robot motion but did
+not display the liquid itself. Earlier claims of liquid visual qualification
+for Hybrid are retracted; decoded frames and robot pixel motion are insufficient
+oracles. Original OfflineRT Sim parity is a separate earlier result.
+
+Controlled reproduction: the same opaque blue 8,000-particle fluid block has
+**0 blue foreground pixels in Hybrid**, versus **5,873 in FastRT**. Hybrid
+shades a Vulkan G-buffer primary surface, while the fluid bridge creates only
+OptiX mesh/sphere instances. Particle statistics and indirect image changes
+can therefore be nonzero while the liquid primary surface is missing.
+
+The fluid deployment and runner now select FastRT, and the public manager and
+runner reject unsupported Hybrid liquid rendering. Empty-cup source generation
+continues using Hybrid. The record camera focuses on the cup and receiver; the
+mesh reconstruction kernel is 1.5 particle spacings. Spacing overrides resample
+the existing fill volume and also scale the explicit render radius, mesh cell
+size and reconstruction kernel radius. Renderer overrides retain other configured
+render settings. The clear-water material, physical solver, fill recipe and
+Task Program motion remain unchanged; no native binary change was needed.
+
+Validation:
+
+- Focused config/launcher/Gym/context contracts: **80 passed**. Serial GPU camera
+  regression: **1 passed**, including physical stepping, display clipping,
+  hidden-fluid controls and exact state restoration. Blue foreground counts
+  are **1,601 / 1,609**, versus **0 / 0** with the fluid outside the views.
+- Complete live SPH rollout: **2 x 76,607 particles**, **744 frames / 31 s**,
+  final received volumes **141.89175 / 141.39900 ml**, all sampled states finite.
+  Every particle-position frame was retained as ignored diagnostic output;
+  the last frame exactly matches the completed physical state.
+- The final visual configuration was qualified by a separate render-only pass
+  over those exact physical particle frames, using the existing per-environment
+  record-camera functor. No physics steps or synthetic liquid motion were added
+  during re-recording. Both **744-frame / 24-fps / 31-s** videos decoded fully;
+  decoded frames show the stream and accumulated water in both environments.
+- Clear-water visible/hidden camera control after the complete frame sequence:
+  **938 / 1,002 receiver pixels** changed by more than 10/255. Receiver RGB MAE
+  is **1.92990 / 2.08807**, versus repeat-render noise **0.17535 / 0.18492**.
+  This is direct liquid visibility evidence, independent of particle counts.
+- The production runner with 6-mm resampling, FastRT and a selected reset passed
+  a live three-control-step check: **1,225 particles/environment**, exact
+  untouched state at reset, and separate **1 / 3-frame** recordings. This checks
+  the scaled reconstruction and recording/reset protocol, not fluid fidelity.
+- Black **26.3.1**, API coverage **2503/2503**, context validation, tutorial syntax
+  and `git diff --check` pass. No full suite or release/AOT rebuild was needed.
+
+Final media and pixel/decode reports:
+`outputs/fluid-gym-visible-qualified/` (`videos/episode_0_fluid_gym_env*.mp4`,
+`render_validation.json`, `video_decode_validation.json`, decoded keyframes,
+`water_visible_env*.png` and `water_hidden_env*.png`). Physical-state evidence is
+`outputs/fluid-visibility-live-cache/` (`fluid_gym_result.json`, diagnostic particle
+frames and `particle_frame_validation.json`). The live scaled/reset smoke is in
+`outputs/fluid-visible-scaled-smoke/`. Baseline renderer comparison remains in
+`outputs/fluid-visibility-baseline/`; these generated files are not task assets.
diff --git a/agent_context/topics/env-framework/fluid-replay.md b/agent_context/topics/env-framework/fluid-replay.md
index d50775fe2..954e63bb8 100644
--- a/agent_context/topics/env-framework/fluid-replay.md
+++ b/agent_context/topics/env-framework/fluid-replay.md
@@ -6,7 +6,9 @@ fluid point files and explicit relative rigid/visual mesh paths against the
 owning component. Spawn rigid references are resolved before fluid binding.
 The default pour deployment generates its fill from a local revolved profile;
 no particle asset file is required. The runner's spacing override resamples the
-same volume recipe and rejects fixed point imports. Fill definitions and
+same volume recipe and rejects fixed point imports. It scales explicit render
+radius, mesh cell size and reconstruction kernel radius by the spacing ratio,
+keeping the surface reconstruction consistent with the new particle density. Fill definitions and
 in-memory template ownership are documented under
 [simulation fluids](../simulation-system/fluids.md).
 
@@ -39,7 +41,12 @@ source provenance; fluid capture metrics remain separate measured outcomes.
 
 ## Rendering and recording
 
-The pouring environment selects `hybrid`; the runner also accepts `fast-rt`.
+The pouring environment and runner select `fast-rt`. Hybrid is rejected for
+native fluid rendering: OptiX-only liquid geometry is absent from its Vulkan
+primary surface. Particle-state and video-decode checks alone do not qualify
+liquid visibility; use the image-based fluid regression linked below. The
+default record view focuses on the cup and receiver; the mesh kernel radius
+is 1.5 times the particle spacing to retain a connected surface after settling.
 Its interval `record_camera` event uses the existing `record_camera_data`
 functor with `per_env=True` and `fps=None`, recording every environment view at
 control frequency. The runner only redirects the output directory and sets
@@ -51,5 +58,6 @@ NoRender qualification removes the recorder before constructing the scene.
 
 Production configuration/measurement checks are in
 `tests/gym/envs/test_fluid_integration.py`; live isolation checks are in
-`tests/sim/objects/test_fluid_batch_runtime.py`. Generated trajectories/videos
+`tests/sim/objects/test_fluid_batch_runtime.py`; actual primary-surface visibility
+is checked by `tests/sim/objects/test_fluid_visibility.py`. Generated trajectories/videos
 are local qualification evidence, not packaged task assets.
diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
index f909defd5..597773238 100644
--- a/agent_context/topics/simulation-system/fluids.md
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -42,7 +42,11 @@ sampling and reject volume-only profile/fill-level controls. The default pour
 scene uses a profile source with no particle file or disk cache. Its existing
 stationary replay hold provides physical settling after direct initialization.
 
-Native rendering is published once through the existing Newton render bridge.
+Native fluid geometry requires FastRT (`fast-rt`) or OfflineRT (`rt`). The
+manager rejects Hybrid before sampling/declaration because the current bridge
+creates OptiX-only instances, absent from Hybrid's Vulkan primary surface.
+Validate camera pixels with visible and hidden fluid, not only particle counts.
+Native rendering is published through the existing Newton render bridge.
 Display clipping acts only on copied positions; Viser consumes detached point
 clouds. NoRender fluid mode does not require optical materials. Ordinary rigid
 objects may supply a separate `visual_shape`; SDF cooking stays on the physical
@@ -51,6 +55,8 @@ shape. Explicit `force_sdf=False` permits pre-baked mesh SDFs.
 Focused checks are `tests/sim/test_fluid_cfg.py`,
 `tests/sim/objects/test_fluid_object.py` and the serial CUDA lifecycle test
 `tests/sim/objects/test_fluid_runtime.py` and `test_fluid_batch_runtime.py`.
+`tests/sim/objects/test_fluid_visibility.py` checks real camera pixels after
+stepping and direct state writes, including display clipping.
 For YAML declarations, refill observations and Task Program replay, see
 [fluid Gym integration](../env-framework/fluid-replay.md). The pouring tutorial is an end-to-end
 qualification workload, not a unit-test import target.
diff --git a/embodichain/lab/scripts/run_fluid_task_program.py b/embodichain/lab/scripts/run_fluid_task_program.py
index f0a149312..dea19c596 100644
--- a/embodichain/lab/scripts/run_fluid_task_program.py
+++ b/embodichain/lab/scripts/run_fluid_task_program.py
@@ -105,7 +105,7 @@ def _replay(args, trajectory: Path, output: Path) -> None:
     cfg = config_to_cfg(load_config(str(path)), source_path=path)
     cfg.num_envs = args.num_envs
     cfg.sim_cfg.headless = True
-    cfg.sim_cfg.render_cfg = RenderCfg(renderer=args.renderer)
+    cfg.sim_cfg.render_cfg.renderer = args.renderer
     if args.renderer == "no-render":
         cfg.enable_sensor = False
         cfg.events.record_camera = None
@@ -119,6 +119,12 @@ def _replay(args, trajectory: Path, output: Path) -> None:
             raise ValueError(
                 "particle-spacing override requires a volume source; imported points have fixed sampling."
             )
+        scale = args.particle_spacing / fluid.particle_spacing
+        if fluid.render.render_radius is not None:
+            fluid.render.render_radius *= scale
+        for key in ("cell_size", "kernel_radius"):
+            if key in fluid.render.mesh_surface:
+                fluid.render.mesh_surface[key] *= scale
         fluid.particle_spacing = args.particle_spacing
         fluid.particle_radius = 0.3 * args.particle_spacing
         cfg.sim_cfg.physics_cfg.solver_cfg["particle_support_radius"] = (
@@ -231,7 +237,10 @@ def main() -> None:
     parser.add_argument("--num-envs", type=int, default=2)
     parser.add_argument("--speed", type=float, default=2.0)
     parser.add_argument(
-        "--renderer", choices=["hybrid", "fast-rt", "no-render"], default="hybrid"
+        "--renderer",
+        choices=["fast-rt", "no-render"],
+        default="fast-rt",
+        help="FastRT renders liquid surfaces; NoRender is for physics-only checks.",
     )
     parser.add_argument("--seconds", type=float)
     parser.add_argument("--particle-spacing", type=float)
diff --git a/embodichain/lab/sim/cfg/fluid.py b/embodichain/lab/sim/cfg/fluid.py
index b1c560872..1ed11df5c 100644
--- a/embodichain/lab/sim/cfg/fluid.py
+++ b/embodichain/lab/sim/cfg/fluid.py
@@ -119,6 +119,8 @@ class FluidRenderCfg:
 
     ``mesh_surface`` forwards DexSim FluidMeshSurfaceRenderDesc fields directly.
     Optional world-space clipping affects only a detached render copy.
+    Native fluid geometry requires ``fast-rt`` or ``rt``; Hybrid's Vulkan
+    primary surface does not contain the OptiX liquid instances.
     ``mode="none"`` currently requires the simulation NoRender backend.
     """
 
diff --git a/embodichain/lab/sim/sim_manager.py b/embodichain/lab/sim/sim_manager.py
index 3b285c6f5..053374585 100644
--- a/embodichain/lab/sim/sim_manager.py
+++ b/embodichain/lab/sim/sim_manager.py
@@ -2718,7 +2718,7 @@ def _declare_rigid_objects(
         return [item[3] for item in declarations]
 
     def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
-        """Declare a fixed-count, single-environment Newton DFSPH fluid.
+        """Declare a fixed-count Newton DFSPH fluid.
 
         Args:
             cfg: Fluid source, initial state, physical and render properties.
@@ -2749,6 +2749,14 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
         cfg = cfg.copy()
         cfg.validate()
         cfg.__post_init__()
+        if (
+            cfg.render.mode != "none"
+            and self.sim_config.render_cfg.renderer == "hybrid"
+        ):
+            raise NotImplementedError(
+                "Newton fluid rendering requires renderer='fast-rt' or 'rt'. "
+                "Hybrid does not publish the OptiX liquid geometry to its Vulkan primary surface."
+            )
         if not cfg.uid or cfg.uid in self.asset_uids:
             raise ValueError(
                 "Fluid uid must be nonempty and unique across scene assets."
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
index 3c72f4a1e..afec5116e 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
@@ -15,17 +15,20 @@
 ## Task Program → 流体 Gym
 
 ```bash
-python -m embodichain.lab.scripts.run_fluid_task_program --num-envs 2 --renderer hybrid
+python -m embodichain.lab.scripts.run_fluid_task_program --num-envs 2 --renderer fast-rt
 ```
 
 默认先在 MuJoCo 中执行现有 Task Program，验证真实杯子抬升、倾倒和放回，
 再启动独立 SPH Gym 回放其关节与杯子实测状态。输出包含源轨迹、源验收报告、
 流体接水量统计，以及 `videos/episode_0_fluid_gym_env0.mp4`、
 `videos/episode_0_fluid_gym_env1.mp4` 等每环境独立视频。
-默认使用 `hybrid`，也支持 `--renderer fast-rt`。
+流体阶段默认使用 `fast-rt`。当前原生液面重建只发布到 OptiX 场景，
+Hybrid 的 Vulkan 主表面看不到它，因此该组合会明确报错；空杯轨迹生成阶段
+仍可使用 Hybrid。
 录制通过环境 `record_camera` 事件调用现有 `record_camera_data`，
 配置 `per_env: true`、`fps: null`，每个控制步采集各 arena 的相对视角，
-按实际控制频率（本配置 24 fps）保存。脚本结束前显式保存最后一段视频。
+按实际控制频率（本配置 24 fps）保存。默认视角靠近杯口与接水碗，便于观察水流。
+脚本结束前显式保存最后一段视频。
 复用本工具生成的已验收轨迹可传
 `--trajectory outputs/fluid-task-program/task_program_trajectory.pt`。
 `--renderer no-render --seconds 0.125 --particle-spacing 0.006 --partial-reset-step 1`
@@ -51,7 +54,8 @@ spawn:
 其液面裁剪与固体壁面间隙分别处理；它不表示稳定后的体积百分比。
 1.5 mm 规则网格生成 76,607 个粒子，名义体积约 258.55 ml。
 修改 `particle_spacing` 或 `fill_level` 后，在下一次构建时重新生成点集。
-`--particle-spacing` 也使用同一截面重新采样，便于粗分辨率检查。
+`--particle-spacing` 也使用同一截面重新采样，并按间距比例缩放显示半径、
+液面网格间距和重建核半径，便于粗分辨率检查。
 
 局部点集只生成一次并保存在内存，reset 根据杯子位姿和 arena 偏移恢复选中环境；
 回放起始的静置阶段继续用于液体稳定化。NPZ 仍可作为外部点集导入格式，
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
index 54d1e6c4f..c28fbd2a2 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
@@ -1,7 +1,7 @@
 environment_id: pour_cup_fluid
 physics: newton
 render_cfg:
-  renderer: hybrid
+  renderer: fast-rt
 num_envs: 2
 arena_space: 2.5
 max_episode_steps: 2000
@@ -220,7 +220,7 @@ simulation:
         gpu_enabled: true
         cell_size: 0.0008100000000000001
         max_res: 512
-        kernel_radius: 0.001515
+        kernel_radius: 0.00225
         iso_density: 0.57
         density_smooth_iters: 2
         aabb_trim_fraction: 0.0
@@ -240,8 +240,8 @@ env:
         name: fluid_gym
         resolution: [640, 480]
         intrinsics: [550.0, 550.0, 320.0, 240.0]
-        eye: [-1.4, -1.4, 1.2]
-        target: [-0.43, -0.03, 0.47]
+        eye: [-1.15, -0.85, 0.95]
+        target: [-0.72, -0.12, 0.63]
         up: [0.0, 0.0, 1.0]
         max_env_num: 16
         per_env: true
diff --git a/scripts/tutorials/sim/franka_dfsph_pour_demo.py b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
index 14894661b..64d86b824 100644
--- a/scripts/tutorials/sim/franka_dfsph_pour_demo.py
+++ b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
@@ -187,7 +187,7 @@ def main() -> None:
     parser.add_argument("--fps", type=int, default=24)
     parser.add_argument("--physics-hz", type=int, default=1440)
     parser.add_argument("--substeps", type=int, default=1)
-    parser.add_argument("--renderer", choices=["rt", "hybrid"], default="rt")
+    parser.add_argument("--renderer", choices=["rt", "fast-rt"], default="rt")
     parser.add_argument("--headless", action="store_true")
     args = parser.parse_args()
     if min(args.speed, args.spacing, args.fps, args.substeps, args.physics_hz) <= 0:
diff --git a/tests/gym/envs/test_fluid_integration.py b/tests/gym/envs/test_fluid_integration.py
index 9a98fce08..c5dcee712 100644
--- a/tests/gym/envs/test_fluid_integration.py
+++ b/tests/gym/envs/test_fluid_integration.py
@@ -42,7 +42,7 @@
 
 def test_fluid_deployment_resolves_sources_and_observation_entities():
     cfg = config_to_cfg(load_config(TASK), source_path=TASK)
-    assert cfg.sim_cfg.render_cfg.renderer == "hybrid"
+    assert cfg.sim_cfg.render_cfg.renderer == "fast-rt"
     assert cfg.events.record_camera.params["per_env"] is True
     assert cfg.events.record_camera.params["fps"] is None
     assert cfg.events.record_camera.interval_step == 1
diff --git a/tests/lab/scripts/test_run_fluid_task_program.py b/tests/lab/scripts/test_run_fluid_task_program.py
index 92e7a4aa0..9b1c6f5c9 100644
--- a/tests/lab/scripts/test_run_fluid_task_program.py
+++ b/tests/lab/scripts/test_run_fluid_task_program.py
@@ -36,7 +36,7 @@
 )
 
 
-@pytest.mark.parametrize("renderer", [None, "hybrid", "fast-rt"])
+@pytest.mark.parametrize("renderer", [None, "fast-rt"])
 def test_launcher_selects_supported_renderers(renderer, monkeypatch, tmp_path):
     args = [
         "fluid",
@@ -52,15 +52,26 @@ def test_launcher_selects_supported_renderers(renderer, monkeypatch, tmp_path):
     replay = Mock()
     monkeypatch.setattr(runner, "_replay", replay)
     runner.main()
-    assert replay.call_args.args[0].renderer == (renderer or "hybrid")
+    assert replay.call_args.args[0].renderer == "fast-rt"
 
 
-def test_final_episode_is_committed_through_record_camera(monkeypatch, tmp_path):
+@pytest.mark.parametrize("spacing", [None, 0.003, 0.006])
+def test_final_episode_is_committed_through_record_camera(
+    spacing, monkeypatch, tmp_path
+):
     from embodichain.lab.gym.envs import fluid_replay
     from embodichain.lab.gym.envs.managers import observations
 
     recorder = SimpleNamespace(save_and_clear=Mock())
     created = []
+    decode_cfg = runner.config_to_cfg
+
+    def configured_render_settings(*args, **kwargs):
+        cfg = decode_cfg(*args, **kwargs)
+        cfg.sim_cfg.render_cfg.spp = 8
+        return cfg
+
+    monkeypatch.setattr(runner, "config_to_cfg", configured_render_settings)
 
     class Environment:
         def __init__(self, cfg, **kwargs):
@@ -103,7 +114,7 @@ def step(self, action):
         replay_task=str(TASK),
         num_envs=3,
         renderer="fast-rt",
-        particle_spacing=None,
+        particle_spacing=spacing,
         speed=2,
         seconds=None,
         partial_reset_step=None,
@@ -111,13 +122,23 @@ def step(self, action):
     runner._replay(args, tmp_path / "source.pt", tmp_path)
     recorder.save_and_clear.assert_called_once_with()
     cfg = created[0]
+    assert cfg.sim_cfg.render_cfg.spp == 8
     assert cfg.events.record_camera.params["max_env_num"] == 3
     assert cfg.events.record_camera.params["save_path"] == str(tmp_path / "videos")
     result = json.loads((tmp_path / "fluid_gym_result.json").read_text())
     assert result["renderer"] == "fast-rt"
     assert result["fluid_source"]["kind"] == "revolved_volume"
     assert result["fluid_source"]["file_path"] is None
-    assert result["particle_spacing_m"] == 0.0015
+    expected_spacing = spacing or 0.0015
+    assert result["particle_spacing_m"] == expected_spacing
+    fluid = cfg.fluid_object[0]
+    scale = expected_spacing / 0.0015
+    assert fluid.render.render_radius == pytest.approx(0.00045 * scale)
+    assert fluid.render.mesh_surface["cell_size"] == pytest.approx(0.00081 * scale)
+    assert fluid.render.mesh_surface["kernel_radius"] == pytest.approx(0.00225 * scale)
+    assert cfg.sim_cfg.physics_cfg.solver_cfg[
+        "particle_support_radius"
+    ] == pytest.approx(2.25 * expected_spacing)
     assert result["video_fps"] == 24
     assert result["videos"] == [
         f"videos/episode_0_fluid_gym_env{i}.mp4" for i in range(3)
@@ -137,9 +158,17 @@ def test_spacing_override_rejects_fixed_points_before_scene_creation(
     args = argparse.Namespace(
         replay_task=str(TASK),
         num_envs=2,
-        renderer="hybrid",
+        renderer="fast-rt",
         particle_spacing=0.006,
         speed=2,
     )
     with pytest.raises(ValueError, match="fixed sampling"):
         runner._replay(args, tmp_path / "unused.pt", tmp_path)
+
+
+def test_fluid_launcher_rejects_invisible_hybrid_backend(monkeypatch, capsys):
+    monkeypatch.setattr(sys, "argv", ["fluid", "--renderer", "hybrid"])
+    with pytest.raises(SystemExit) as error:
+        runner.main()
+    assert error.value.code == 2
+    assert "invalid choice" in capsys.readouterr().err
diff --git a/tests/sim/objects/test_fluid_visibility.py b/tests/sim/objects/test_fluid_visibility.py
new file mode 100644
index 000000000..1ffc1ca4b
--- /dev/null
+++ b/tests/sim/objects/test_fluid_visibility.py
@@ -0,0 +1,170 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""FastRT fluid primary visibility, independent of physical particle counts."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+import subprocess
+import sys
+
+import pytest
+
+pytestmark = [pytest.mark.gpu, pytest.mark.subprocess_sim]
+
+
+def test_fluid_surface_changes_record_camera_pixels_in_every_environment(tmp_path):
+    result = subprocess.run(
+        [sys.executable, str(Path(__file__).resolve()), str(tmp_path)],
+        capture_output=True,
+        text=True,
+        timeout=180,
+    )
+    assert result.returncode == 0, result.stdout + result.stderr
+    results = json.loads((tmp_path / "visibility.json").read_text())
+    assert results
+    for row in results:
+        assert row["visible_blue_pixels"] > 100
+        assert row["visible_blue_pixels"] > 4 * row["hidden_blue_pixels"]
+
+
+def _run_visibility_worker(output: Path) -> None:
+    import numpy as np
+    import torch
+    from PIL import Image
+    from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
+    from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
+    from embodichain.lab.sim.cfg import (
+        FluidObjectCfg,
+        FluidSpawnCfg,
+        FluidRenderCfg,
+        NewtonPhysicsCfg,
+        RenderCfg,
+    )
+    from embodichain.lab.sim.material import VisualMaterialCfg
+    from embodichain.lab.sim.sensors import CameraCfg
+
+    solver = ensure_solver_available("sph")
+    num_envs = 2 if getattr(solver, "supports_world_isolation", False) else 1
+    sim = SimulationManager(
+        SimulationManagerCfg(
+            headless=True,
+            physics_dt=1 / 1440,
+            device="cuda",
+            num_envs=num_envs,
+            arena_space=1.0,
+            render_cfg=RenderCfg(renderer="fast-rt", spp=8),
+            physics_cfg=NewtonPhysicsCfg(
+                num_substeps=1,
+                collision_cfg=None,
+                solver_cfg={
+                    "solver_type": "sph",
+                    "volume_map_boundary_enabled": False,
+                    "particle_support_radius": 0.00675,
+                    "pressure_relaxation": 0.2,
+                    "max_velocity": 2.0,
+                },
+            ),
+        )
+    )
+    fluid = camera = None
+    try:
+        fluid = sim.add_fluid_object(
+            FluidObjectCfg(
+                uid="visible_water",
+                particle_spacing=0.003,
+                init_pos=(0, 0, 0.15),
+                spawn=FluidSpawnCfg(kind="box", size=(0.06,) * 3),
+                render=FluidRenderCfg(
+                    mode="mesh_surface",
+                    render_radius=0.0009,
+                    clip_below_z=0.05,
+                    material=VisualMaterialCfg(
+                        base_color=[0.05, 0.35, 0.9, 1], transmission=0, roughness=0.1
+                    ),
+                    mesh_surface={
+                        "gpu_enabled": True,
+                        "cell_size": 0.00162,
+                        "max_res": 128,
+                        "kernel_radius": 0.0045,
+                        "iso_density": 0.57,
+                        "density_smooth_iters": 2,
+                        "min_component_tris": 32,
+                    },
+                ),
+            )
+        )
+        camera = sim.add_sensor(
+            CameraCfg(
+                uid="record",
+                width=320,
+                height=240,
+                intrinsics=(350, 350, 160, 120),
+                visualization_role="record",
+                extrinsics=CameraCfg.ExtrinsicsCfg(
+                    eye=(0.18, -0.24, 0.28), target=(0, 0, 0.15), up=(0, 0, 1)
+                ),
+            )
+        )
+        sim.prepare()
+        sim.update(step=2)
+
+        def capture():
+            sim.render_camera_group([camera.group_id])
+            camera.update(fetch_only=True)
+            return camera.get_data()["color"][..., :3].cpu().numpy().copy()
+
+        visible = capture()
+        before = fluid.data.particle_state_w
+        hidden_state = before.clone()
+        hidden_state[..., 0] += 10
+        fluid.write_particle_state_to_sim(hidden_state)
+        hidden = capture()
+        # Visibility captures do not advance physics; state restoration is exact.
+        fluid.write_particle_state_to_sim(before)
+        torch.testing.assert_close(fluid.data.particle_state_w, before, rtol=0, atol=0)
+
+        def blue_pixels(frame):
+            frame = frame.astype(np.int16)
+            return int(
+                (
+                    (frame[..., 2] > frame[..., 0] + 40)
+                    & (frame[..., 2] > frame[..., 1] + 20)
+                ).sum()
+            )
+
+        results = []
+        for env_id in range(num_envs):
+            Image.fromarray(visible[env_id]).save(output / f"visible_env{env_id}.png")
+            Image.fromarray(hidden[env_id]).save(output / f"hidden_env{env_id}.png")
+            results.append(
+                {
+                    "env_id": env_id,
+                    "visible_blue_pixels": blue_pixels(visible[env_id]),
+                    "hidden_blue_pixels": blue_pixels(hidden[env_id]),
+                }
+            )
+        (output / "visibility.json").write_text(json.dumps(results, indent=2) + "\n")
+    finally:
+        fluid = camera = None
+        sim.destroy(exit_process=False)
+        SimulationManager.flush_cleanup_queue()
+
+
+if __name__ == "__main__":
+    _run_visibility_worker(Path(sys.argv[1]))
diff --git a/tests/sim/test_fluid_cfg.py b/tests/sim/test_fluid_cfg.py
index 77ff9f368..d550b10d4 100644
--- a/tests/sim/test_fluid_cfg.py
+++ b/tests/sim/test_fluid_cfg.py
@@ -201,3 +201,23 @@ def test_profile_source_round_trip_rebuilds_without_a_particle_file():
 def test_points_reject_geometry_filters_instead_of_silently_ignoring_them(extra):
     with pytest.raises(ValueError, match="points sources"):
         FluidSpawnCfg(positions=[[0, 0, 0]], **extra).sample(0.001)
+
+
+def test_manager_rejects_hybrid_fluid_rendering_before_sampling():
+    from types import SimpleNamespace
+    import torch
+    from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
+    from embodichain.lab.sim.cfg import RenderCfg
+
+    manager = SimpleNamespace(
+        physics=SimpleNamespace(name="newton"),
+        device=torch.device("cuda"),
+        num_envs=1,
+        _active_newton_solver_type="sph",
+        spawn_result=None,
+        sim_config=SimulationManagerCfg(
+            physics_cfg=NewtonPhysicsCfg(), render_cfg=RenderCfg(renderer="hybrid")
+        ),
+    )
+    with pytest.raises(NotImplementedError, match="fast-rt.*Vulkan"):
+        SimulationManager.add_fluid_object(manager, FluidObjectCfg(uid="water"))

From d720fe68e16da09392da7297dafe7de9ad0802ff Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Wed, 14 Oct 2026 18:23:50 +0800
Subject: [PATCH 08/19] fix(sim): enforce fluid gate for Hybrid process
 renderers

---
 IMPLEMENTATION_PLAN.md                        |  10 +-
 .../topics/simulation-system/fluids.md        |   8 +-
 embodichain/lab/sim/sim_manager.py            |  17 +--
 tests/sim/test_fluid_cfg.py                   | 109 +++++++++++++++++-
 4 files changed, 129 insertions(+), 15 deletions(-)

diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
index d2e927a58..af796c175 100644
--- a/IMPLEMENTATION_PLAN.md
+++ b/IMPLEMENTATION_PLAN.md
@@ -311,7 +311,11 @@ OptiX mesh/sphere instances. Particle statistics and indirect image changes
 can therefore be nonzero while the liquid primary surface is missing.
 
 The fluid deployment and runner now select FastRT, and the public manager and
-runner reject unsupported Hybrid liquid rendering. Empty-cup source generation
+runner reject unsupported Hybrid liquid rendering. The declaration gate checks
+both the resolved configuration and the initialized process renderer, including
+all fluid render modes, before sampling, material creation or declaration.
+Existing Hybrid processes require a fresh process for FastRT/RT; pure physics
+uses NoRender. Empty-cup source generation
 continues using Hybrid. The record camera focuses on the cup and receiver; the
 mesh reconstruction kernel is 1.5 particle spacings. Spacing overrides resample
 the existing fill volume and also scale the explicit render radius, mesh cell
@@ -321,6 +325,10 @@ Task Program motion remain unchanged; no native binary change was needed.
 
 Validation:
 
+- Hybrid declaration gate, process-backend resolution, launcher and context
+  regression: **265 passed**. Coverage includes auto selection, reused Hybrid processes,
+  all fluid render modes and supported FastRT/RT/NoRender declarations. These
+  contract tests do not allocate GPU fluid state.
 - Focused config/launcher/Gym/context contracts: **80 passed**. Serial GPU camera
   regression: **1 passed**, including physical stepping, display clipping,
   hidden-fluid controls and exact state restoration. Blue foreground counts
diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
index 597773238..a4d2f22cd 100644
--- a/agent_context/topics/simulation-system/fluids.md
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -43,8 +43,12 @@ scene uses a profile source with no particle file or disk cache. Its existing
 stationary replay hold provides physical settling after direct initialization.
 
 Native fluid geometry requires FastRT (`fast-rt`) or OfflineRT (`rt`). The
-manager rejects Hybrid before sampling/declaration because the current bridge
-creates OptiX-only instances, absent from Hybrid's Vulkan primary surface.
+manager rejects Hybrid in both the resolved configuration and the initialized
+process renderer before sampling, material creation or declaration. The gate
+covers every fluid render mode: requesting FastRT in an existing Hybrid process
+cannot bypass it. Start a fresh process to change the initialized renderer;
+pure physics runs use NoRender. The current bridge creates OptiX-only instances,
+absent from Hybrid's Vulkan primary surface.
 Validate camera pixels with visible and hidden fluid, not only particle counts.
 Native rendering is published through the existing Newton render bridge.
 Display clipping acts only on copied positions; Viser consumes detached point
diff --git a/embodichain/lab/sim/sim_manager.py b/embodichain/lab/sim/sim_manager.py
index 053374585..30599a311 100644
--- a/embodichain/lab/sim/sim_manager.py
+++ b/embodichain/lab/sim/sim_manager.py
@@ -2750,12 +2750,19 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
         cfg.validate()
         cfg.__post_init__()
         if (
-            cfg.render.mode != "none"
-            and self.sim_config.render_cfg.renderer == "hybrid"
+            self.sim_config.render_cfg.renderer == "hybrid"
+            or SimulationManager._get_process_renderer() == "hybrid"
         ):
             raise NotImplementedError(
                 "Newton fluid rendering requires renderer='fast-rt' or 'rt'. "
-                "Hybrid does not publish the OptiX liquid geometry to its Vulkan primary surface."
+                "Hybrid does not publish the OptiX liquid geometry to its Vulkan primary surface. "
+                "Use renderer='no-render' for physics-only runs. "
+                "Select FastRT or OfflineRT before creating the first World; "
+                "start a new process if Hybrid is already initialized."
+            )
+        if cfg.render.mode == "none" and self.has_native_renderer:
+            raise NotImplementedError(
+                "Fluid mode='none' currently requires renderer='no-render'."
             )
         if not cfg.uid or cfg.uid in self.asset_uids:
             raise ValueError(
@@ -2791,10 +2798,6 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
                 )
             pose = _pose_from_cfg(reference.cfg) @ pose
         mesh_surface = FluidMeshSurfaceRenderDesc(**cfg.render.mesh_surface)
-        if cfg.render.mode == "none" and self.has_native_renderer:
-            raise NotImplementedError(
-                "Fluid mode='none' currently requires renderer='no-render'."
-            )
         material = None
         if self.has_native_renderer and cfg.render.material is not None:
             material_cfg = cfg.render.material.copy()
diff --git a/tests/sim/test_fluid_cfg.py b/tests/sim/test_fluid_cfg.py
index d550b10d4..8ccd409a6 100644
--- a/tests/sim/test_fluid_cfg.py
+++ b/tests/sim/test_fluid_cfg.py
@@ -203,21 +203,120 @@ def test_points_reject_geometry_filters_instead_of_silently_ignoring_them(extra)
         FluidSpawnCfg(positions=[[0, 0, 0]], **extra).sample(0.001)
 
 
-def test_manager_rejects_hybrid_fluid_rendering_before_sampling():
+def _fluid_manager_for_renderer(renderer):
     from types import SimpleNamespace
+    from unittest.mock import Mock
     import torch
-    from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
+    from embodichain.lab.sim import SimulationManagerCfg
     from embodichain.lab.sim.cfg import RenderCfg
 
-    manager = SimpleNamespace(
-        physics=SimpleNamespace(name="newton"),
+    return SimpleNamespace(
+        physics=SimpleNamespace(name="newton", configure_world=Mock()),
         device=torch.device("cuda"),
         num_envs=1,
         _active_newton_solver_type="sph",
         spawn_result=None,
         sim_config=SimulationManagerCfg(
-            physics_cfg=NewtonPhysicsCfg(), render_cfg=RenderCfg(renderer="hybrid")
+            headless=True,
+            physics_cfg=NewtonPhysicsCfg(),
+            render_cfg=RenderCfg(renderer=renderer),
         ),
+        has_native_renderer=renderer != "no-render",
+        asset_uids=[],
+        _fluid_objects={},
+        _rigid_objects={},
+        _spawn_scene=SimpleNamespace(declare=Mock()),
+        create_visual_material=Mock(),
+        _invalidate_fluid_state=Mock(),
+        notify_visualization_topology_changed=Mock(),
+    )
+
+
+@pytest.mark.parametrize("mode", ["particles", "mesh_surface", "none"])
+@pytest.mark.parametrize(
+    "requested,process_renderer",
+    [
+        ("hybrid", None),
+        ("fast-rt", "hybrid"),
+        ("rt", "hybrid"),
+        ("auto", "hybrid"),
+        ("no-render", "hybrid"),
+    ],
+)
+def test_manager_rejects_hybrid_fluid_rendering_before_sampling(
+    requested, process_renderer, mode, monkeypatch
+):
+    from unittest.mock import Mock
+    from embodichain.lab.sim import SimulationManager
+
+    monkeypatch.setattr(
+        SimulationManager, "_get_process_renderer", lambda: process_renderer
+    )
+    sample = Mock(side_effect=AssertionError("Fluid must be rejected before sampling"))
+    monkeypatch.setattr(FluidSpawnCfg, "sample", sample)
+    manager = _fluid_manager_for_renderer(requested)
+    cfg = FluidObjectCfg(uid="water", render={"mode": mode})
+    with pytest.raises(NotImplementedError, match="fast-rt.*Vulkan.*new process"):
+        SimulationManager.add_fluid_object(manager, cfg)
+    sample.assert_not_called()
+    manager.create_visual_material.assert_not_called()
+    manager._spawn_scene.declare.assert_not_called()
+
+
+@pytest.mark.parametrize("process_renderer", [None, "hybrid"])
+def test_auto_selected_hybrid_rejects_fluid_before_sampling(
+    process_renderer, monkeypatch, tmp_path
+):
+    from unittest.mock import Mock
+    from embodichain.lab.sim import SimulationManager, cfg as sim_cfg
+
+    monkeypatch.setattr(sim_cfg, "DEFAULT_RENDERER", "auto")
+    monkeypatch.setattr(
+        SimulationManager, "_get_process_renderer", lambda: process_renderer
+    )
+    monkeypatch.setattr(
+        "embodichain.lab.sim.utility.render_utils.select_default_renderer",
+        lambda _: "hybrid",
     )
+    manager = _fluid_manager_for_renderer("auto")
+    manager._requires_native_renderer = True
+    manager._material_cache_dir = tmp_path
+    SimulationManager._convert_sim_config(manager, manager.sim_config)
+    assert manager.sim_config.render_cfg.renderer == "hybrid"
+    sample = Mock(side_effect=AssertionError("Fluid must be rejected before sampling"))
+    monkeypatch.setattr(FluidSpawnCfg, "sample", sample)
     with pytest.raises(NotImplementedError, match="fast-rt.*Vulkan"):
         SimulationManager.add_fluid_object(manager, FluidObjectCfg(uid="water"))
+    sample.assert_not_called()
+
+
+@pytest.mark.parametrize(
+    "requested,process_renderer,mode",
+    [
+        ("fast-rt", None, "particles"),
+        ("fast-rt", "fast-rt", "mesh_surface"),
+        ("rt", "rt", "mesh_surface"),
+        ("no-render", "no-render", "none"),
+        ("no-render", "no-render", "particles"),
+    ],
+)
+def test_fluid_renderer_gate_allows_supported_backends(
+    requested, process_renderer, mode, monkeypatch
+):
+    from unittest.mock import Mock
+    from embodichain.lab.sim import SimulationManager
+
+    monkeypatch.setattr(
+        SimulationManager, "_get_process_renderer", lambda: process_renderer
+    )
+    facade = Mock()
+    monkeypatch.setattr(
+        "embodichain.lab.sim.sim_manager.FluidObject", Mock(return_value=facade)
+    )
+    manager = _fluid_manager_for_renderer(requested)
+    cfg = FluidObjectCfg(
+        uid="water", spawn=FluidSpawnCfg(kind="box"), render={"mode": mode}
+    )
+    assert SimulationManager.add_fluid_object(manager, cfg) is facade
+    assert manager._fluid_objects["water"] is facade
+    manager._spawn_scene.declare.assert_called_once()

From d6c7b39ada94207c1b8edf1c71a1da6e1c690224 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Wed, 14 Oct 2026 19:03:18 +0800
Subject: [PATCH 09/19] feat(data): host pouring task assets on Hugging Face

---
 IMPLEMENTATION_PLAN.md                        |    31 +
 agent_context/MAP.yaml                        |     2 +-
 .../topics/data-assets/data-assets.md         |     9 +-
 .../topics/env-framework/fluid-replay.md      |     4 +-
 docs/source/api_reference/public_api.rst      |    19 +
 embodichain/data/assets/__init__.py           |     4 +-
 embodichain/data/assets/task_assets.py        |    78 +
 embodichain/data/download.py                  |     1 +
 embodichain/lab/gym/utils/gym_utils.py        |    24 +-
 .../manipulation/tableware/pour_cup/README.md |    31 +-
 .../tableware/pour_cup/assets/cup.glb         |   Bin 23244 -> 0 bytes
 .../tableware/pour_cup/assets/fluid_floor.obj |    22 -
 .../tableware/pour_cup/assets/fluid_table.obj |    22 -
 .../tableware/pour_cup/assets/mug_wall.obj    |  1540 -
 .../pour_cup/assets/receiver_wall.obj         | 73732 ----------------
 .../tableware/pour_cup/env.fluid.yaml         |    10 +-
 .../tableware/pour_cup/env.newton.yaml        |     2 +-
 .../manipulation/tableware/pour_cup/env.yaml  |     2 +-
 .../sim/assets/franka_dfsph_pour/.gitignore   |     6 -
 .../sim/assets/franka_dfsph_pour/README.md    |    52 +-
 .../sim/assets/franka_dfsph_pour/close_23.png |   Bin 421745 -> 0 bytes
 .../assets/franka_dfsph_pour/environment.json |    30 -
 .../sim/assets/franka_dfsph_pour/frame_15.png |   Bin 750331 -> 0 bytes
 .../assets/franka_dfsph_pour/manifest.json    |    54 -
 .../franka_dfsph_pour/media_validation.json   |   439 -
 .../newton_smooth_validation.json             |    97 -
 .../assets/franka_dfsph_pour/particles_30.npz |   Bin 1655413 -> 0 bytes
 .../pr_smoke_validation.json                  |    73 -
 .../settling_comparison.json                  |    44 -
 .../assets/franka_dfsph_pour/trajectory.pt    |   Bin 301077 -> 0 bytes
 .../assets/franka_dfsph_pour/validation.json  |   446 -
 .../water_settling_closeup.mp4                |   Bin 1414937 -> 0 bytes
 .../tutorials/sim/franka_dfsph_pour_demo.py   |    18 +-
 setup.py                                      |     2 -
 tests/data/assets/test_task_assets.py         |   102 +
 tests/data/test_download_prefix.py            |    13 +-
 tests/gym/envs/test_fluid_integration.py      |    32 +-
 tests/gym/utils/test_gym_utils.py             |    37 +
 .../scripts/test_run_fluid_task_program.py    |    27 +
 tests/test_task_program_package_data.py       |    14 +-
 40 files changed, 415 insertions(+), 76604 deletions(-)
 create mode 100644 embodichain/data/assets/task_assets.py
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/cup.glb
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_floor.obj
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_table.obj
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/mug_wall.obj
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/receiver_wall.obj
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/.gitignore
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/close_23.png
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/environment.json
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/frame_15.png
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/manifest.json
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/media_validation.json
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/newton_smooth_validation.json
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/particles_30.npz
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/pr_smoke_validation.json
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/settling_comparison.json
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/trajectory.pt
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/validation.json
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/water_settling_closeup.mp4
 create mode 100644 tests/data/assets/test_task_assets.py

diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
index 37a6d9dbb..edc63c320 100644
--- a/IMPLEMENTATION_PLAN.md
+++ b/IMPLEMENTATION_PLAN.md
@@ -393,3 +393,34 @@ The earlier full physical replay and video qualification remain prior evidence;
 the full 31-second physical workload was not repeated for this Git integration.
 The DexSim batched-SPH release/pin and release/AOT qualification remain pending,
 so #777 stays Draft.
+
+## Downloadable task assets
+
+Task meshes and historical demo data are hosted in
+`DexForceAI/embodichain_data/task_assets` at HF commit
+`f13a1240d82d728cd0caf7550487a833c2b59368`. Runtime and demo downloads are
+separate: `PourCupAssets.zip` contains the five meshes, while
+`PourCupDemoData.zip` preserves the trajectory, reference snapshot, media and
+original validation records. Both use the standard `EmbodiChainDataset` locked
+cache, pinned archive MD5, configured mirror and official-Hub fallback, with a
+new `task` download-CLI category. The HF manifest records original file SHA-256
+and source commit `0f132ef68`.
+
+The Git tree and wheel no longer carry these payloads. YAML references use
+`PourCupAssets/...`; the standalone tutorial resolves its default trajectory
+lazily from `PourCupDemoData/trajectory.pt`. Configuration parsing resolves
+both collision and independent visual mesh assets, including rigid groups.
+Fluid initialization remains profile-based in memory; the archived final NPZ
+snapshot is historical evidence only.
+
+Verification includes an empty-cache download/extraction with all **20 file
+SHA-256 checks** and both ZIP MD5 checks matching their original source bytes;
+a cold wheel with the downloader and three environment configs but **zero pour
+mesh or demo payload files**; the real dual-environment FastRT runner with
+1,225 particles per row, exact untouched state at selected reset and separate
+1 / 3-frame recordings; **250 passing data/config/packaging/API/context checks**; and
+standalone tutorial syntax/help checks. Local transfer and cache reports are
+in `outputs/task-assets-hf/`, wheel validation in
+`outputs/task-assets-package-validation/`, and the live smoke in
+`outputs/task-assets-runtime-smoke/`. Historical wheel-with-mesh observations
+above describe the earlier implementation and are superseded by this migration.
diff --git a/agent_context/MAP.yaml b/agent_context/MAP.yaml
index 9e0610844..51b65af44 100644
--- a/agent_context/MAP.yaml
+++ b/agent_context/MAP.yaml
@@ -378,7 +378,7 @@ topics:
   title: Data Assets
   aliases: [data assets, data path, asset download, 数据资产, 资产下载, 数据路径]
   keywords: [get_data_path, get_data_class, EmbodiChainDataset, EMBODICHAIN_DATA_ROOT, EMBODICHAIN_DATASET_ROOT,
-    CATEGORY_MODULES, download lock, get_registry]
+    CATEGORY_MODULES, download lock, get_registry, task_assets, PourCupAssets, PourCupDemoData]
   paths: [topics/data-assets/data-assets.md]
   source_of_truth:
   - embodichain/data/dataset.py
diff --git a/agent_context/topics/data-assets/data-assets.md b/agent_context/topics/data-assets/data-assets.md
index 40233e576..dec418a1a 100644
--- a/agent_context/topics/data-assets/data-assets.md
+++ b/agent_context/topics/data-assets/data-assets.md
@@ -35,6 +35,9 @@ base URL for asset archives (default: the hf-mirror.com copy of
 The final nested asset path is not independently verified. A successful lookup
 can trigger download/extraction and still name a nonexistent nested file.
 Inspect `get_data_class()` and package imports when a newly added class is invisible.
+Gym `config_to_cfg()` applies the same resolver to file-backed `shape` and
+`visual_shape` meshes in backgrounds, rigid objects and rigid-object groups.
+An independent visual mesh must resolve through the cache before native creation.
 
 ## Download and registry boundaries
 
@@ -46,7 +49,11 @@ receive the project's locking/repair behavior.
 
 The CLI imports the fixed `CATEGORY_MODULES` and finds `DownloadDataset`
 subclasses defined in each module. Adding an asset module alone does not add a
-CLI category. `get_registry()` also appends public classes from
+CLI category. The `task` category exposes `assets/task_assets.py`: `PourCupAssets`
+provides runtime meshes and `PourCupDemoData` provides recorded demo inputs and
+evidence. They use `task_assets/<ClassName>.zip`, honor the configured prefix
+with a Hub fallback, and reuse the shared MD5-checked cache. Task configurations
+use class-prefixed paths; demo/media files are not wheel resources. `get_registry()` also appends public classes from
 `assets/locomotion_assets.py` to the robot category and excludes underscore-prefixed
 helper classes. These seven classes use `robot_assets/<ClassName>.zip` on the
 configured remote sources, with the model entry at the ZIP root.
diff --git a/agent_context/topics/env-framework/fluid-replay.md b/agent_context/topics/env-framework/fluid-replay.md
index 954e63bb8..0523d72b8 100644
--- a/agent_context/topics/env-framework/fluid-replay.md
+++ b/agent_context/topics/env-framework/fluid-replay.md
@@ -5,7 +5,9 @@ components own these alongside rigid entities; `config_to_cfg()` resolves
 fluid point files and explicit relative rigid/visual mesh paths against the
 owning component. Spawn rigid references are resolved before fluid binding.
 The default pour deployment generates its fill from a local revolved profile;
-no particle asset file is required. The runner's spacing override resamples the
+no particle asset file is required. Pouring meshes use `PourCupAssets/...`
+references resolved through the [data asset cache](../data-assets/data-assets.md),
+so installed task packages carry configuration without mesh payloads. The runner's spacing override resamples the
 same volume recipe and rejects fixed point imports. It scales explicit render
 radius, mesh cell size and reconstruction kernel radius by the spacing ratio,
 keeping the surface reconstruction consistent with the new particle density. Fill definitions and
diff --git a/docs/source/api_reference/public_api.rst b/docs/source/api_reference/public_api.rst
index 6630cd8d3..b2814e2a3 100644
--- a/docs/source/api_reference/public_api.rst
+++ b/docs/source/api_reference/public_api.rst
@@ -83,6 +83,25 @@ embodichain.data.assets.solver_assets
 
    download_neural_ik_checkpoint
 
+embodichain.data.assets.task_assets
+-----------------------------------
+
+Task mesh bundles and reference demonstration data downloaded through the shared
+asset cache. Runtime meshes and demo recordings are separate downloads so task
+initialization does not require video assets.
+
+.. currentmodule:: embodichain.data.assets.task_assets
+
+.. autosummary::
+   :nosignatures:
+
+   PourCupAssets
+   PourCupDemoData
+
+.. autoclass:: PourCupAssets
+
+.. autoclass:: PourCupDemoData
+
 embodichain.data_pipeline.depth_video
 -------------------------------------
 
diff --git a/embodichain/data/assets/__init__.py b/embodichain/data/assets/__init__.py
index c6c7cb00a..ede40878e 100644
--- a/embodichain/data/assets/__init__.py
+++ b/embodichain/data/assets/__init__.py
@@ -14,7 +14,7 @@
 # limitations under the License.
 # ----------------------------------------------------------------------------
 
-"""Preset configuration objects for scene assets (robots, end-effectors, objects, materials, sensors, planners, solvers, demo scenes) ready to reference from task configs."""
+"""Preset configuration objects for scene assets (robots, end-effectors, objects, materials, sensors, planners, solvers, demo scenes and task bundles) ready to reference from task configs."""
 
 from __future__ import annotations
 
@@ -29,3 +29,5 @@
 from .planner_assets import *
 
 from .locomotion_assets import *
+
+from .task_assets import *
diff --git a/embodichain/data/assets/task_assets.py b/embodichain/data/assets/task_assets.py
new file mode 100644
index 000000000..3cb73f8e5
--- /dev/null
+++ b/embodichain/data/assets/task_assets.py
@@ -0,0 +1,78 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Downloadable task meshes and reference demonstration data."""
+
+from __future__ import annotations
+
+import open3d as o3d
+
+from embodichain.data.constants import (
+    EMBODICHAIN_DEFAULT_DATA_ROOT,
+    EMBODICHAIN_DOWNLOAD_PREFIX,
+)
+from embodichain.data.dataset import EmbodiChainDataset
+
+__all__ = ["PourCupAssets", "PourCupDemoData"]
+
+_HUB_PREFIX = (
+    "https://huggingface.co/datasets/DexForceAI/embodichain_data/resolve/main/"
+)
+
+
+class _TaskAsset(EmbodiChainDataset):
+    """Download one task archive through the shared locked dataset cache."""
+
+    archive_md5: str
+
+    def __init__(self, data_root: str | None = None) -> None:
+        """Initialize and extract the task bundle.
+
+        Args:
+            data_root: Optional cache root overriding the EmbodiChain default.
+        """
+        prefix = type(self).__name__
+        filename = f"task_assets/{prefix}.zip"
+        urls = [f"{EMBODICHAIN_DOWNLOAD_PREFIX}{filename}"]
+        if EMBODICHAIN_DOWNLOAD_PREFIX != _HUB_PREFIX:
+            urls.append(f"{_HUB_PREFIX}{filename}")
+        descriptor = o3d.data.DataDescriptor(urls, self.archive_md5)
+        super().__init__(
+            prefix,
+            descriptor,
+            EMBODICHAIN_DEFAULT_DATA_ROOT if data_root is None else data_root,
+        )
+
+
+class PourCupAssets(_TaskAsset):
+    """Cup, fluid boundary, receiver, table and floor meshes for cup pouring.
+
+    Resolve individual meshes with ``get_data_path("PourCupAssets/cup.glb")``.
+    The bundle contains no initial-fluid particle file.
+    """
+
+    archive_md5 = "f97857b639189be4daf717a873ab84cf"
+
+
+class PourCupDemoData(_TaskAsset):
+    """Recorded pouring trajectory, videos and original validation evidence.
+
+    ``trajectory.pt`` is the MuJoCo recording used by the standalone pouring
+    tutorial. ``particles_30.npz`` is a final reference snapshot and is not used
+    to initialize fluids. Resolve files through ``get_data_path()``.
+    """
+
+    archive_md5 = "8eb96cb756ac08efe403417f50ad2bf2"
diff --git a/embodichain/data/download.py b/embodichain/data/download.py
index b7661d15f..cf29717a1 100644
--- a/embodichain/data/download.py
+++ b/embodichain/data/download.py
@@ -58,6 +58,7 @@
     "obj": "embodichain.data.assets.obj_assets",
     "robot": "embodichain.data.assets.robot_assets",
     "scene": "embodichain.data.assets.scene_assets",
+    "task": "embodichain.data.assets.task_assets",
     "w1": "embodichain.data.assets.w1_assets",
 }
 
diff --git a/embodichain/lab/gym/utils/gym_utils.py b/embodichain/lab/gym/utils/gym_utils.py
index 8530e04d8..11f6bc317 100644
--- a/embodichain/lab/gym/utils/gym_utils.py
+++ b/embodichain/lab/gym/utils/gym_utils.py
@@ -490,6 +490,16 @@ def config_to_cfg(
     from embodichain.utils import configclass
     from embodichain.data import get_data_path
 
+    def _resolve_mesh_asset_paths(obj_dict: dict[str, Any]) -> None:
+        for key in ("shape", "visual_shape"):
+            shape = obj_dict.get(key)
+            if (
+                shape is not None
+                and shape.get("shape_type") == "Mesh"
+                and shape.get("fpath") is not None
+            ):
+                shape["fpath"] = get_data_path(shape["fpath"])
+
     @configclass
     class ComponentCfg:
         """Configuration for env events.
@@ -749,9 +759,7 @@ class ComponentCfg:
     # parser background objects config
     if "background" in config:
         for obj_dict in config["background"]:
-            shape_type = obj_dict["shape"]["shape_type"]
-            if shape_type == "Mesh":
-                obj_dict["shape"]["fpath"] = get_data_path(obj_dict["shape"]["fpath"])
+            _resolve_mesh_asset_paths(obj_dict)
             # Set to static object if not specified.
             obj_dict["body_type"] = (
                 "static" if "body_type" not in obj_dict else obj_dict["body_type"]
@@ -762,9 +770,7 @@ class ComponentCfg:
     # parser scene objects config
     if "rigid_object" in config:
         for obj_dict in config["rigid_object"]:
-            shape_type = obj_dict["shape"]["shape_type"]
-            if shape_type == "Mesh":
-                obj_dict["shape"]["fpath"] = get_data_path(obj_dict["shape"]["fpath"])
+            _resolve_mesh_asset_paths(obj_dict)
             cfg = RigidObjectCfg.from_dict(obj_dict)
             env_cfg.rigid_object.append(cfg)
 
@@ -773,11 +779,7 @@ class ComponentCfg:
             if "folder_path" in obj_dict:
                 obj_dict["folder_path"] = get_data_path(obj_dict["folder_path"])
             for rigid_obj in obj_dict["rigid_objects"].values():
-                shape_type = rigid_obj["shape"]["shape_type"]
-                if shape_type == "Mesh" and "fpath" in rigid_obj["shape"]:
-                    rigid_obj["shape"]["fpath"] = get_data_path(
-                        rigid_obj["shape"]["fpath"]
-                    )
+                _resolve_mesh_asset_paths(rigid_obj)
             cfg = RigidObjectGroupCfg.from_dict(obj_dict)
             env_cfg.rigid_object_group.append(cfg)
 
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
index afec5116e..5ff49ecf1 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
@@ -35,7 +35,7 @@ Hybrid 的 Vulkan 主表面看不到它，因此该组合会明确报错；空
 用于低成本双环境局部重置检查；粗粒度检查不代表完整液体效果。
 
 `env.fluid.yaml` 拥有 SPH 参数、刚体边界、初始粒子和流体观测；
-`task.franka.fluid.yaml` 选择环境和机器人组件。杯子与边界网格随任务包发布，
+`task.franka.fluid.yaml` 选择环境和机器人组件。杯子与边界网格通过 `PourCupAssets` 下载并缓存，
 水体在构建场景时直接采样生成，默认不读取或保存粒子文件。
 求解器配置直接使用 DexSim SPHSolverCfg。
 
@@ -88,9 +88,9 @@ spawn:
 python scripts/tutorials/sim/franka_dfsph_pour_demo.py --headless
 ```
 
-脚本默认读取随本分支提交的
-[`trajectory.pt`](../../../../../../scripts/tutorials/sim/assets/franka_dfsph_pour/trajectory.pt)，
-不需要先生成轨迹。去掉 `--headless` 可打开原生窗口。
+脚本默认通过 `get_data_path("PourCupDemoData/trajectory.pt")` 下载并缓存
+已验证轨迹，不需要先生成。运行时杯子网格使用 `PourCupAssets/cup.glb`。
+去掉 `--headless` 可打开原生窗口。
 短时检查或指定自行生成的轨迹：
 
 ```bash
@@ -104,18 +104,19 @@ python scripts/tutorials/sim/franka_dfsph_pour_demo.py --headless \
 本机验证使用 Python 3.10、DexSim 0.5.1 本地 wheel、Newton 1.6.0、
 MuJoCo / MuJoCo-Warp 3.12.0、Warp 1.17.0 和 NVIDIA RTX 5090 D v2。
 还需要 Torch、TensorDict、NumPy、SciPy、Trimesh、OpenCV、ImageIO 和
-ImageIO-FFmpeg。完整版本记录见
-[`environment.json`](../../../../../../scripts/tutorials/sim/assets/franka_dfsph_pour/environment.json)。
+ImageIO-FFmpeg。完整版本记录可通过
+`get_data_path("PourCupDemoData/environment.json")` 获取。
 DexSim 必须包含原生 SPH、volume map、液体网格重建和 OptiX 支持；
 本地 wheel 和虚拟环境不随分支提交，也未更改项目的全局依赖。
-本演示针对源码 checkout，不承诺随 wheel 打包二进制资产。
+教程脚本针对源码 checkout；资产单独下载，wheel 仅携带代码与配置。
 
-Franka 模型沿用 EC 现有资产解析；杯子网格随任务提交，接水碗由脚本生成。
+Franka 模型沿用 EC 现有资产解析；杯子网格通过任务资产下载，接水碗由脚本生成。
 默认输出目录是 `outputs/franka_pour_cup/dfsph_stable_bowl/`，包括全景/近景
 MP4、每秒关键帧、粒子位置和速度 NPZ、参数及统计 `validation.json`。
 已录制的最终结果见
 [演示资料目录](../../../../../../scripts/tutorials/sim/assets/franka_dfsph_pour/README.md)，
-包含三段视频、原始轨迹、最终粒子快照和验证报告。
+`PourCupDemoData` 包含三段视频、原始轨迹、最终粒子快照和验证报告，
+均托管于 [Hugging Face task_assets](https://huggingface.co/datasets/DexForceAI/embodichain_data/tree/main/task_assets)。
 
 ## 场景和求解器
 
@@ -176,17 +177,15 @@ MP4、每秒关键帧、粒子位置和速度 NPZ、参数及统计 `validation.
 两份完整视频各 744 帧、31 秒，18 秒静置近景片段为 432 帧，均逐帧解码通过。
 31 份粒子快照的数量均为 76,538，位置和速度均有限。
 这些是一次完整运行和离散快照的结果，不代表批量成功率。
-已提交最终快照和全部统计，其他每秒快照可由运行脚本重新生成。
+下载包保留最终快照和全部统计，其他每秒快照可由运行脚本重新生成。
 
 ## 空杯任务及轨迹生成
 
-首次运行 YAML 部署前，把杯子放入本机资产缓存；若设置了
-`EMBODICHAIN_DATA_ROOT`，改用该目录：
+首次运行会自动下载并缓存任务网格，也可提前下载。
+`EMBODICHAIN_DATA_ROOT` 可覆盖缓存根目录：
 
 ```bash
-mkdir -p ~/.cache/embodichain_data/PourCupDemo
-cp embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/cup.glb \
-  ~/.cache/embodichain_data/PourCupDemo/cup.glb
+embodichain data download --name PourCupAssets
 ```
 
 Newton + MuJoCo-Warp：
@@ -218,7 +217,7 @@ Newton 配置使用 `mujoco_warp`、`solver: newton`、`implicitfast`，
 运动学继续使用 FEP IK 和 `ik_interp`，没有新增 IK 或动作实现。
 两种执行策略均为 `projected`，程序完成须结合实际杯子位姿和录像判断。
 
-随分支提交的 MuJoCo 轨迹来自固定种子 0 的一次完整成功运行，1160 帧、20 Hz，
+下载包中的 MuJoCo 轨迹来自固定种子 0 的一次完整成功运行，1160 帧、20 Hz，
 包含侧抓、抬升、倾倒、回正、放回和松爪。相对优化前布局，累计关节转动量
 从 19.23 rad 降到 6.03 rad（减少 68.7%），最大关节速度从 0.473 rad/s
 降到 0.163 rad/s。杯子最大抬升 11.98 cm、实际最大倾角 66.09°，
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/cup.glb b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/cup.glb
deleted file mode 100644
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diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_floor.obj b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_floor.obj
deleted file mode 100644
index cda0ffe0a..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_floor.obj
+++ /dev/null
@@ -1,22 +0,0 @@
-# https://github.com/mikedh/trimesh
-v -1.25000000 -1.25000000 -0.05000000
-v -1.25000000 -1.25000000 0.05000000
-v -1.25000000 1.25000000 -0.05000000
-v -1.25000000 1.25000000 0.05000000
-v 1.25000000 -1.25000000 -0.05000000
-v 1.25000000 -1.25000000 0.05000000
-v 1.25000000 1.25000000 -0.05000000
-v 1.25000000 1.25000000 0.05000000
-f 2 4 1
-f 5 2 1
-f 1 4 3
-f 3 5 1
-f 2 8 4
-f 6 2 5
-f 6 8 2
-f 4 8 3
-f 7 5 3
-f 3 8 7
-f 7 6 5
-f 8 6 7
-
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_table.obj b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_table.obj
deleted file mode 100644
index 8f42220e6..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/fluid_table.obj
+++ /dev/null
@@ -1,22 +0,0 @@
-# https://github.com/mikedh/trimesh
-v -0.21000000 -0.35000000 -0.25000000
-v -0.21000000 -0.35000000 0.25000000
-v -0.21000000 0.35000000 -0.25000000
-v -0.21000000 0.35000000 0.25000000
-v 0.21000000 -0.35000000 -0.25000000
-v 0.21000000 -0.35000000 0.25000000
-v 0.21000000 0.35000000 -0.25000000
-v 0.21000000 0.35000000 0.25000000
-f 2 4 1
-f 5 2 1
-f 1 4 3
-f 3 5 1
-f 2 8 4
-f 6 2 5
-f 6 8 2
-f 4 8 3
-f 7 5 3
-f 3 8 7
-f 7 6 5
-f 8 6 7
-
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/mug_wall.obj b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/mug_wall.obj
deleted file mode 100644
index ba21407af..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/mug_wall.obj
+++ /dev/null
@@ -1,1540 +0,0 @@
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diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/receiver_wall.obj b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/receiver_wall.obj
deleted file mode 100644
index ddc8d747b..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/receiver_wall.obj
+++ /dev/null
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-
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
index c28fbd2a2..521d6ed7d 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
@@ -63,7 +63,7 @@ simulation:
     body_type: kinematic
     shape:
       shape_type: Mesh
-      fpath: ./assets/fluid_table.obj
+      fpath: PourCupAssets/fluid_table.obj
       collision:
         approximation: sdf
         force_sdf: false
@@ -98,7 +98,7 @@ simulation:
     body_type: kinematic
     shape:
       shape_type: Mesh
-      fpath: ./assets/fluid_floor.obj
+      fpath: PourCupAssets/fluid_floor.obj
       collision:
         approximation: sdf
         force_sdf: false
@@ -124,7 +124,7 @@ simulation:
     body_type: kinematic
     shape:
       shape_type: Mesh
-      fpath: ./assets/mug_wall.obj
+      fpath: PourCupAssets/mug_wall.obj
       collision:
         approximation: sdf
         force_sdf: false
@@ -147,12 +147,12 @@ simulation:
         dynamic_friction: 0.2
     visual_shape:
       shape_type: Mesh
-      fpath: ./assets/cup.glb
+      fpath: PourCupAssets/cup.glb
   - uid: receiver
     body_type: kinematic
     shape:
       shape_type: Mesh
-      fpath: ./assets/receiver_wall.obj
+      fpath: PourCupAssets/receiver_wall.obj
       collision:
         approximation: sdf
         force_sdf: false
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
index 4a8fa4f92..2a1e9fa22 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
@@ -60,7 +60,7 @@ simulation:
     - uid: cup
       shape:
         shape_type: Mesh
-        fpath: ./assets/cup.glb
+        fpath: PourCupAssets/cup.glb
         compute_uv: true
         collision:
           approximation: convex_decomposition
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
index 17b38e068..001b92002 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
@@ -31,7 +31,7 @@ simulation:
     - uid: cup
       shape:
         shape_type: Mesh
-        fpath: ./assets/cup.glb
+        fpath: PourCupAssets/cup.glb
         compute_uv: true
         collision:
           approximation: convex_decomposition
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/.gitignore b/scripts/tutorials/sim/assets/franka_dfsph_pour/.gitignore
deleted file mode 100644
index 4a97e0644..000000000
--- a/scripts/tutorials/sim/assets/franka_dfsph_pour/.gitignore
+++ /dev/null
@@ -1,6 +0,0 @@
-!trajectory.pt
-!franka_dfsph_pour.mp4
-!water_closeup.mp4
-!water_settling_closeup.mp4
-!frame_15.png
-!close_23.png
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/README.md b/scripts/tutorials/sim/assets/franka_dfsph_pour/README.md
index 28448ce09..15560102c 100644
--- a/scripts/tutorials/sim/assets/franka_dfsph_pour/README.md
+++ b/scripts/tutorials/sim/assets/franka_dfsph_pour/README.md
@@ -1,41 +1,23 @@
-# 已验证的 Franka DFSPH 演示资料
+# Franka DFSPH 演示资料
 
-运行说明和边界条件见
-[任务 README](../../../../../embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md)。
-此目录约 31 MB，为 draft 提供可直接回放的输入和可检查的最终输出。
+轨迹、参考快照、录像、预览图和验证报告已迁移到
+[Hugging Face task assets](https://huggingface.co/datasets/DexForceAI/embodichain_data/tree/main/task_assets)，不再随代码仓库提交。
 
-| 文件 | 用途 |
-| --- | --- |
-| `trajectory.pt` | MuJoCo 实际机器人/杯子状态，1160 帧、20 Hz；脚本默认输入 |
-| `franka_dfsph_pour.mp4` | 完整全景，31 秒、744 帧 |
-| `water_closeup.mp4` | 完整杯口/水流/碗近景，31 秒、744 帧 |
-| `water_settling_closeup.mp4` | 近景第 12–30 秒片段，18 秒、432 帧 |
-| `frame_15.png` / `close_23.png` | 倾倒全景与静置后液面 |
-| `particles_30.npz` | 最后时刻 30.975 秒的全部粒子位置、速度、杯位姿 |
-| `validation.json` | DFSPH 参数及全程 32 组物理统计 |
-| `media_validation.json` | 三段视频逐帧解码、31 份原始快照的有限值和数量检查 |
-| `newton_smooth_validation.json` | 轨迹平滑前后指标与原 MuJoCo 任务完成元数据 |
-| `settling_comparison.json` | 相同初始快照的两组静水对照 |
-| `environment.json` | 验证环境版本与提交来源 |
-| `pr_smoke_validation.json` | 当前分支 2 秒 GPU 短测，76,538 粒，容器外粒子数为 0 |
-| `manifest.json` | 资料大小和 SHA-256 |
+```bash
+embodichain data download --name PourCupDemoData
+```
 
-原始报告保留录制机器的路径作为来源记录，不用作运行输入。
-除最终粒子快照和两张预览外，其余每秒快照/图片未提交；可运行脚本重新生成。
-`media_validation.json` 记录的是当时所有 31 份原始快照的检查结果。
+```python
+from pathlib import Path
+from embodichain.data import get_data_path
 
-轨迹和完整录像生成于 EC 提交 `3bb2a03c5`，使用本机 DexSim 0.5.1 wheel。
-Draft 基于 `453675d8a`，配置按当前 `eef` 命名调整；此次基线的静态检查与
-GPU 短测另记在 PR。这里的原录像不是新基线重新完整运行的声明。
+trajectory = Path(get_data_path("PourCupDemoData/trajectory.pt"))
+print(trajectory.parent)  # 同目录包含录像、参考快照及原始验证报告
+```
 
-`trajectory.pt` 是本演示自产的 Torch / TensorDict 序列化文件，包含
-`states`、`actions` 和 `meta`。液体脚本读取 `meta.dt`、机器人 9 维 `qpos`
-和杯子 7 维位姿（XYZ + XYZW），不会依赖原机器的绝对路径。
+运行时网格单独使用 `PourCupAssets`，任务无需下载演示录像。
+资产包提供 MD5 校验，远端 `task_assets/manifest.json` 记录每个文件的 SHA-256。
+`particles_30.npz` 仅为历史最终状态参考，不用于初始化水体；默认水体仍在内存中采样。
 
-单向耦合：机械臂和杯子为轨迹回放，水为真实 DFSPH 粒子仿真。
-最终约 140.38 mL 进入碗、117.11 mL 留在杯中、0.83 mL 在容器外。
-台面以下粒子只从渲染副本过滤，物理和统计仍保留全部 76,538 粒。
-
-![倾倒全景](frame_15.png)
-
-![倾倒后的碗内液面](close_23.png)
+报告保留原录制环境和提交来源；它们不是新版本完整复跑的声明。
+运行方式见[任务 README](../../../../../embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md)。
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/close_23.png b/scripts/tutorials/sim/assets/franka_dfsph_pour/close_23.png
deleted file mode 100644
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diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/environment.json b/scripts/tutorials/sim/assets/franka_dfsph_pour/environment.json
deleted file mode 100644
index 07d96955e..000000000
--- a/scripts/tutorials/sim/assets/franka_dfsph_pour/environment.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "python": "3.10.21",
-  "os": "Linux-6.8.0-117-generic-x86_64-with-glibc2.35",
-  "gpu": "NVIDIA GeForce RTX 5090 D v2",
-  "recorded_on_embodichain_commit": "3bb2a03c5500350116d37cf876590f3ec12accc4",
-  "draft_base_commit": "453675d8ab7a1f7ae0564ae40db80d9c707a30f4",
-  "packages": {
-    "dexsim-engine": "0.5.1",
-    "newton": "1.6.0",
-    "mujoco": "3.12.0",
-    "mujoco-warp": "3.12.0",
-    "warp-lang": "1.17.0",
-    "torch": "2.14.0",
-    "tensordict": "0.14.2",
-    "numpy": "2.2.6",
-    "scipy": "1.15.3",
-    "trimesh": "4.12.2",
-    "imageio": "2.38.0",
-    "imageio-ffmpeg": "0.6.0",
-    "opencv-python": "5.0.0.93",
-    "viser": "1.0.21",
-    "black": "26.3.1",
-    "pytest": "9.1.1"
-  },
-  "notes": [
-    "DexSim is the locally supplied wheel; no wheel or virtual environment is included.",
-    "Videos and reports were recorded before rebasing the demo onto the draft base.",
-    "Current-base compatibility checks are reported separately in the pull request."
-  ]
-}
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/frame_15.png b/scripts/tutorials/sim/assets/franka_dfsph_pour/frame_15.png
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deleted file mode 100644
index 37a5b9d77..000000000
--- a/scripts/tutorials/sim/assets/franka_dfsph_pour/media_validation.json
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diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/newton_smooth_validation.json b/scripts/tutorials/sim/assets/franka_dfsph_pour/newton_smooth_validation.json
deleted file mode 100644
index 4beb6dc40..000000000
--- a/scripts/tutorials/sim/assets/franka_dfsph_pour/newton_smooth_validation.json
+++ /dev/null
@@ -1,97 +0,0 @@
-{
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-  "solver_type": "mujoco_warp",
-  "constraint_solver": "newton",
-  "motion_strategy": "ik_interp",
-  "sample_count": 360,
-  "seed": 0,
-  "dt": 0.05,
-  "state_frames": 1160,
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-    "width": 1280,
-    "height": 960
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-  "keyframes": {
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-    "grasp": 230,
-    "lift": 380,
-    "pour": 570,
-    "return": 1159
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diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/pr_smoke_validation.json b/scripts/tutorials/sim/assets/franka_dfsph_pour/pr_smoke_validation.json
deleted file mode 100644
index 8ec1725a1..000000000
--- a/scripts/tutorials/sim/assets/franka_dfsph_pour/pr_smoke_validation.json
+++ /dev/null
@@ -1,73 +0,0 @@
-{
-  "physics": "Newton SPH / DFSPH",
-  "rigid_motion": "kinematic replay of verified MuJoCo trajectory",
-  "coupling": "one-way moving volume-map boundaries; no fluid reaction on robot",
-  "boundary": {
-    "method": "volume_map",
-    "volume_map_enabled": true,
-    "sdf_penalty_enabled": false,
-    "volume_map_resolution": 192,
-    "boundary_shape_count": 4,
-    "rest_density_kg_m3": 1000.0,
-    "viscosity": 0.0006
-  },
-  "bowl": {
-    "center_m": [
-      -0.755,
-      -0.137,
-      0.5
-    ],
-    "outer_diameter_m": 0.18894443627691185,
-    "height_m": 0.066,
-    "shape": "curved ceramic bowl with flat foot"
-  },
-  "trajectory": "/home/oem/.codex/worktrees/franka-dfsph-pour-demo/EmbodiChain/scripts/tutorials/sim/assets/franka_dfsph_pour/trajectory.pt",
-  "particle_count": 76538,
-  "spacing_m": 0.0015,
-  "source_start_time_s": 0.0,
-  "rest_density_kg_m3": 1000.0,
-  "viscosity": 0.0006,
-  "surface_tension_coefficient": 0.002,
-  "render_aabb_trim_fraction": 0.0,
-  "render_only_cull_below_z_m": 0.49,
-  "nominal_volume_ml": 258.31575,
-  "simulation_dt": 0.0006944444444444445,
-  "substeps": 1,
-  "duration_s": 2.0,
-  "wall_seconds": 21.189388323997264,
-  "samples": [
-    {
-      "in_mug": 76538,
-      "in_receiver": 0,
-      "below_table": 0,
-      "outside_containers": 0,
-      "received_ml": 0.0,
-      "min_z": 0.5154969692230225,
-      "time_s": 0.0006944444444444445,
-      "speed_p99_m_s": 0.09959307312965393,
-      "density_p99_kg_m3": 1011.4403076171875
-    },
-    {
-      "in_mug": 76538,
-      "in_receiver": 0,
-      "below_table": 0,
-      "outside_containers": 0,
-      "received_ml": 0.0,
-      "min_z": 0.5096086859703064,
-      "time_s": 1.0006944444444446,
-      "speed_p99_m_s": 0.0166236013174057,
-      "density_p99_kg_m3": 1014.6708374023438
-    },
-    {
-      "in_mug": 76538,
-      "in_receiver": 0,
-      "below_table": 0,
-      "outside_containers": 0,
-      "received_ml": 0.0,
-      "min_z": 0.5096070170402527,
-      "time_s": 2.0,
-      "speed_p99_m_s": 0.016581816598773003,
-      "density_p99_kg_m3": 1014.6282958984375
-    }
-  ]
-}
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/settling_comparison.json b/scripts/tutorials/sim/assets/franka_dfsph_pour/settling_comparison.json
deleted file mode 100644
index 8f8e19112..000000000
--- a/scripts/tutorials/sim/assets/franka_dfsph_pour/settling_comparison.json
+++ /dev/null
@@ -1,44 +0,0 @@
-{
-  "experiment": "same saved post-pour particle positions, initially zero velocity, stationary bowl and final robot pose",
-  "duration_s": 5,
-  "initial_snapshot": "outputs/franka_pour_cup/dfsph_volume_map/particles_30.npz",
-  "baseline": {
-    "viscosity": 0.00025,
-    "surface_tension": 0.02,
-    "volume_map_resolution": 96,
-    "final": {
-      "in_mug": 40960,
-      "in_receiver": 33850,
-      "below_table": 1728,
-      "outside_containers": 1728,
-      "received_ml": 114.24374999999999,
-      "min_z": -45.27298355102539,
-      "bowl_speed_rms_m_s": 0.13404083251953125,
-      "bowl_speed_p95_m_s": 0.22384269535541534,
-      "bowl_height_p99_m": 0.02481362521648407,
-      "time_s": 5.0,
-      "speed_p99_m_s": 2.0,
-      "density_p99_kg_m3": 1023.318115234375
-    }
-  },
-  "revised": {
-    "viscosity": 0.0006,
-    "surface_tension": 0.002,
-    "volume_map_resolution": 192,
-    "final": {
-      "in_mug": 40960,
-      "in_receiver": 33850,
-      "below_table": 1728,
-      "outside_containers": 1728,
-      "received_ml": 114.24374999999999,
-      "min_z": -45.27298355102539,
-      "bowl_speed_rms_m_s": 0.004749146290123463,
-      "bowl_speed_p95_m_s": 0.008076288737356663,
-      "bowl_height_p99_m": 0.02113095223903656,
-      "time_s": 5.0,
-      "speed_p99_m_s": 2.0,
-      "density_p99_kg_m3": 1009.6124267578125
-    }
-  },
-  "bowl_speed_rms_reduction_percent": 96.45694061961943
-}
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/trajectory.pt b/scripts/tutorials/sim/assets/franka_dfsph_pour/trajectory.pt
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diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/validation.json b/scripts/tutorials/sim/assets/franka_dfsph_pour/validation.json
deleted file mode 100644
index 2da102288..000000000
--- a/scripts/tutorials/sim/assets/franka_dfsph_pour/validation.json
+++ /dev/null
@@ -1,446 +0,0 @@
-{
-  "physics": "Newton SPH / DFSPH",
-  "rigid_motion": "kinematic replay of verified MuJoCo trajectory",
-  "coupling": "one-way moving volume-map boundaries; no fluid reaction on robot",
-  "boundary": {
-    "method": "volume_map",
-    "volume_map_enabled": true,
-    "sdf_penalty_enabled": false,
-    "volume_map_resolution": 192,
-    "boundary_shape_count": 4,
-    "rest_density_kg_m3": 1000.0,
-    "viscosity": 0.0006
-  },
-  "bowl": {
-    "center_m": [
-      -0.755,
-      -0.137,
-      0.5
-    ],
-    "outer_diameter_m": 0.18894443627691185,
-    "height_m": 0.066,
-    "shape": "curved ceramic bowl with flat foot"
-  },
-  "trajectory": "/home/oem/EmbodiChain/outputs/franka_pour_cup/newton_smooth_mug_trajectories/traj_env0_000000.pt",
-  "particle_count": 76538,
-  "spacing_m": 0.0015,
-  "source_start_time_s": 0.0,
-  "rest_density_kg_m3": 1000.0,
-  "viscosity": 0.0006,
-  "surface_tension_coefficient": 0.002,
-  "render_aabb_trim_fraction": 0.0,
-  "render_only_cull_below_z_m": 0.49,
-  "nominal_volume_ml": 258.31575,
-  "simulation_dt": 0.0006944444444444445,
-  "substeps": 1,
-  "duration_s": 30.975,
-  "wall_seconds": 363.6709657689935,
-  "samples": [
-    {
-      "in_mug": 76538,
-      "in_receiver": 0,
-      "below_table": 0,
-      "outside_containers": 0,
-      "received_ml": 0.0,
-      "min_z": 0.5154969692230225,
-      "time_s": 0.0006944444444444445,
-      "speed_p99_m_s": 0.09959307312965393,
-      "density_p99_kg_m3": 1011.4403076171875
-    },
-    {
-      "in_mug": 76538,
-      "in_receiver": 0,
-      "below_table": 0,
-      "outside_containers": 0,
-      "received_ml": 0.0,
-      "min_z": 0.5096086859703064,
-      "time_s": 1.0006944444444446,
-      "speed_p99_m_s": 0.0166236013174057,
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diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/water_settling_closeup.mp4 b/scripts/tutorials/sim/assets/franka_dfsph_pour/water_settling_closeup.mp4
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z1{MD^EZ+?-E^$jc9g4}rtczy%RG@*`SwXhl=H)36fMHVa_11gOOtnQC!CB-*4!3i$
zND8yN+9#~7hffiAT%IBRte9$7ZT08%&D|ShP-af5OppXtbibH05x5Q$26my76&OrD
zV@aM#@$7{XcNnR~OxyX0sO2dZZG+zXE*;OQ-kI;>8j=J?V<b74Tyz9^&9YK)(G?pX
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zGDTaX$e*H_{s6s+{9?cqIO%QQy*3onnWq0?{9Yl<tbeX^3<#Pwq#Cpq`g*sc=Hvf$
zJ?ualkWme0_I2Th<(GKfZjft6N6)z<vPB~{W$%JBq1x1FjV1~jVv&Gu;Q3^0wyTi#
Oy9@6V4&lr}<~nuq9V@2*

diff --git a/scripts/tutorials/sim/franka_dfsph_pour_demo.py b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
index c69d8ad76..b8962e631 100644
--- a/scripts/tutorials/sim/franka_dfsph_pour_demo.py
+++ b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
@@ -48,6 +48,7 @@
     FluidSpawnCfg,
     FluidRenderCfg,
 )
+from embodichain.data import get_data_path
 from embodichain.lab.sim.material import VisualMaterialCfg
 from embodichain.lab.visualization import VisualizationCfg
 from embodichain_robots import FrankaPandaCfg
@@ -56,10 +57,8 @@
 __all__ = ["main"]
 
 ROOT = Path(__file__).resolve().parents[3]
-DEFAULT_TRAJECTORY = Path(__file__).parent / "assets/franka_dfsph_pour/trajectory.pt"
-CUP_ASSET = ROOT / (
-    "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/cup.glb"
-)
+DEFAULT_TRAJECTORY = "PourCupDemoData/trajectory.pt"
+CUP_ASSET = "PourCupAssets/cup.glb"
 RECEIVER_CENTER = np.array([-0.755, -0.137, 0.50])
 BOWL_OUTER_RADIUS = 0.097
 BOWL_INNER_RADIUS = 0.0915
@@ -171,7 +170,11 @@ def _particle_summary(
 def main() -> None:
     """Replay the arm and simulate water in the same EmbodiChain world."""
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--trajectory", type=Path, default=DEFAULT_TRAJECTORY)
+    parser.add_argument(
+        "--trajectory",
+        type=Path,
+        help=f"Recorded trajectory path; defaults to cached {DEFAULT_TRAJECTORY}.",
+    )
     parser.add_argument(
         "--output",
         type=Path,
@@ -193,6 +196,9 @@ def main() -> None:
     if min(args.speed, args.spacing, args.fps, args.substeps, args.physics_hz) <= 0:
         parser.error("speed, spacing, fps, substeps and physics-hz must be positive")
     args.output.mkdir(parents=True, exist_ok=True)
+    if args.trajectory is None:
+        args.trajectory = Path(get_data_path(DEFAULT_TRAJECTORY))
+    cup_asset = Path(get_data_path(CUP_ASSET))
     data = torch.load(args.trajectory, map_location="cpu", weights_only=False)
     joints = data["states"]["robot", "qpos"][0].numpy()
     cup_states = data["states"]["rigid_objects", "cup", "pose"][0].numpy()
@@ -320,7 +326,7 @@ def main() -> None:
             sections=128,
         )
         mug = _add_boundary(
-            sim, "liquid_mug", mug_mesh, initial_pose, visual_asset=CUP_ASSET
+            sim, "liquid_mug", mug_mesh, initial_pose, visual_asset=cup_asset
         )
         if args.substeps > 1:
             boundary_times = (
diff --git a/setup.py b/setup.py
index 758ed6890..a38667cc2 100644
--- a/setup.py
+++ b/setup.py
@@ -151,8 +151,6 @@ def main():
                 "**/*.yaml",
                 "**/*.yml",
                 "**/README.md",
-                "tasks/manipulation/tableware/pour_cup/assets/*.obj",
-                "tasks/manipulation/tableware/pour_cup/assets/*.glb",
             ],
         },
         cmdclass=cmdclass,
diff --git a/tests/data/assets/test_task_assets.py b/tests/data/assets/test_task_assets.py
new file mode 100644
index 000000000..65bdfce98
--- /dev/null
+++ b/tests/data/assets/test_task_assets.py
@@ -0,0 +1,102 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Task-asset descriptors, cache selection and public registry contracts."""
+
+from __future__ import annotations
+
+from argparse import Namespace
+from pathlib import Path
+
+import open3d as o3d
+import pytest
+
+from embodichain.data import download
+from embodichain.data.assets import task_assets
+from embodichain.data.dataset import EmbodiChainDataset, get_data_class
+
+pytestmark = pytest.mark.no_sim
+
+_HUB = "https://huggingface.co/datasets/DexForceAI/embodichain_data/resolve/main/"
+_ASSETS = (
+    ("PourCupAssets", "f97857b639189be4daf717a873ab84cf"),
+    ("PourCupDemoData", "8eb96cb756ac08efe403417f50ad2bf2"),
+)
+
+
+@pytest.mark.parametrize("name,md5", _ASSETS)
+@pytest.mark.parametrize(
+    "prefix", [_HUB, "https://hf-mirror.com/assets/", "https://example.test/"]
+)
+@pytest.mark.parametrize("custom_root", [False, True])
+def test_task_archive_descriptor_uses_configured_source_and_locked_cache(
+    name: str,
+    md5: str,
+    prefix: str,
+    custom_root: bool,
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    calls = []
+
+    class DownloadIntercepted(Exception):
+        """Stop before the Open3D base performs network I/O."""
+
+    def initialize(
+        self: EmbodiChainDataset,
+        asset: str,
+        descriptor: o3d.data.DataDescriptor,
+        root: str,
+    ) -> None:
+        calls.append((asset, descriptor, root))
+        raise DownloadIntercepted
+
+    default = str(tmp_path / "default")
+    override = str(tmp_path / "custom") if custom_root else None
+    monkeypatch.setattr(task_assets, "EMBODICHAIN_DEFAULT_DATA_ROOT", default)
+    monkeypatch.setattr(task_assets, "EMBODICHAIN_DOWNLOAD_PREFIX", prefix)
+    monkeypatch.setattr(EmbodiChainDataset, "__init__", initialize)
+    with pytest.raises(DownloadIntercepted):
+        getattr(task_assets, name)(data_root=override)
+    assert len(calls) == 1
+    asset, descriptor, root = calls[0]
+    assert asset == name
+    assert root == (override or default)
+    relative = f"task_assets/{name}.zip"
+    assert descriptor.urls == [f"{prefix}{relative}"] + (
+        [] if prefix == _HUB else [f"{_HUB}{relative}"]
+    )
+    assert descriptor.md5 == md5
+
+
+@pytest.mark.parametrize("name,_", _ASSETS)
+def test_task_assets_are_shared_by_config_and_cli_registry(
+    name: str, _: str, monkeypatch: pytest.MonkeyPatch, capsys
+) -> None:
+    registry = download.get_registry()
+    cls = get_data_class(name)
+    assert dict(registry["task"])[name] is cls
+    assert all(not key.startswith("_") for key, _ in registry["task"])
+    download.cmd_list(Namespace(category="task"))
+    assert name in capsys.readouterr().out
+    calls = []
+    monkeypatch.setattr(
+        download,
+        "download_asset",
+        lambda key, value: calls.append((key, value)) or True,
+    )
+    download.cmd_download(Namespace(all=False, category=None, name=name.lower()))
+    assert calls == [(name.lower(), cls)]
diff --git a/tests/data/test_download_prefix.py b/tests/data/test_download_prefix.py
index 11422e10a..2eda5ea25 100644
--- a/tests/data/test_download_prefix.py
+++ b/tests/data/test_download_prefix.py
@@ -36,7 +36,7 @@
 _COLLECT_URLS = """
 import json
 from embodichain.data import dataset
-from embodichain.data.assets import CobotMagicArm, UnitreeGo2Locomotion
+from embodichain.data.assets import CobotMagicArm, UnitreeGo2Locomotion, PourCupAssets, PourCupDemoData
 
 urls = {}
 
@@ -48,7 +48,7 @@ def capture(self, prefix, descriptor, path):
     raise Captured  # stop before anything is downloaded
 
 dataset.EmbodiChainDataset.__init__ = capture
-for cls in (CobotMagicArm, UnitreeGo2Locomotion):
+for cls in (CobotMagicArm, UnitreeGo2Locomotion, PourCupAssets, PourCupDemoData):
     try:
         cls()
     except Captured:
@@ -107,6 +107,11 @@ def test_download_prefix_gets_a_trailing_slash(monkeypatch, reload_constants) ->
 def test_assets_download_from_the_configured_prefix(prefix: str) -> None:
     urls = _consumer_urls(prefix)
     assert urls["CobotMagicArm"] == [f"{MIRROR}robot_assets/CobotMagicArmV4.zip"]
+    for name in ("PourCupAssets", "PourCupDemoData"):
+        assert urls[name] == [
+            f"{MIRROR}task_assets/{name}.zip",
+            f"{HF_PREFIX}task_assets/{name}.zip",
+        ]
     # Locomotion tries the configured source first and keeps the Hub as fallback.
     assert urls["UnitreeGo2Locomotion"] == [
         f"{MIRROR}robot_assets/UnitreeGo2Locomotion.zip",
@@ -115,8 +120,10 @@ def test_assets_download_from_the_configured_prefix(prefix: str) -> None:
 
 
 @pytest.mark.no_sim
-def test_locomotion_does_not_repeat_the_hub_url() -> None:
+def test_assets_do_not_repeat_the_hub_url() -> None:
     urls = _consumer_urls(HF_PREFIX)
     assert urls["UnitreeGo2Locomotion"] == [
         f"{HF_PREFIX}robot_assets/UnitreeGo2Locomotion.zip"
     ]
+    for name in ("PourCupAssets", "PourCupDemoData"):
+        assert urls[name] == [f"{HF_PREFIX}task_assets/{name}.zip"]
diff --git a/tests/gym/envs/test_fluid_integration.py b/tests/gym/envs/test_fluid_integration.py
index c5dcee712..67016e99e 100644
--- a/tests/gym/envs/test_fluid_integration.py
+++ b/tests/gym/envs/test_fluid_integration.py
@@ -40,7 +40,34 @@
 )
 
 
-def test_fluid_deployment_resolves_sources_and_observation_entities():
+@pytest.fixture(autouse=True)
+def task_mesh_cache(tmp_path, monkeypatch):
+    """Resolve task meshes locally so configuration tests never download them."""
+    from types import SimpleNamespace
+    from embodichain.data import dataset
+
+    directory = tmp_path / "PourCupAssets"
+    directory.mkdir()
+    for name in (
+        "cup.glb",
+        "mug_wall.obj",
+        "receiver_wall.obj",
+        "fluid_table.obj",
+        "fluid_floor.obj",
+    ):
+        (directory / name).touch()
+    original = dataset.get_data_class
+
+    def resolve(name, extra_modules=None):
+        if name == "PourCupAssets":
+            return lambda: SimpleNamespace(extract_dir=str(directory))
+        return original(name, extra_modules)
+
+    monkeypatch.setattr(dataset, "get_data_class", resolve)
+    return directory
+
+
+def test_fluid_deployment_resolves_sources_and_observation_entities(task_mesh_cache):
     cfg = config_to_cfg(load_config(TASK), source_path=TASK)
     assert cfg.sim_cfg.render_cfg.renderer == "fast-rt"
     assert cfg.events.record_camera.params["per_env"] is True
@@ -58,7 +85,8 @@ def test_fluid_deployment_resolves_sources_and_observation_entities():
         for item in cfg.observations.received_volume.params["entity_cfgs"]
     )
     cup = next(item for item in cfg.background if item.uid == "cup")
-    assert Path(cup.visual_shape.fpath).is_absolute()
+    assert cup.visual_shape.fpath == str(task_mesh_cache / "cup.glb")
+    assert cup.shape.fpath == str(task_mesh_cache / "mug_wall.obj")
     assert cup.shape.collision.force_sdf is False
 
 
diff --git a/tests/gym/utils/test_gym_utils.py b/tests/gym/utils/test_gym_utils.py
index 68c5e1ab8..9de58250f 100644
--- a/tests/gym/utils/test_gym_utils.py
+++ b/tests/gym/utils/test_gym_utils.py
@@ -2554,3 +2554,40 @@ def test_launcher_can_explicitly_disable_configured_headless() -> None:
     args = parser.parse_args(["--gym_config", "task.yaml", "--no-headless"])
     merged = merge_args_with_gym_config(args, {"physics": "default", "headless": True})
     assert merged["headless"] is False
+
+
+@pytest.mark.parametrize(
+    "collection", ["background", "rigid_object", "rigid_object_group"]
+)
+def test_mesh_asset_paths_resolve_collision_and_visual_geometry(
+    collection: str, tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """Separate render meshes use the same asset resolver as collision meshes."""
+    paths = {
+        "TaskMeshes/collision.obj": str(tmp_path / "collision.obj"),
+        "TaskMeshes/visual.glb": str(tmp_path / "visual.glb"),
+    }
+    monkeypatch.setattr("embodichain.data.get_data_path", paths.__getitem__)
+    obj = {
+        "uid": "cup",
+        "shape": {"shape_type": "Mesh", "fpath": "TaskMeshes/collision.obj"},
+        "visual_shape": {"shape_type": "Mesh", "fpath": "TaskMeshes/visual.glb"},
+    }
+    entries = [obj]
+    if collection == "rigid_object_group":
+        entries = [{"uid": "cups", "rigid_objects": {"cup": obj}}]
+    cfg = config_to_cfg(
+        {
+            "id": "EmbodiedEnv-v1",
+            "physics": "default",
+            "env": {},
+            "robot": {"uid": "robot"},
+            collection: entries,
+        },
+        manager_modules=DEFAULT_MANAGER_MODULES,
+    )
+    parsed = getattr(cfg, collection)[0]
+    if collection == "rigid_object_group":
+        parsed = parsed.rigid_objects["cup"]
+    assert parsed.shape.fpath == paths["TaskMeshes/collision.obj"]
+    assert parsed.visual_shape.fpath == paths["TaskMeshes/visual.glb"]
diff --git a/tests/lab/scripts/test_run_fluid_task_program.py b/tests/lab/scripts/test_run_fluid_task_program.py
index 9b1c6f5c9..a309567d5 100644
--- a/tests/lab/scripts/test_run_fluid_task_program.py
+++ b/tests/lab/scripts/test_run_fluid_task_program.py
@@ -36,6 +36,33 @@
 )
 
 
+@pytest.fixture(autouse=True)
+def task_mesh_cache(tmp_path, monkeypatch):
+    """Resolve task meshes locally so configuration tests never download them."""
+    from types import SimpleNamespace
+    from embodichain.data import dataset
+
+    directory = tmp_path / "PourCupAssets"
+    directory.mkdir()
+    for name in (
+        "cup.glb",
+        "mug_wall.obj",
+        "receiver_wall.obj",
+        "fluid_table.obj",
+        "fluid_floor.obj",
+    ):
+        (directory / name).touch()
+    original = dataset.get_data_class
+
+    def resolve(name, extra_modules=None):
+        if name == "PourCupAssets":
+            return lambda: SimpleNamespace(extract_dir=str(directory))
+        return original(name, extra_modules)
+
+    monkeypatch.setattr(dataset, "get_data_class", resolve)
+    return directory
+
+
 @pytest.mark.parametrize("renderer", [None, "fast-rt"])
 def test_launcher_selects_supported_renderers(renderer, monkeypatch, tmp_path):
     args = [
diff --git a/tests/test_task_program_package_data.py b/tests/test_task_program_package_data.py
index 746a2f4f2..653dc97b4 100644
--- a/tests/test_task_program_package_data.py
+++ b/tests/test_task_program_package_data.py
@@ -98,16 +98,6 @@
 }
 _RESOURCE_PATHS = frozenset(
     {
-        *(
-            Path("tasks/manipulation/tableware/pour_cup/assets") / name
-            for name in (
-                "cup.glb",
-                "fluid_floor.obj",
-                "fluid_table.obj",
-                "mug_wall.obj",
-                "receiver_wall.obj",
-            )
-        ),
         Path("tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"),
         Path("tasks/manipulation/tableware/pour_cup/env.fluid.yaml"),
         *_PROGRAMS,
@@ -245,8 +235,6 @@ def test_setup_stages_all_official_task_programs(
         "**/*.yaml",
         "**/*.yml",
         "**/README.md",
-        "tasks/manipulation/tableware/pour_cup/assets/*.obj",
-        "tasks/manipulation/tableware/pour_cup/assets/*.glb",
     ]
     expected_outputs = {
         Path("embodichain_tasks") / "configs" / relative_path
@@ -255,7 +243,7 @@ def test_setup_stages_all_official_task_programs(
     assert expected_outputs <= staged_config_package.relative_outputs
     assert not (
         staged_config_package.build_lib
-        / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets/water_fill.npz"
+        / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets"
     ).exists()
 
 

From 26507e158e85ea8dd9ffb0fa7ef3d38bd6931a17 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Wed, 14 Oct 2026 19:17:11 +0800
Subject: [PATCH 10/19] chore(pr): remove development notes and obsolete asset
 documentation

---
 IMPLEMENTATION_PLAN.md                        | 426 ------------------
 .../manipulation/tableware/pour_cup/README.md | 291 ++++--------
 .../sim/assets/franka_dfsph_pour/README.md    |  23 -
 3 files changed, 87 insertions(+), 653 deletions(-)
 delete mode 100644 IMPLEMENTATION_PLAN.md
 delete mode 100644 scripts/tutorials/sim/assets/franka_dfsph_pour/README.md

diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
deleted file mode 100644
index edc63c320..000000000
--- a/IMPLEMENTATION_PLAN.md
+++ /dev/null
@@ -1,426 +0,0 @@
-# Fluid integration implementation plan
-
-## Accepted scope
-
-Start from PR #771 (d5322aa). Use DexSim's existing SPHSolverCfg directly;
-add only the existing Newton solver mapping, not a new EC solver config.
-Initial fill, fixed particle counts, Newton CUDA DFSPH, one-way coupling.
-
-## Delivery gates
-
-1. **Sim public interfaces (complete)**: FluidObjectCfg / spawn and render
-   configuration; declared FluidObject / particle state; manager and Spawn
-   lifecycle; rigid boundary state and SDF adaptation; production tests.
-2. **Match the existing Sim demo (complete)**: migrate #771 using identical
-   particles, trajectory, numerical parameters, camera and material. Run a
-   same-environment baseline and migrated short test, then full pouring.
-   Compare received/residual/spilled volume, settling, images, runtime and VRAM.
-   A short test alone does not pass this gate. Record environment and evidence.
-3. **Gym integration (complete after gate 2)**: YAML scene
-   ownership, vectorized observations, partial reset, optional particle records.
-   DexSim world-filtered SPH neighborhoods/boundaries and per-world solver reset
-   are prerequisites; spacing worlds apart is not isolation.
-
-## Sim acceptance
-
-- Native solver object and mapping-valued sph configuration both work.
-- Object declaration -> prepare -> GPU particle state -> write/reset -> cleanup.
-- Deterministic points and volume sampling; local/world pose conventions.
-- Rendering never changes physical particle state; no native solver, builder or
-  render-sync access from the migrated tutorial.
-- Start with one environment and reject unsupported batch use explicitly.
-- Production tests, Black 26.3.1, API coverage and updated project context.
-
-## Evidence
-
-Implementation and validation results will be appended here. No Gym changes
-will be represented as complete before full demo parity is established.
-
-
-## Implemented Sim interfaces
-
-- `FluidObjectCfg`, `FluidSpawnCfg`, `FluidRenderCfg`; points/NPZ, box, sphere,
-  cylinder and closed fill-mesh sources. Shared numerical sampling is in compute.
-- `add_fluid_object`, `get_fluid_object`, existing `get_asset`, and detached
-  world-frame particle data; write/reset/reset_to_pose through Scene batches.
-- Direct DexSim SPHSolverCfg and mapping-valued `solver_type: sph` support.
-- Rigid kinematic pose/twist writes and native PRE_SUBSTEP trajectory controls.
-- Independent rigid visual/collision geometry and pre-baked SDF policy.
-- Native render publication, render-only clipping, and detached Viser points.
-- This first Sim milestone initially rejected multiple environments; the
-  completed Gym phase below lifts that gate only for qualified native artifacts.
-
-## Current qualification limits
-
-- Fixed physical topology after prepare; no particle/rigid topology rebuild.
-- `FluidRenderCfg(mode="none")` requires the NoRender backend.
-- Native runtime used here is a local DexSim build; repository dependency pin
-  is unchanged. This does not qualify every published wheel.
-- GPU memory was sampled on matching 2-second runs: baseline 4981 MiB,
-  public API 4985 MiB. This does not measure the full-pour peak.
-
-## Review and validation
-
-Architecture/API and engine reviewers checked the implementation independently.
-Integer initial velocities, independent-visual-mesh UV selection, optional optical
-material forwarding and render-frame cache scope were corrected and regression
-covered. The full run must include a pixel/video check: an earlier iteration had
-correct particle snapshots but stale offscreen images, fixed by publishing via
-`sim.sync_render_state()` before camera acquisition.
-
-
-## Full-demo parity result
-
-- Runtime: Python 3.11.14, local DexSim 0.5.1+21bfad162518216fdd9cf313c70b15af94e6fa84,
-  Torch 2.10.0, Warp 1.15.0, Newton 1.4.0, RTX 5090.
-- Same-environment original script versus public-API script: 30.975 seconds,
-  76,538 particles, 31 snapshots with **bit-identical positions and velocities**.
-  All recorded statistics match exactly, including 139.16475 mL received and
-  final receiver speed RMS 0.0048424867 m/s.
-- Two videos each decode all 744 frames. Mean pixel absolute difference on the
-  0-255 scale: 0.477 (wide), 0.586 (close); moving/pouring and settling frames
-  were also visually inspected. Original native linear water color is converted
-  explicitly to the public material's sRGB convention.
-- Full-loop wall time: original 341.69 s, public API 349.04 s (+2.15%).
-  This is a single-run comparison, not a benchmark distribution.
-- Evidence: `outputs/fluid-parity.json`, `outputs/fluid-baseline-full/`,
-  `outputs/fluid-public-final/`. Generated videos/NPZ remain local ignored outputs.
-- Focused CPU/config/lifecycle/visualization/material/context/API-check tests:
-  533 passed, 1 skipped in the final combined run. GPU lifecycle/substep/reset
-  test: 1 passed. The tutorial also passed a two-substep smoke run.
-- GPU lifecycle test verifies declaration, state, native substep control,
-  selected reset, repeated rollout, endpoint hold and cleanup.
-
-## Delivered milestones
-
-Sim parity and two-stage Task Program -> fluid Gym integration are complete in
-isolated worktrees. Batch support remains gated by loaded native capabilities.
-Published native wheel/AOT qualification is a separate release task; no shared
-runtime or project dependency pin was changed.
-
-GPU memory evidence: `outputs/fluid-memory-comparison.json` (process samples
-at approximately 250 ms cadence; 4981 -> 4985 MiB).
-
-
-## Completed phase: batch SPH, then Task Program -> fluid Gym
-
-User confirmed the execution route: generate and validate a new trajectory with
-Task Program in a MuJoCo Gym environment; replay its recorded robot and cup
-states in a separate fluid Gym environment. This remains one-way coupling;
-there is no online rigid-fluid joint solver or synthetic grasp attachment.
-Generation acceptance and fluid physical metrics are recorded independently.
-
-Dependency worktree: `/home/dex/.codex/worktrees/fluid-sph-worlds/dexsim`,
-branch `codex/fluid-sph-worlds`, base `21bfad162518216fdd9cf313c70b15af94e6fa84`.
-Native implementation commit: `d2d36fbcb`.
-EC worktree remains this checkout, base Sim commit `bbd6928c1`.
-Native modifications belong to the existing SPH solver/kernels and backend reset
-interface. Compare: per-world solver instances (resource overhead), independent
-partition scheduling (larger redesign), or world-filtered batched kernels
-(selected, preserves ownership and batched stepping). No threading changes.
-
-First oracle: coincident replicated worlds must have independent particle
-neighbors, boundaries, gravity and convergence. Establish baseline failures
-before fixing kernels. Then selected-world reset must preserve all other rows.
-On green: enable capability-gated EC batch declaration and integrate Gym YAML,
-fluid observations/reset, and Task Program trajectory generation + fluid replay.
-On red: diagnose the failing kernel/contract in the dependency, without removing
-EC capability guards. Runtime tests must print/verify source package identity;
-the shared installed DexSim build is not modified.
-
-
-## Gym and native qualification results
-
-- Native source: world-filtered neighbors and SDF/volume-map boundaries,
-  per-world gravity and DFSPH convergence; canonical `E+1` selected reset.
-  Pure global legacy single-world particles remain supported. Mixed global/local
-  particles and global particles with multiple worlds fail explicitly.
-- Before fixes, overlapping worlds shared 16 instead of 8 neighbors and gravity;
-  convergence differed by 0.62 m/s and a foreign boundary displaced particles
-  by 0.08 m. New native regressions plus existing boundary checks: **14 passed**.
-- EC YAML declares fluids through physical environment ownership; packaged
-  relative source assets, fluid observations, container refill and capability
-  gated batching are implemented. `FluidReplayEnv` reuses normal Gym lifecycle,
-  with per-row clocks and partial reset. Robot roots/joint order are validated.
-  Action managers and policy dataset/trajectory recording are rejected.
-- Task Program source freshly generated in MuJoCo and accepted: lift
-  **0.1197915 m**, maximum tilt **66.4883 degrees**, return error **0.00223394 m**.
-  Source program hash:
-  `6cc3d7aa5a64c4cc86f8d4f60bf80dbad23fca368785e165164cd39f50b288cb`.
-- Full Gym replay: **2 x 76,538 particles**, **31 s**, **744 video frames** fully
-  decoded and inspected at lift/pour/return. Final received volume:
-  **141.98288 / 140.88261 mL**; all sampled particle states finite. Wall time
-  **905.33 s**. Source motions are measured states with one-way fluid coupling;
-  these results do not claim online robot-fluid force feedback.
-- Focused EC config, objects, packaging, context and docs checker tests:
-  **259 passed**. Later batch-count regression and refill reuse: **11 passed**.
-  Fresh-process live Gym independent completion/reset and Sim selected reset:
-  **2 passed**. Final coarse two-row CLI partial-reset and single-row override
-  smoke checks also passed.
-- Independent reviews corrected wheel asset omission, root pose adoption,
-  misleading action recording, dataset mapping compatibility and canonical
-  global reset masks. Real Gym tests additionally caught pre-resolution sim
-  versus env counts; both replay and CLI now use `EnvCfg.num_envs`.
-- Sphinx dummy build succeeded. The full run reported 811 warnings versus
-  the earlier 800: one new duplicate fluid field entry was corrected, and
-  ten additional warnings concern optional Pinocchio/type imports in the
-  isolated environment. The post-fix incremental build also succeeded, with
-  1123 warnings from repeated existing API indexing/type resolution and no
-  fluid-specific warning entries. This is not a warning-free documentation build.
-- API coverage **2503/2503**; source Task Program inspector passed all three
-  calls. A built EC wheel contains all six pour-cup assets. Native validation
-  uses staged JIT source, not a rebuilt release/AOT wheel.
-- Registered `sph_speed_dfsph_4096` benchmark, same RTX 5090 / Newton 1.6 /
-  Warp 1.17 configuration, 20 warmup + 100 measured steps: baseline mean
-  **3.05956 ms**, changed **3.17811 ms** (+3.87%); p95 **3.24373 / 3.38617 ms**.
-  Both runs finite with matching physical summary metrics. Single-run evidence,
-  not a statistical performance guarantee; no claim of multi-world speedup.
-- Runtime: isolated Python 3.11, Torch 2.10, Newton 1.6.0, Warp 1.17.0,
-  MuJoCo/MuJoCo-Warp 3.12.0; DexSim native binaries from local base build and
-  independently staged changed JIT source. Shared build and environments remain
-  unchanged. NoRender robot smoke teardown still logs native entity balance
-  `1024 != 1023`; tests complete, but repeated native resource-leak qualification
-  is not established by these short runs.
-- Context reviewed and revised: fluid lifecycle/capability, Gym replay and
-  native Newton world/reset ownership. Existing Task Program language/semantics
-  guidance remains accurate because those contracts are reused unchanged.
-
-Evidence (ignored local outputs): `outputs/fluid-task-program/`,
-`outputs/fluid-gym-full/fluid_gym.mp4`, `outputs/fluid-gym-full/fluid_gym_result.json`,
-`outputs/fluid-gym-full/contact-sheet.jpg`, `outputs/fluid-gym-final-smoke/`,
-`outputs/fluid-benchmark/`, `outputs/fluid-wheel/asset-validation.json`.
-
-Reproduction in this checkout uses the isolated native JIT runtime and virtual
-Python at `/home/dex/.codex/worktrees/fluid-sph-worlds/venv/bin/python`, with
-`DEXSIM_NEWTON_ARTIFACT_MODE=jit`; `python -m
-embodichain.lab.scripts.run_fluid_task_program --num-envs 2` runs both phases.
-Old pre-recorded tutorial trajectories lack qualification metadata and are
-intentionally rejected by the Gym adapter.
-
-
-## Rendering follow-up: Hybrid / FastRT and record cameras
-
-User requested Hybrid or FastRT rendering and record-camera views for every
-environment. The fluid deployment now owns a `record_camera` interval event
-using `record_camera_data(per_env=True, fps=None)`. The runner defaults to
-Hybrid, supports FastRT, redirects recorder output to `<output>/videos`, and
-commits the final recording explicitly before close. Independent recorder
-buffers/counters preserve unselected rows during partial reset. Capture renders
-the current camera group before fetching frames, since interval events precede
-observation rendering. The previous script-owned, environment-zero ImageIO
-writer is removed.
-
-Validation:
-- Focused recorder, finalization, launcher/config and context tests: **128 passed**.
-- Live Gym row-reset/exhaustion regression: **1 passed**.
-- Hybrid and FastRT short camera runs both generated two valid 640x480,
-  24-fps videos. Full Hybrid replay generated **two separate 744-frame,
-  31-second videos**. Every frame decoded; both arena views were inspected at
-  lift, pour and return. Source acceptance and final fluid statistics are
-  unchanged: received **141.98288 / 140.88261 mL**, all sampled states finite.
-- Full Hybrid run wall time: **908.71 s**, including final video persistence.
-  This run is functional qualification, not a rendering performance benchmark.
-- Live partial-reset recording smoke: after discarding row 0's first two
-  frames, row 0 saved one new frame while row 1 retained all three frames.
-  The unchanged row's particle state also passed the exact preservation check.
-- API coverage **2503/2503**, Black 26.3.1, syntax/headers, context and diff checks
-  passed. General recording ownership was revised in manager-functor context;
-  fluid deployment usage was revised in env-framework context. Existing render
-  lifecycle guidance remains accurate because capture uses its public group API.
-
-Evidence: `outputs/fluid-gym-hybrid-record/videos/episode_0_fluid_gym_env0.mp4`,
-`episode_0_fluid_gym_env1.mp4` in the same directory, and
-`outputs/fluid-gym-hybrid-record/record-camera-validation.json` /
-`record-camera-contact-sheet.jpg`. Backend smoke evidence is in
-`outputs/fluid-record-fastrt-smoke/`; row-selection evidence is in
-`outputs/fluid-record-partial-smoke/`.
-
-
-## Completed follow-up: file-free procedural fluid initialization
-
-Default pouring now declares a capped revolved cavity profile and fill level.
-`compute/geometry/particles.py` samples it on the existing deterministic cubic
-lattice, respecting side-wall distance and end-cap clearance. The free-surface
-clip is separate from solid clearance. Geometric configuration is converted to
-local points once during declaration, with no disk cache or particle artifact;
-existing scene binding and selected reset reuse the memory template. Direct
-initialization and the existing stationary replay hold are retained; this change
-does not introduce an unused settled-state cache or a second stepping owner.
-
-`water_fill.npz` is removed, and package-data patterns include only task mesh
-assets (OBJ/GLB). Explicit points/NPZ imports remain supported. Volume-only
-filters and launcher spacing overrides are rejected for fixed point imports.
-The runner's spacing override preserves the configured volume instead of
-substituting a cylinder. New reports include the source recipe, spacing and
-density for reproducibility.
-
-For this cup: profile `[-0.044, 0.031] -> [0.05, 0.0385]` (Z, radius),
-fill level 0.91, clearance 0.003 m, spacing 0.0015 m. The standard cell-centre
-lattice produces **76,607 particles / 258.548625 ml**, versus the previous
-**76,538 / 258.31575 ml** (nominal volume +0.0901%). Point layouts differ because
-sampling now consistently follows source bounds and physical wall clearance.
-
-Acceptance: numerical geometry/volume/spacing and configuration tests; package
-asset removal; real batched initialization/reset without resampling; complete
-Hybrid pouring with record cameras for both environments; finite state and
-received-volume comparison with the previous run.
-
-Validation completed:
-- Geometry/config/object/packaging/task/context regression: **294 passed**.
-  Final launcher/packaging checks after the fixed-point override guard: **7 passed**.
-  Context routing checks after registering profile sampling: **40 passed**.
-- Real batched Gym reset/exhaustion and Sim selected-reset regressions:
-  **2 passed**. Gym tests replace the sampler with a failing stub during full
-  and selected reset, proving reset reuses the memory template.
-- Cold-built wheel contains five pour-cup mesh assets and no `water_fill.npz`.
-  Points/NPZ import tests continue to pass. API coverage **2503/2503**, Black
-  26.3.1, context validation and diff whitespace checks pass.
-- Complete Hybrid replay: **2 x 76,607 particles**, both rows completed;
-  all sampled states finite; two independent **744-frame / 31-second / 24-fps**
-  videos decoded fully and visually checked through lift, pour and return.
-  Wall time **914.49 s**. Final received volumes **141.89175 / 141.39900 ml**,
-  relative changes **-0.06418% / +0.36654%** versus the previous saved-point run.
-- FastRT 6-mm spacing / partial-reset smoke uses the same configured profile
-  and fill level, generating **1,225** particles per environment. Untouched
-  particle state is identical at reset; record-camera frame counts are **1 / 3**
-  for reset/untouched rows. This coarse case validates protocol and finite state,
-  not liquid fidelity at the qualified 1.5-mm resolution.
-- Context revised in simulation fluids and the consuming Gym guide; routing now
-  includes profile sampling and its compute owner. Existing solver, Task Program
-  and recording contracts remain unchanged.
-
-Evidence: `outputs/fluid-gym-procedural-full/initialization-comparison.json`,
-`record-camera-validation.json`, `record-camera-contact-sheet.jpg` and both
-`videos/episode_0_fluid_gym_env*.mp4` in that output directory. The coarse check
-is in `outputs/fluid-procedural-coarse/`; package inspection is in
-`outputs/fluid-procedural-validation/wheel-assets.json`.
-
-
-## Completed fix: liquid visibility in recorded cameras
-
-The user reported no fluid in the supplied videos. Reproduction confirmed that
-the prior Hybrid recordings qualified particle state and robot motion but did
-not display the liquid itself. Earlier claims of liquid visual qualification
-for Hybrid are retracted; decoded frames and robot pixel motion are insufficient
-oracles. Original OfflineRT Sim parity is a separate earlier result.
-
-Controlled reproduction: the same opaque blue 8,000-particle fluid block has
-**0 blue foreground pixels in Hybrid**, versus **5,873 in FastRT**. Hybrid
-shades a Vulkan G-buffer primary surface, while the fluid bridge creates only
-OptiX mesh/sphere instances. Particle statistics and indirect image changes
-can therefore be nonzero while the liquid primary surface is missing.
-
-The fluid deployment and runner now select FastRT, and the public manager and
-runner reject unsupported Hybrid liquid rendering. The declaration gate checks
-both the resolved configuration and the initialized process renderer, including
-all fluid render modes, before sampling, material creation or declaration.
-Existing Hybrid processes require a fresh process for FastRT/RT; pure physics
-uses NoRender. Empty-cup source generation
-continues using Hybrid. The record camera focuses on the cup and receiver; the
-mesh reconstruction kernel is 1.5 particle spacings. Spacing overrides resample
-the existing fill volume and also scale the explicit render radius, mesh cell
-size and reconstruction kernel radius. Renderer overrides retain other configured
-render settings. The clear-water material, physical solver, fill recipe and
-Task Program motion remain unchanged; no native binary change was needed.
-
-Validation:
-
-- Hybrid declaration gate, process-backend resolution, launcher and context
-  regression: **265 passed**. Coverage includes auto selection, reused Hybrid processes,
-  all fluid render modes and supported FastRT/RT/NoRender declarations. These
-  contract tests do not allocate GPU fluid state.
-- Focused config/launcher/Gym/context contracts: **80 passed**. Serial GPU camera
-  regression: **1 passed**, including physical stepping, display clipping,
-  hidden-fluid controls and exact state restoration. Blue foreground counts
-  are **1,601 / 1,609**, versus **0 / 0** with the fluid outside the views.
-- Complete live SPH rollout: **2 x 76,607 particles**, **744 frames / 31 s**,
-  final received volumes **141.89175 / 141.39900 ml**, all sampled states finite.
-  Every particle-position frame was retained as ignored diagnostic output;
-  the last frame exactly matches the completed physical state.
-- The final visual configuration was qualified by a separate render-only pass
-  over those exact physical particle frames, using the existing per-environment
-  record-camera functor. No physics steps or synthetic liquid motion were added
-  during re-recording. Both **744-frame / 24-fps / 31-s** videos decoded fully;
-  decoded frames show the stream and accumulated water in both environments.
-- Clear-water visible/hidden camera control after the complete frame sequence:
-  **938 / 1,002 receiver pixels** changed by more than 10/255. Receiver RGB MAE
-  is **1.92990 / 2.08807**, versus repeat-render noise **0.17535 / 0.18492**.
-  This is direct liquid visibility evidence, independent of particle counts.
-- The production runner with 6-mm resampling, FastRT and a selected reset passed
-  a live three-control-step check: **1,225 particles/environment**, exact
-  untouched state at reset, and separate **1 / 3-frame** recordings. This checks
-  the scaled reconstruction and recording/reset protocol, not fluid fidelity.
-- Black **26.3.1**, API coverage **2503/2503**, context validation, tutorial syntax
-  and `git diff --check` pass. No full suite or release/AOT rebuild was needed.
-
-Final media and pixel/decode reports:
-`outputs/fluid-gym-visible-qualified/` (`videos/episode_0_fluid_gym_env*.mp4`,
-`render_validation.json`, `video_decode_validation.json`, decoded keyframes,
-`water_visible_env*.png` and `water_hidden_env*.png`). Physical-state evidence is
-`outputs/fluid-visibility-live-cache/` (`fluid_gym_result.json`, diagnostic particle
-frames and `particle_frame_validation.json`). The live scaled/reset smoke is in
-`outputs/fluid-visible-scaled-smoke/`. Baseline renderer comparison remains in
-`outputs/fluid-visibility-baseline/`; these generated files are not task assets.
-
-
-## Main-based PR consolidation
-
-PR #777 now includes the complete #771 branch (`d5322aaad`) and targets `main`.
-The feature branch merges main commit `f9fb56d9a`; parent history is retained
-without rewriting either branch. #771 is superseded after confirming its head
-is an ancestor of the published feature head. Its branch is retained.
-
-Main integration required the pouring tutorial to import `FrankaPandaCfg` from
-`embodichain_robots`, matching the new canonical robot package. Fluid physical
-parameters, source initialization, Task Program motion and Hybrid gates retain
-their existing behavior. Refreshing the isolated editable install supplies the
-new robot catalog entry point; obsolete generated bytecode was moved outside
-the retired robot namespace.
-
-Post-integration validation: **616 CPU checks passed** across sampling, fluid
-state/configuration, Sim manager, Gym composition/replay/recording, task/package
-layout, robot catalogs/imports, visualization and context. **3 serial GPU checks
-passed** for camera-visible fluid, selected-world reset and Gym replay lifecycle.
-Both Default and Newton pour Task Program deployments pass static inspection.
-API coverage is **2629/2629**. A cold wheel includes the canonical Franka package
-and all five task mesh assets, with no `water_fill.npz` or retired robot modules.
-Black 26.3.1, context checks, tutorial syntax and `git diff --check` pass.
-
-Evidence: `outputs/fluid-main-validation/wheel_validation.json`; test logs
-`/tmp/fluid-main-regression.log`, `/tmp/fluid-main-adapter-regression.log`,
-`/tmp/fluid-main-gpu-regression.log` and `/tmp/fluid-main-deployments.log`.
-The earlier full physical replay and video qualification remain prior evidence;
-the full 31-second physical workload was not repeated for this Git integration.
-The DexSim batched-SPH release/pin and release/AOT qualification remain pending,
-so #777 stays Draft.
-
-## Downloadable task assets
-
-Task meshes and historical demo data are hosted in
-`DexForceAI/embodichain_data/task_assets` at HF commit
-`f13a1240d82d728cd0caf7550487a833c2b59368`. Runtime and demo downloads are
-separate: `PourCupAssets.zip` contains the five meshes, while
-`PourCupDemoData.zip` preserves the trajectory, reference snapshot, media and
-original validation records. Both use the standard `EmbodiChainDataset` locked
-cache, pinned archive MD5, configured mirror and official-Hub fallback, with a
-new `task` download-CLI category. The HF manifest records original file SHA-256
-and source commit `0f132ef68`.
-
-The Git tree and wheel no longer carry these payloads. YAML references use
-`PourCupAssets/...`; the standalone tutorial resolves its default trajectory
-lazily from `PourCupDemoData/trajectory.pt`. Configuration parsing resolves
-both collision and independent visual mesh assets, including rigid groups.
-Fluid initialization remains profile-based in memory; the archived final NPZ
-snapshot is historical evidence only.
-
-Verification includes an empty-cache download/extraction with all **20 file
-SHA-256 checks** and both ZIP MD5 checks matching their original source bytes;
-a cold wheel with the downloader and three environment configs but **zero pour
-mesh or demo payload files**; the real dual-environment FastRT runner with
-1,225 particles per row, exact untouched state at selected reset and separate
-1 / 3-frame recordings; **250 passing data/config/packaging/API/context checks**; and
-standalone tutorial syntax/help checks. Local transfer and cache reports are
-in `outputs/task-assets-hf/`, wheel validation in
-`outputs/task-assets-package-validation/`, and the live smoke in
-`outputs/task-assets-runtime-smoke/`. Historical wheel-with-mesh observations
-above describe the earlier implementation and are superseded by this migration.
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
index 5ff49ecf1..c17513d6c 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
@@ -1,193 +1,109 @@
-# Franka 单臂马克杯倒水 demo
+# Franka 单臂马克杯倒水
 
-## Sim 公共接口接入
+使用现有 `Pick → simulation.pour → Place` Task Program 生成并验证空杯轨迹，
+再在独立的 Newton SPH 环境中回放机械臂关节和杯子实测状态，观察接水效果。
+流体接入使用 `SimulationManager` 的公共接口，求解器参数直接复用 DexSim `SPHSolverCfg`。
 
-当前实现用 `SimulationManager.add_fluid_object(FluidObjectCfg(...))` 声明水体；
-求解器仍直接使用 DexSim 的 `SPHSolverCfg`。杯壁、台面和碗通过刚体配置声明，
-碰撞网格与显示网格可分离。原生 SDF 和液面重建由底层适配负责。
-离屏相机采集前使用 `sim.sync_render_state()` 发布状态，裁剪仅影响显示副本。
-多子步时杯壁轨迹通过 `register_kinematic_rigid_trajectory()` 在 solver 子步采样。
+## 运行要求与资产
 
-当前支持 Newton CUDA、固定粒子数和单向耦合。场景准备后物理拓扑冻结。
-多环境要求 DexSim SPH 提供 world 隔离及选择性重置能力；旧运行时仍限制为单环境。
-`IMPLEMENTATION_PLAN.md` 记录分阶段验证结果。
+需要支持原生 SPH、volume map、液面重建和 OptiX 的 DexSim / Newton CUDA 运行时。
+多环境还要求 SPH 的 world 隔离和选择性重置能力；旧运行时保留单环境门禁。
+当前批量实现依赖尚未发布的 DexSim 扩展，发布 wheel / AOT 验收状态见 PR。
 
-## Task Program → 流体 Gym
+运行时网格和可选演示资料通过标准资产缓存下载：
 
 ```bash
-python -m embodichain.lab.scripts.run_fluid_task_program --num-envs 2 --renderer fast-rt
+embodichain data download --name PourCupAssets
+# 仅复现原始 Sim 演示时需要：
+embodichain data download --name PourCupDemoData
 ```
 
-默认先在 MuJoCo 中执行现有 Task Program，验证真实杯子抬升、倾倒和放回，
-再启动独立 SPH Gym 回放其关节与杯子实测状态。输出包含源轨迹、源验收报告、
-流体接水量统计，以及 `videos/episode_0_fluid_gym_env0.mp4`、
-`videos/episode_0_fluid_gym_env1.mp4` 等每环境独立视频。
-流体阶段默认使用 `fast-rt`。当前原生液面重建只发布到 OptiX 场景，
-Hybrid 的 Vulkan 主表面看不到它，因此该组合会明确报错；空杯轨迹生成阶段
-仍可使用 Hybrid。
-录制通过环境 `record_camera` 事件调用现有 `record_camera_data`，
-配置 `per_env: true`、`fps: null`，每个控制步采集各 arena 的相对视角，
-按实际控制频率（本配置 24 fps）保存。默认视角靠近杯口与接水碗，便于观察水流。
-脚本结束前显式保存最后一段视频。
-复用本工具生成的已验收轨迹可传
-`--trajectory outputs/fluid-task-program/task_program_trajectory.pt`。
-`--renderer no-render --seconds 0.125 --particle-spacing 0.006 --partial-reset-step 1`
-用于低成本双环境局部重置检查；粗粒度检查不代表完整液体效果。
-
-`env.fluid.yaml` 拥有 SPH 参数、刚体边界、初始粒子和流体观测；
-`task.franka.fluid.yaml` 选择环境和机器人组件。杯子与边界网格通过 `PourCupAssets` 下载并缓存，
-水体在构建场景时直接采样生成，默认不读取或保存粒子文件。
-求解器配置直接使用 DexSim SPHSolverCfg。
-
-初始水体由以下配置描述（单位为米）：
+| 资产包 | 内容 |
+| --- | --- |
+| `PourCupAssets` | 杯子显示网格、杯壁、接水碗、台面和地面碰撞网格 |
+| `PourCupDemoData` | 原始已验证轨迹、参考视频、最终粒子快照和验证报告 |
 
-```yaml
-spawn:
-  kind: revolved_volume
-  reference_uid: cup
-  profile: [[-0.044, 0.031], [0.05, 0.0385]]  # [杯子局部 Z, 腔体内半径]
-  fill_level: 0.91
-  clearance: 0.003
-```
+未预下载时，`get_data_path("PourCupAssets/cup.glb")` 等路径会自动下载并缓存。
+`EMBODICHAIN_DATA_ROOT` 可覆盖缓存根目录；数据包托管于
+[Hugging Face task_assets](https://huggingface.co/datasets/DexForceAI/embodichain_data/tree/main/task_assets)。
+代码仓库和 wheel 仅携带下载器与配置，默认初始水体直接在内存生成。
 
-截面绕局部 Z 轴旋转并封闭两端。`fill_level` 指完整腔体高度的比例，
-其液面裁剪与固体壁面间隙分别处理；它不表示稳定后的体积百分比。
-1.5 mm 规则网格生成 76,607 个粒子，名义体积约 258.55 ml。
-修改 `particle_spacing` 或 `fill_level` 后，在下一次构建时重新生成点集。
-`--particle-spacing` 也使用同一截面重新采样，并按间距比例缩放显示半径、
-液面网格间距和重建核半径，便于粗分辨率检查。
+## Task Program → 流体 Gym
 
-局部点集只生成一次并保存在内存，reset 根据杯子位姿和 arena 偏移恢复选中环境；
-回放起始的静置阶段继续用于液体稳定化。NPZ 仍可作为外部点集导入格式，
-点集导入模式的采样分辨率固定，不接受脚本的间距覆盖。
+从源码仓库根目录运行：
 
-`FluidReplayEnv` 支持标准 reset/step、按行时钟与流体重置、summary 和区域体积观测。
-它从单行合格源轨迹广播到多个环境，要求固定机器人根位姿和单个 Newton 子步。
-机器人动作由源状态决定，因此拒绝 action manager 和策略 action 数据录制。
-流体不会反向影响机械臂；Task Program 源验收与流体接水效果分别报告。
-本次实测依赖隔离的 DexSim JIT 构建、Newton 1.6 / Warp 1.17；发布 wheel 尚未验收。
+```bash
+python -m embodichain.lab.scripts.run_fluid_task_program --num-envs 2 --renderer fast-rt
+```
 
+脚本先在 MuJoCo 中执行并验收抬杯、倾倒、放回，再启动独立 SPH Gym。
+默认输出到 `outputs/fluid-task-program/`，包含源轨迹、源验收报告、接水量统计，
+以及 `videos/episode_0_fluid_gym_env0.mp4` 等每环境独立视频。
+录制通过 `record_camera` 事件调用 `record_camera_data`，使用 `per_env: true`、
+`fps: null`，按实际控制频率保存（本配置为 24 fps）。
 
+复用本工具生成的合格轨迹，或进行粗粒度局部重置检查：
 
-使用真实 Franka Panda 机型的仿真模型，从侧面夹住正常大小的马克杯杯把，
-抬杯、向碗里倒水、回正并放回。这里包含两个入口：
+```bash
+python -m embodichain.lab.scripts.run_fluid_task_program \
+  --trajectory outputs/fluid-task-program/task_program_trajectory.pt --num-envs 2
 
-- **空杯 Task Program**：只用 YAML 组合现有 `Pick → simulation.pour → Place`，
-  分别提供 Default 和 Newton + MuJoCo-Warp 部署。
-- **DFSPH 带水演示**：教程脚本回放已验证的 MuJoCo 机械臂和杯子轨迹，
-  同时通过 EmbodiChain 的 Newton 后端模拟液体。
+python -m embodichain.lab.scripts.run_fluid_task_program \
+  --trajectory outputs/fluid-task-program/task_program_trajectory.pt \
+  --num-envs 2 --renderer no-render --seconds 0.125 \
+  --particle-spacing 0.006 --partial-reset-step 1
+```
 
-原始教程入口保留用于效果对照；生产接入位于 `embodichain/` 生产代码。液体采用单向耦合：移动杯壁
-影响水，水不反作用于机械臂；DFSPH 场景不重新求解夹持接触。
+流体阶段默认使用 FastRT。当前液面几何只发布到 OptiX 场景，Hybrid 的 Vulkan
+主表面无法显示，因此流体对象会拒绝 Hybrid；空杯轨迹生成仍可使用 Hybrid。
+`no-render` 仅用于物理检查。粗分辨率短测不能替代完整倒水效果验证。
 
-## 直接运行带水演示
+`FluidReplayEnv` 提供标准 `reset` / `step`、按环境独立时钟、流体选择性重置、
+summary 和区域体积观测。它将单行合格轨迹广播到多环境，要求固定机器人根位姿
+和单个 Newton 子步。回放由源状态驱动，不接受 action manager 或策略 action 数据录制。
+耦合为单向：杯壁影响水，水不反作用于机械臂，SPH 阶段不重新求解抓持接触。
+Task Program 验收和流体接水结果分别报告。
 
-从源码仓库根目录，在已安装 EmbodiChain 和兼容 DexSim 的环境中运行：
+## 初始水体配置
 
-```bash
-python scripts/tutorials/sim/franka_dfsph_pour_demo.py --headless
-```
+`env.fluid.yaml` 声明 SPH 参数、刚体边界、流体和观测。
+初始水体由杯子局部坐标系下的腔体截面描述，单位为米：
 
-脚本默认通过 `get_data_path("PourCupDemoData/trajectory.pt")` 下载并缓存
-已验证轨迹，不需要先生成。运行时杯子网格使用 `PourCupAssets/cup.glb`。
-去掉 `--headless` 可打开原生窗口。
-短时检查或指定自行生成的轨迹：
+```yaml
+spawn:
+  kind: revolved_volume
+  reference_uid: cup
+  profile: [[-0.044, 0.031], [0.05, 0.0385]]  # [局部 Z, 腔体内半径]
+  fill_level: 0.91
+  clearance: 0.003
+```
 
-```bash
-python scripts/tutorials/sim/franka_dfsph_pour_demo.py \
-  --headless --seconds 2 --output outputs/franka_pour_cup/smoke
+截面绕局部 Z 轴旋转并封闭两端。`fill_level` 是完整腔体高度的比例，
+液面裁剪与壁面间隙分别处理；它不表示稳定后的体积百分比。
+默认 1.5 mm 规则网格采样得到 76,607 个粒子，名义体积约 258.55 mL。
+局部点集只生成一次并保存在内存；reset 根据杯子位姿和 arena 偏移恢复选中环境，
+回放起始的静置阶段用于液体稳定化。
 
-python scripts/tutorials/sim/franka_dfsph_pour_demo.py --headless \
-  --trajectory outputs/franka_pour_cup/newton_smooth_mug_trajectories/traj_env0_000000.pt
-```
+修改配置后重新构建会重新采样。`--particle-spacing` 同时按间距比例缩放显示半径、
+液面网格间距和重建核半径。外部点集 / NPZ 导入仍受支持，但其采样分辨率固定，
+不接受脚本的间距覆盖。
 
-本机验证使用 Python 3.10、DexSim 0.5.1 本地 wheel、Newton 1.6.0、
-MuJoCo / MuJoCo-Warp 3.12.0、Warp 1.17.0 和 NVIDIA RTX 5090 D v2。
-还需要 Torch、TensorDict、NumPy、SciPy、Trimesh、OpenCV、ImageIO 和
-ImageIO-FFmpeg。完整版本记录可通过
-`get_data_path("PourCupDemoData/environment.json")` 获取。
-DexSim 必须包含原生 SPH、volume map、液体网格重建和 OptiX 支持；
-本地 wheel 和虚拟环境不随分支提交，也未更改项目的全局依赖。
-教程脚本针对源码 checkout；资产单独下载，wheel 仅携带代码与配置。
-
-Franka 模型沿用 EC 现有资产解析；杯子网格通过任务资产下载，接水碗由脚本生成。
-默认输出目录是 `outputs/franka_pour_cup/dfsph_stable_bowl/`，包括全景/近景
-MP4、每秒关键帧、粒子位置和速度 NPZ、参数及统计 `validation.json`。
-已录制的最终结果见
-[演示资料目录](../../../../../../scripts/tutorials/sim/assets/franka_dfsph_pour/README.md)，
-`PourCupDemoData` 包含三段视频、原始轨迹、最终粒子快照和验证报告，
-均托管于 [Hugging Face task_assets](https://huggingface.co/datasets/DexForceAI/embodichain_data/tree/main/task_assets)。
-
-## 场景和求解器
-
-| 项目 | 配置 |
-| --- | --- |
-| 马克杯 | 杯口外径 8.5 cm、高 10 cm、壁厚 4 mm、底厚 6 mm |
-| 杯子容量和重量 | 几何满口容量约 362 mL，空杯 300 g |
-| 杯把 | 平侧面截面 12 × 12 mm，总宽约 11.33 cm |
-| 接水碗 | 弧形内壁、平底，口径约 18.9 cm、高 6.6 cm |
-| 碗中心 | `[-0.755, -0.137, 0.50]` m |
-| 液体 | 76,538 粒、间距 1.5 mm、初始名义水量约 258 mL |
-| 边界 | volume map，最大网格分辨率 192，额外 SDF 惩罚力关闭 |
-| 物理更新 | 1440 Hz，每步 1 个子步 |
-| DFSPH | 散度迭代上限 10、密度迭代上限 20、压力松弛 0.2 |
-| 液体参数 | 密度 1000 kg/m³、黏性系数 0.0006、离散表面张力 0.002 |
-| 录制 | 24 fps，全景 1280 × 960，近景 960 × 720 |
-
-体积按粒子初始体积估算。液体参数为演示调参，未做材料物性标定。
-`--spacing`、`--volume-map-resolution`、`--viscosity` 和 `--surface-tension`
-可调整对应参数。默认轨迹两倍速，首尾各静置 1 秒，共 30.975 秒仿真。
-
-杯子位姿、线速度和角速度每个物理步更新。机器人关节与杯子位置用 PCHIP
-插值，杯子姿态用 SLERP。volume map 从容器网格 SDF 烘焙，保留表面投影；
-`volume_map_boundary_enabled=True`，`sdf_boundary_enabled=False`。
-为适配 Newton 1.6，使用显式预烘焙 `force_sdf=False` 和 `approximation=SDF`。
-
-脚本参考 DexSim 的
-`examples/python/physics/particle_fluid/demo_newton_sph_dam_break_optix.py`，
-通过 `SimulationManager` / `NewtonPhysicsCfg` 运行
-`SPHSolverCfg(sph_method="dfsph")`。临时接入直接访问
-`sim._spawn_scene.builder` 和 Newton 后端 `render_sync.sync()`；
-这不是正式的公共液体 API。
-
-## 碗内稳定性处理
-
-旧版在碗内有持续的高频运动，散落粒子还会把重建范围拉大，使水面变粗或消失。
-本版增加黏性耗散、减小离散表面张力、提高 volume map 分辨率，并把碗朝水流
-落点平移 3.5 cm。重建使用 `aabb_trim_fraction=0.0`；每帧单独复制渲染位置，
-把 `z < 0.49 m` 的点标为非有限值，由原生重建器过滤。
-
-此过滤只作用于渲染副本，不回写求解器。全部粒子仍参与物理、保存和统计，
-没有冻结水面或删除散落粒子。水流和碗内液面使用 OptiX 光追及表面网格重建。
-
-最终完整运行结果：
-
-| 最终位置 | 粒子数 | 名义水量 |
-| --- | ---: | ---: |
-| 碗内 | 41,594 | 140.38 mL |
-| 杯内 | 34,698 | 117.11 mL |
-| 容器外 | 246 | 0.83 mL |
-
-散落比例为 0.32%，不是无泄漏仿真。第 19 秒至结束的每秒及最终快照中，
-碗内速度均方根平均 **5.05 mm/s**、最大 5.87 mm/s；99 分位水面高度极差
-约 **0.074 mm**。静水对照在相同旧版快照、相同零初速度及固定容器下各运行
-5 秒，速度均方根从 0.13404 m/s 降到 0.004749 m/s（降低 96.46%）；
-这是特定对照结果，不是完整倒水全过程的统一降幅。
-
-两份完整视频各 744 帧、31 秒，18 秒静置近景片段为 432 帧，均逐帧解码通过。
-31 份粒子快照的数量均为 76,538，位置和速度均有限。
-这些是一次完整运行和离散快照的结果，不代表批量成功率。
-下载包保留最终快照和全部统计，其他每秒快照可由运行脚本重新生成。
-
-## 空杯任务及轨迹生成
-
-首次运行会自动下载并缓存任务网格，也可提前下载。
-`EMBODICHAIN_DATA_ROOT` 可覆盖缓存根目录：
+## 独立 Sim 演示
 
 ```bash
-embodichain data download --name PourCupAssets
+python scripts/tutorials/sim/franka_dfsph_pour_demo.py --headless
 ```
 
+默认从 `PourCupDemoData/trajectory.pt` 获取原始已验证轨迹，使用 OfflineRT 渲染；
+运行时杯子网格来自 `PourCupAssets/cup.glb`，接水碗由脚本生成。
+去掉 `--headless` 可打开原生窗口；`--renderer fast-rt` 可切换到 FastRT。
+`--trajectory` 可指定其他轨迹，`--seconds 2` 可限制运行时长。
+默认结果保存到 `outputs/franka_pour_cup/dfsph_stable_bowl/`，包括视频、关键帧、
+粒子状态和 `validation.json`。原始演示的环境版本与统计见下载包中的报告。
+
+## 空杯 Task Program 部署
+
+以下两个部署只生成空杯任务，不包含 SPH 或接水碗。
 Newton + MuJoCo-Warp：
 
 ```bash
@@ -206,53 +122,20 @@ embodichain run-env \
   --trajectory_save_dir outputs/franka_pour_cup/mug_trajectories
 ```
 
-YAML 部署均为空杯任务，不直接加载 DFSPH 或碗。
-Newton 版杯心 `[-0.75, 0, 0.552]` m，机械臂基座在原点、朝向 180°。
-50 cm 高的支撑台与侧抓预备姿态减少了腕部折叠；夹爪沿世界 -X 方向接近，
-两指沿 Y 方向夹住杯把，TCP 距杯心朝机械臂方向 6.5 cm，抬升约 12 cm。
-Default 版保留地面布局和约 20 cm 抬升。
-
-Newton 配置使用 `mujoco_warp`、`solver: newton`、`implicitfast`，
-5 ms 物理步分成 50 个子步；杯子和指腹使用 `condim: 6` 的抗扭/滚动摩擦。
-运动学继续使用 FEP IK 和 `ik_interp`，没有新增 IK 或动作实现。
-两种执行策略均为 `projected`，程序完成须结合实际杯子位姿和录像判断。
-
-下载包中的 MuJoCo 轨迹来自固定种子 0 的一次完整成功运行，1160 帧、20 Hz，
-包含侧抓、抬升、倾倒、回正、放回和松爪。相对优化前布局，累计关节转动量
-从 19.23 rad 降到 6.03 rad（减少 68.7%），最大关节速度从 0.473 rad/s
-降到 0.163 rad/s。杯子最大抬升 11.98 cm、实际最大倾角 66.09°，
-最终倾斜 0.28°、放回误差 2.09 mm。实际杯子倾角小于 80° 指令，
-夹持中仍有相对转动。详细记录见资料目录的 `newton_smooth_validation.json`。
-
-## YAML 分工
-
-两种后端各使用 6 份 YAML，共 12 份：
-
-| 配置 | 内容 |
+| 配置 | 职责 |
 | --- | --- |
-| `task.franka.yaml` / `task.franka.newton.yaml` | 运行入口、种子、环境/机器人/程序/策略选择、初始姿态 |
-| `env.yaml` / `env.newton.yaml` | 物理后端、杯子和台面、接触参数、灯光、录制和复位 |
-| `task_program/program*.yaml` | Pick → Pour → Place、放回目标、稳定等待与终点验证 |
-| `task_program/integration*.yaml` | 杯子语义绑定、侧抓位姿、抬升距离、80° 倾倒指令 |
-| `components/embodiments/franka_panda_firm_grip*.yaml` | 原 Franka 模型、传感器、夹爪力度、接触参数和技能声明 |
-| `components/execution_policies/trajectory_open_loop_pour_cup.yaml` / `trajectory_open_loop_gentle.yaml` | IK 插值，分别 240 / 360 点，动作后稳定 0.4 秒 |
-
-组件路径以 `embodichain_tasks/configs/` 为根。为适配当前主分支，夹爪控制组
-使用 `eef`；Default 策略单独命名，避免覆盖现有共享慢速策略。
-机器人 link / joint 资产名保持原值。
+| `task.franka*.yaml` | 运行入口及环境、机器人、程序和执行策略选择 |
+| `env.yaml` / `env.newton.yaml` | 空杯场景、物理后端、接触参数、录制和复位 |
+| `env.fluid.yaml` | SPH 场景、初始水体、流体观测和每环境相机录制 |
+| `task_program/program*.yaml` | Pick → Pour → Place、稳定等待和终点验证 |
+| `task_program/integration*.yaml` | 语义绑定、侧抓位姿、抬升距离和倾倒指令 |
+| `components/embodiments/franka_panda_*.yaml` | 机器人、传感器、接触参数和技能声明 |
+| `components/execution_policies/trajectory_open_loop_*.yaml` | 轨迹插值和动作后稳定策略 |
 
-## 验证和范围
-
-静态检查两个部署：
+组件路径以 `embodichain_tasks/configs/` 为根。静态检查空杯部署：
 
 ```bash
 python .agents/skills/add-task-program/scripts/inspect_deployment.py \
   embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml \
   embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml
 ```
-
-原始 PR #771 新增任务配置、教程、资产和演示证据；当前接入分支另增加了 EC 流体公共 API。
-没有新建教程单元测试；使用部署检查、现有配置/资源测试、语法检查和 GPU
-短测验证。完整演示最初在提交 `3bb2a03c5` 上录制，分支基于 `453675d8a` 整理；
-当前分支的检查结果单独记录在 PR，不能把原录像当作新基线的完整复跑。
-现有上下文已覆盖配置和模拟器接入边界，无需增加架构文档。
diff --git a/scripts/tutorials/sim/assets/franka_dfsph_pour/README.md b/scripts/tutorials/sim/assets/franka_dfsph_pour/README.md
deleted file mode 100644
index 15560102c..000000000
--- a/scripts/tutorials/sim/assets/franka_dfsph_pour/README.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# Franka DFSPH 演示资料
-
-轨迹、参考快照、录像、预览图和验证报告已迁移到
-[Hugging Face task assets](https://huggingface.co/datasets/DexForceAI/embodichain_data/tree/main/task_assets)，不再随代码仓库提交。
-
-```bash
-embodichain data download --name PourCupDemoData
-```
-
-```python
-from pathlib import Path
-from embodichain.data import get_data_path
-
-trajectory = Path(get_data_path("PourCupDemoData/trajectory.pt"))
-print(trajectory.parent)  # 同目录包含录像、参考快照及原始验证报告
-```
-
-运行时网格单独使用 `PourCupAssets`，任务无需下载演示录像。
-资产包提供 MD5 校验，远端 `task_assets/manifest.json` 记录每个文件的 SHA-256。
-`particles_30.npz` 仅为历史最终状态参考，不用于初始化水体；默认水体仍在内存中采样。
-
-报告保留原录制环境和提交来源；它们不是新版本完整复跑的声明。
-运行方式见[任务 README](../../../../../embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md)。

From 0dd4d16c1fa633d183ef4e05691d6da274da1960 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Wed, 14 Oct 2026 23:20:37 +0800
Subject: [PATCH 11/19] fix(fluid): preserve cleanup and selected replay state

---
 .../topics/manager-functor/manager-functor.md |  6 +-
 embodichain/lab/gym/envs/managers/record.py   | 16 +++-
 .../lab/scripts/run_fluid_task_program.py     |  2 +-
 embodichain/lab/sim/sim_manager.py            |  4 +-
 tests/gym/envs/managers/test_record.py        | 47 +++++++++++
 .../scripts/test_run_fluid_task_program.py    | 80 +++++++++++++++++++
 tests/sim/objects/test_rigid_constraint.py    |  1 +
 7 files changed, 148 insertions(+), 8 deletions(-)

diff --git a/agent_context/topics/manager-functor/manager-functor.md b/agent_context/topics/manager-functor/manager-functor.md
index 8d6895fac..5245ebd73 100644
--- a/agent_context/topics/manager-functor/manager-functor.md
+++ b/agent_context/topics/manager-functor/manager-functor.md
@@ -112,8 +112,10 @@ helper. See [profiling](../env-framework/profiling.md).
 `record_camera_data` is an interval event, independent of policy dataset
 recording. It renders its camera group before reading the buffers because
 interval events precede observation rendering. Views use arena-relative camera
-extrinsics. `per_env=True` retains separate owned CPU frames and episode counters;
-selected save/discard only touches those rows. The default remains a tiled video.
+extrinsics. `per_env=True` retains separate owned CPU frames and episode counters.
+It captures only the event's selected `env_ids` within `max_env_num`, preserving
+their original arena IDs; `None` captures all views within that limit. Selected
+save/discard only touches those rows. The default remains a tiled video.
 `fps=None` follows control frequency for an every-step recording event.
 
 Reset commits or discards frames via `save_and_clear` / `discard_and_clear`.
diff --git a/embodichain/lab/gym/envs/managers/record.py b/embodichain/lab/gym/envs/managers/record.py
index e166519bc..9038c6de9 100644
--- a/embodichain/lab/gym/envs/managers/record.py
+++ b/embodichain/lab/gym/envs/managers/record.py
@@ -41,7 +41,7 @@ class record_camera_data(Functor):
 
     By default, views are tiled into one video at 20 fps. Set ``per_env=True``
     to save one video per environment, with independent episode counters and
-    selected-row save/discard. ``fps=None`` uses the environment's control
+    selected-row capture/save/discard. ``fps=None`` uses the environment's control
     frequency; use an explicit fps when capturing less often than every step.
 
     Frames are committed by :meth:`save_and_clear` before reset. Closing the
@@ -239,7 +239,8 @@ def __call__(
 
         Args:
             env: Owning environment.
-            env_ids: Event rows; all camera rows are captured at a global interval.
+            env_ids: Rows to capture in per-environment mode, or all camera rows
+                when None. Tiled recording always captures all camera rows.
             name: Camera name, configured at initialization.
             resolution: Image width and height, configured at initialization.
             eye: Arena-relative camera position.
@@ -262,7 +263,16 @@ def __call__(
         num_frames = min(rgb.shape[0], max_env_num)
         rgb = rgb[:num_frames]
         if self._per_env:
-            for env_id, frame in enumerate(rgb[..., :3].detach().cpu().numpy()):
+            selected_env_ids = list(range(num_frames))
+            frames = rgb[..., :3]
+            if env_ids is not None:
+                selected_env_ids = [
+                    int(env_id)
+                    for env_id in torch.as_tensor(env_ids).reshape(-1).cpu().tolist()
+                    if 0 <= int(env_id) < num_frames
+                ]
+                frames = frames[selected_env_ids]
+            for env_id, frame in zip(selected_env_ids, frames.detach().cpu().numpy()):
                 self._env_frames.setdefault(env_id, []).append(frame.copy())
             return
         rgb = self._draw_frames_into_one_image(rgb)[..., :3].cpu().numpy()
diff --git a/embodichain/lab/scripts/run_fluid_task_program.py b/embodichain/lab/scripts/run_fluid_task_program.py
index dea19c596..69c3f5b1b 100644
--- a/embodichain/lab/scripts/run_fluid_task_program.py
+++ b/embodichain/lab/scripts/run_fluid_task_program.py
@@ -150,7 +150,7 @@ def _replay(args, trajectory: Path, output: Path) -> None:
                 before = (
                     env.sim.get_fluid_object("water").data.particle_state_w[1].clone()
                 )
-                env.reset(
+                obs, info = env.reset(
                     options={
                         "reset_ids": torch.tensor([0], device=env.device),
                         "save_data": False,
diff --git a/embodichain/lab/sim/sim_manager.py b/embodichain/lab/sim/sim_manager.py
index fd69ae500..5fa1b3fed 100644
--- a/embodichain/lab/sim/sim_manager.py
+++ b/embodichain/lab/sim/sim_manager.py
@@ -607,6 +607,8 @@ def _initialize_simulation(
         self._gizmos: dict[str, object] = {}
         self._markers: dict[str, _AxisMarkerGroup] = {}
         self._constraints: dict[str, RigidConstraint] = {}
+        self._fluid_objects: dict[str, FluidObject] = {}
+        self._rigid_trajectory_controls: dict[str, tuple] = {}
         self._visual_materials: dict[str, VisualMaterial] = {}
         self._texture_cache: dict[str, torch.Tensor | list[torch.Tensor]] = {}
 
@@ -711,8 +713,6 @@ def _initialize_simulation(
         self._rigid_objects: Dict[str, RigidObject] = dict()
         self._rigid_object_groups: Dict[str, RigidObjectGroup] = dict()
         self._deformable_objects: Dict[str, DeformableObject] = dict()
-        self._fluid_objects: dict[str, FluidObject] = {}
-        self._rigid_trajectory_controls: dict[str, tuple] = {}
         self._articulations: Dict[str, Articulation] = dict()
         self._robots: Dict[str, Robot] = dict()
 
diff --git a/tests/gym/envs/managers/test_record.py b/tests/gym/envs/managers/test_record.py
index f5e6e477e..64c619c25 100644
--- a/tests/gym/envs/managers/test_record.py
+++ b/tests/gym/envs/managers/test_record.py
@@ -122,6 +122,53 @@ def test_per_environment_reset_and_discard_preserve_unselected_recordings(monkey
     assert not recorder._env_frames[1]
 
 
+@pytest.mark.parametrize(
+    ("env_ids", "max_env_num", "expected_env_ids"),
+    [
+        (None, 3, [0, 1, 2]),
+        ([2, 0], 3, [2, 0]),
+        ([2, 0], 2, [0]),
+        ([], 3, []),
+        ([2], 2, []),
+    ],
+)
+def test_per_environment_capture_honors_event_rows_and_recording_limit(
+    env_ids, max_env_num, expected_env_ids, monkeypatch
+):
+    env, recorder = _make_recorder(per_env=True)
+    # Pixel values identify original arena IDs even for reordered selections.
+    env.sim.camera.rgb = torch.stack(
+        [torch.full((4, 6, 4), env_id, dtype=torch.uint8) for env_id in range(3)]
+    )
+    selection = None if env_ids is None else torch.tensor(env_ids, dtype=torch.long)
+    recorder(env, selection, name="views", max_env_num=max_env_num)
+    save_video = Mock()
+    monkeypatch.setattr(record_module, "images_to_video", save_video)
+
+    recorder.save_and_clear()
+
+    assert [call.args[2] for call in save_video.call_args_list] == [
+        f"episode_0_views_env{env_id}" for env_id in expected_env_ids
+    ]
+    for env_id, call in zip(expected_env_ids, save_video.call_args_list):
+        np.testing.assert_array_equal(call.args[0][0], np.full((4, 6, 3), env_id))
+
+
+def test_per_environment_capture_preserves_frames_for_rows_not_due(monkeypatch):
+    env, recorder = _make_recorder(per_env=True)
+    save_video = Mock()
+    monkeypatch.setattr(record_module, "images_to_video", save_video)
+    recorder(env, None, name="views")
+    # After a partial reset, per-row event intervals can be out of phase.
+    recorder(env, torch.tensor([1]), name="views")
+
+    recorder.save_and_clear()
+
+    assert [
+        (call.args[2], len(call.args[0])) for call in save_video.call_args_list
+    ] == [("episode_0_views_env0", 1), ("episode_0_views_env1", 2)]
+
+
 def test_failed_per_environment_save_keeps_frames_for_retry(monkeypatch):
     env, recorder = _make_recorder(per_env=True)
     recorder(env, None, name="views")
diff --git a/tests/lab/scripts/test_run_fluid_task_program.py b/tests/lab/scripts/test_run_fluid_task_program.py
index a309567d5..3d58be4e9 100644
--- a/tests/lab/scripts/test_run_fluid_task_program.py
+++ b/tests/lab/scripts/test_run_fluid_task_program.py
@@ -172,6 +172,86 @@ def step(self, action):
     ]
 
 
+@pytest.mark.parametrize("reset_step", [0, 1])
+def test_partial_reset_reports_current_row_clocks_and_observations(
+    reset_step, monkeypatch, tmp_path
+):
+    from embodichain.lab.gym.envs import fluid_replay
+    from embodichain.lab.gym.envs.managers import observations
+
+    class Environment:
+        def __init__(self, cfg, **kwargs):
+            self.num_envs = cfg.num_envs
+            self.device = torch.device("cpu")
+            self.step_dt = 1 / 24
+            self.replay_duration = 2 * self.step_dt
+            self.active_joint_ids = [0]
+            self.source_qualification = {"accepted": True}
+            self.close = Mock()
+            self.clock = torch.zeros(self.num_envs)
+            self.water = SimpleNamespace(
+                num_particles=1,
+                data=SimpleNamespace(particle_state_w=torch.zeros(self.num_envs, 1, 6)),
+            )
+            self.sim = SimpleNamespace(get_fluid_object=lambda uid: self.water)
+
+        def snapshot(self):
+            return {"clock": self.clock.clone()}, {
+                "fluid_replay_time_s": self.clock.clone(),
+                "fluid_replay_finished": self.clock >= self.replay_duration,
+            }
+
+        def reset(self, *, seed=None, options=None):
+            ids = options["reset_ids"] if options else torch.arange(self.num_envs)
+            self.clock[ids] = 0
+            self.water.data.particle_state_w[ids] = 0
+            return self.snapshot()
+
+        def step(self, action):
+            self.clock += self.step_dt
+            self.water.data.particle_state_w[..., 0] += 1
+            obs, info = self.snapshot()
+            return obs, None, None, None, info
+
+    monkeypatch.setattr(fluid_replay, "FluidReplayEnv", Environment)
+    monkeypatch.setattr(
+        observations,
+        "get_fluid_summary",
+        lambda env, obs, *args: torch.stack(
+            (obs["clock"], torch.ones(env.num_envs)), dim=-1
+        ),
+    )
+    monkeypatch.setattr(
+        observations,
+        "get_fluid_volume_in_region",
+        lambda env, *args, **kwargs: torch.zeros(env.num_envs, 1),
+    )
+    args = argparse.Namespace(
+        replay_task=str(TASK),
+        num_envs=2,
+        renderer="no-render",
+        particle_spacing=None,
+        speed=2,
+        seconds=None,
+        partial_reset_step=reset_step,
+    )
+
+    runner._replay(args, tmp_path / "source.pt", tmp_path)
+
+    result = json.loads((tmp_path / "fluid_gym_result.json").read_text())
+    step_dt = 1 / 24
+    expected_times = (
+        [[0, step_dt], [step_dt, 2 * step_dt]]
+        if reset_step == 0
+        else [[step_dt, step_dt], [0, 2 * step_dt]]
+    )
+    assert len(result["samples"]) == len(expected_times)
+    for sample, expected in zip(result["samples"], expected_times):
+        assert sample["time_s"] == pytest.approx(expected)
+        assert [row[0] for row in sample["summary"]] == pytest.approx(expected)
+    assert result["replay_complete"] == [False, True]
+
+
 def test_spacing_override_rejects_fixed_points_before_scene_creation(
     monkeypatch, tmp_path
 ):
diff --git a/tests/sim/objects/test_rigid_constraint.py b/tests/sim/objects/test_rigid_constraint.py
index bfb7cb9db..1be6779b5 100644
--- a/tests/sim/objects/test_rigid_constraint.py
+++ b/tests/sim/objects/test_rigid_constraint.py
@@ -264,6 +264,7 @@ def __init__(self, num_envs=4, arenas=None):
         self._rigid_objects = {}
         self._rigid_object_groups = {}
         self._deformable_objects = {}
+        self._fluid_objects = {}
         self._articulations = {}
         self._constraints = {}
         self.device = torch.device("cpu")

From 7c17d77442d91c0a23157907f87f029cd2ca2498 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Thu, 15 Oct 2026 03:25:26 +0800
Subject: [PATCH 12/19] feat(fluid): solve pouring demos and Task Program
 online

---
 .../topics/env-framework/env-framework.md     |   4 +-
 .../topics/env-framework/fluid-replay.md      |  40 +-
 .../topics/simulation-system/fluids.md        |  22 +-
 embodichain/data/assets/task_assets.py        |   4 +-
 .../lab/gym/utils/_component_composition.py   |   2 +-
 embodichain/lab/gym/utils/gym_utils.py        |   2 +-
 .../lab/scripts/run_fluid_task_program.py     | 381 ++++-----
 embodichain/lab/sim/cfg/rigid_object.py       |  12 +-
 .../lab/sim/objects/backends/newton.py        |  14 +-
 embodichain/lab/sim/physics/newton.py         |  13 +-
 embodichain/lab/sim/spawn/descriptors.py      |  35 +-
 .../franka_panda_fluid_replay.yaml            |  32 -
 .../manipulation/tableware/pour_cup/README.md | 168 ++--
 .../tableware/pour_cup/env.fluid.yaml         | 178 ++--
 .../tableware/pour_cup/task.franka.fluid.yaml |  24 +-
 .../task_program/integration.fluid.yaml       |  97 +++
 .../pour_cup/task_program/program.fluid.yaml  |  96 +++
 .../manipulation/tableware/pour_cup.py        |  28 -
 .../tutorials/sim/franka_dfsph_pour_demo.py   | 757 ++++++------------
 tests/gym/envs/test_fluid_integration.py      |  29 +-
 tests/gym/envs/test_fluid_replay_runtime.py   |  11 +-
 .../scripts/test_run_fluid_task_program.py    | 299 +++----
 .../sim/objects/test_fluid_online_runtime.py  | 285 +++++++
 tests/sim/spawn/test_descriptors.py           |  48 ++
 24 files changed, 1360 insertions(+), 1221 deletions(-)
 delete mode 100644 embodichain_tasks/configs/components/embodiments/franka_panda_fluid_replay.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.fluid.yaml
 create mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.fluid.yaml
 delete mode 100644 embodichain_tasks/embodichain_tasks/manipulation/tableware/pour_cup.py
 create mode 100644 tests/sim/objects/test_fluid_online_runtime.py

diff --git a/agent_context/topics/env-framework/env-framework.md b/agent_context/topics/env-framework/env-framework.md
index 362399b8c..c2d2ad5e0 100644
--- a/agent_context/topics/env-framework/env-framework.md
+++ b/agent_context/topics/env-framework/env-framework.md
@@ -27,8 +27,8 @@ Lightweight RL environments have a separate owner in
 
 Read [configuration and registration](configuration.md) for task-first layout,
 component ownership, path resolution, config-owned IDs, and task listing.
-Read [fluid replay](fluid-replay.md) for Task Program generation in MuJoCo followed
-by independent SPH evaluation, fluid configuration and coordinate conventions.
+Read [fluid Gym integration](fluid-replay.md) for online Task Program execution
+with rigid/SPH dynamics, fluid configuration and coordinate conventions.
 Read [execution](execution.md) for hooks, bridge acceptance, reset ordering,
 wrappers, and replay. Read [profiling](profiling.md) only for instrumentation.
 
diff --git a/agent_context/topics/env-framework/fluid-replay.md b/agent_context/topics/env-framework/fluid-replay.md
index 0523d72b8..7e1a69182 100644
--- a/agent_context/topics/env-framework/fluid-replay.md
+++ b/agent_context/topics/env-framework/fluid-replay.md
@@ -20,26 +20,29 @@ arena-local. Region measurement and `events.reset_fluid_in_container()` add
 arena offsets before comparing or placing particles. Region volume is particle
 count times nominal spacing cubed, not a reconstructed-surface measurement.
 
-## Two-stage Task Program workflow
+## Online Task Program workflow
 
-`lab/scripts/run_fluid_task_program.py` generates the existing pour-cup Task
-Program in MuJoCo, requires program acceptance and measured cup lift/tilt/return,
-and stores provenance in trajectory metadata. A separate process/world runs
-`gym/envs/fluid_replay.py::FluidReplayEnv` with live SPH. The physical deployments
-remain separate files (`env.newton.yaml` versus `env.fluid.yaml`); the latter
-reuses DexSim's SPH solver configuration directly.
+`lab/scripts/run_fluid_task_program.py` runs the configured pour-cup deployment
+through ordinary `EmbodiedEnv` and `execute_demo_episode()`. The environment
+component selects DexSim SPH with an existing MuJoCo configuration nested under
+`rigid_solver_cfg`. Robot drive targets, dynamic grasp contacts and fluid state
+advance in one World, with no recorded trajectory input. SPH reads live rigid
+boundaries; fluid reaction forces are not fed back into the rigid solver.
+The fluid program measures cup lift and alignment, transports the held cup
+to a receiver-relative pour pose through the existing MoveHeldObject lowerer,
+then pours and returns it. The standalone Sim tutorial uses the same physical
+component and relative pour target with freshly planned Atomic Action targets.
 
-Replay broadcasts one accepted source row across environments. It adopts the
-recorded fixed robot root, checks joint compatibility, interpolates measured
-joints/rigid poses on the physics grid, and publishes kinematic boundary twist.
-Each environment has its own clock and selected reset/refill. Newton substeps
-must be one; control intervals may contain multiple physics steps. Completion
-is truncation; reset is required once every row is exhausted.
+Program completion and physical qualification are separate: the runner measures
+cup lift, maximum tilt, final return error and received volume. Projected effect
+assurance alone does not prove grasp or pouring success. Optional trajectory
+recording stores measured online state for inspection. `--seconds` qualifies
+only a bounded fragment, not completion of the task.
 
-This is one-way state replay: policy actions do not drive the robot and fluid
-forces do not affect the recorded motion. Action managers, trajectory recording
-and active policy dataset recorders are rejected. Task Program acceptance is
-source provenance; fluid capture metrics remain separate measured outcomes.
+`FluidReplayEnv` remains an explicit diagnostic utility for accepted historical
+recordings; it is not the default deployment or task runner. It requires a
+separate kinematic configuration, one Newton substep and fixed-root source
+states, and rejects action managers and policy dataset/trajectory recording.
 
 ## Rendering and recording
 
@@ -60,6 +63,7 @@ NoRender qualification removes the recorder before constructing the scene.
 
 Production configuration/measurement checks are in
 `tests/gym/envs/test_fluid_integration.py`; live isolation checks are in
-`tests/sim/objects/test_fluid_batch_runtime.py`; actual primary-surface visibility
+`tests/sim/objects/test_fluid_batch_runtime.py` and
+`tests/sim/objects/test_fluid_online_runtime.py`; actual primary-surface visibility
 is checked by `tests/sim/objects/test_fluid_visibility.py`. Generated trajectories/videos
 are local qualification evidence, not packaged task assets.
diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
index a4d2f22cd..80a3df4ed 100644
--- a/agent_context/topics/simulation-system/fluids.md
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -21,6 +21,17 @@ Writes update Scene particle batches; resetting transforms a saved local
 particle template or restores the immutable initial state. Render topology is
 reconstructed and is not inherited from volume/surface deformables.
 
+An opt-in `SPHSolverCfg.rigid_solver_cfg` composes the existing MuJoCo solver
+with SPH using Newton-owned views on one model/clock. MuJoCo owns rigid bodies
+and joints; SPH owns particles and reads live body proxies. Rigid microsteps
+refine contact integration without adding simulation time. This is online
+one-way coupling; there is no fluid reaction feedback. `RigidObjectCfg` may
+provide `particle_collision_shape`, an SDF mesh on the same body, so rigid
+contact hulls and a hollow fluid boundary coexist without extra mass or a
+pose follower. The native adapter owns particle-only SDF cooking and mass
+exclusion. Selected fluid state writes invalidate SPH caches without clearing
+the rigid solver history.
+
 Kinematic rigid boundaries can be written with `set_kinematic_state()` or
 driven before each solver substep using `register_kinematic_rigid_trajectory()`.
 Registered host controls disable graph replay and rewind selected clocks on
@@ -39,8 +50,9 @@ memory; replica binding and selected reset never reopen or regenerate a source.
 Changing spacing or fill level before construction regenerates the point count;
 prepared topology remains fixed. Points/NPZ imports already contain fixed
 sampling and reject volume-only profile/fill-level controls. The default pour
-scene uses a profile source with no particle file or disk cache. Its existing
-stationary replay hold provides physical settling after direct initialization.
+scene uses a profile source with no particle file or disk cache. Gym reset restores/refills selected rows without advancing the shared world;
+normal approach actions provide physical settling. The standalone Sim host
+resets configured robot/object state before a short initialization warmup.
 
 Native fluid geometry requires FastRT (`fast-rt`) or OfflineRT (`rt`). The
 manager rejects Hybrid in both the resolved configuration and the initialized
@@ -53,14 +65,14 @@ Validate camera pixels with visible and hidden fluid, not only particle counts.
 Native rendering is published through the existing Newton render bridge.
 Display clipping acts only on copied positions; Viser consumes detached point
 clouds. NoRender fluid mode does not require optical materials. Ordinary rigid
-objects may supply a separate `visual_shape`; SDF cooking stays on the physical
-shape. Explicit `force_sdf=False` permits pre-baked mesh SDFs.
+objects may supply a separate `visual_shape`; SDF cooking stays on the collision
+shape or the separate particle boundary. Explicit `force_sdf=False` permits pre-baked mesh SDFs.
 
 Focused checks are `tests/sim/test_fluid_cfg.py`,
 `tests/sim/objects/test_fluid_object.py` and the serial CUDA lifecycle test
 `tests/sim/objects/test_fluid_runtime.py` and `test_fluid_batch_runtime.py`.
 `tests/sim/objects/test_fluid_visibility.py` checks real camera pixels after
 stepping and direct state writes, including display clipping.
-For YAML declarations, refill observations and Task Program replay, see
+For YAML declarations, refill observations and online Task Program execution, see
 [fluid Gym integration](../env-framework/fluid-replay.md). The pouring tutorial is an end-to-end
 qualification workload, not a unit-test import target.
diff --git a/embodichain/data/assets/task_assets.py b/embodichain/data/assets/task_assets.py
index 3cb73f8e5..d27958c7f 100644
--- a/embodichain/data/assets/task_assets.py
+++ b/embodichain/data/assets/task_assets.py
@@ -70,8 +70,8 @@ class PourCupAssets(_TaskAsset):
 class PourCupDemoData(_TaskAsset):
     """Recorded pouring trajectory, videos and original validation evidence.
 
-    ``trajectory.pt`` is the MuJoCo recording used by the standalone pouring
-    tutorial. ``particles_30.npz`` is a final reference snapshot and is not used
+    ``trajectory.pt`` is a historical MuJoCo recording for diagnostic replay.
+    Online pouring tutorials and tasks do not load it. ``particles_30.npz`` is a final reference snapshot and is not used
     to initialize fluids. Resolve files through ``get_data_path()``.
     """
 
diff --git a/embodichain/lab/gym/utils/_component_composition.py b/embodichain/lab/gym/utils/_component_composition.py
index b33d0d402..9c2387d23 100644
--- a/embodichain/lab/gym/utils/_component_composition.py
+++ b/embodichain/lab/gym/utils/_component_composition.py
@@ -69,7 +69,7 @@ def _resolve_physical_source_paths(simulation: dict, base_dir: Path) -> dict:
     result = deepcopy(dict(simulation))
     for field in ("background", "rigid_object"):
         for item in result.get(field, []):
-            for key in ("shape", "visual_shape"):
+            for key in ("shape", "visual_shape", "particle_collision_shape"):
                 shape = item.get(key) or {}
                 path = shape.get("fpath")
                 if isinstance(path, str) and path.startswith(("./", "../")):
diff --git a/embodichain/lab/gym/utils/gym_utils.py b/embodichain/lab/gym/utils/gym_utils.py
index 11f6bc317..6f825d0a7 100644
--- a/embodichain/lab/gym/utils/gym_utils.py
+++ b/embodichain/lab/gym/utils/gym_utils.py
@@ -491,7 +491,7 @@ def config_to_cfg(
     from embodichain.data import get_data_path
 
     def _resolve_mesh_asset_paths(obj_dict: dict[str, Any]) -> None:
-        for key in ("shape", "visual_shape"):
+        for key in ("shape", "visual_shape", "particle_collision_shape"):
             shape = obj_dict.get(key)
             if (
                 shape is not None
diff --git a/embodichain/lab/scripts/run_fluid_task_program.py b/embodichain/lab/scripts/run_fluid_task_program.py
index 69c3f5b1b..fc023a048 100644
--- a/embodichain/lab/scripts/run_fluid_task_program.py
+++ b/embodichain/lab/scripts/run_fluid_task_program.py
@@ -14,195 +14,181 @@
 # limitations under the License.
 # ----------------------------------------------------------------------------
 
-"""Generate a qualified Task Program trajectory, then evaluate fluid Gym replay."""
+"""Run a Task Program with live robot/cup contact and one-way SPH fluids."""
 
 from __future__ import annotations
 
 import argparse
+from collections.abc import Iterable, Iterator
 import json
 from pathlib import Path
-import subprocess
-import sys
 import time
+from typing import Any
+
 import numpy as np
 import torch
-from scipy.spatial.transform import Rotation
 
-from embodichain.lab.gym.utils.gym_utils import config_to_cfg, load_trajectory
-from embodichain.lab.gym.utils.registration import make, discover_task_packages
 from embodichain.lab.gym.envs.demo import execute_demo_episode
+from embodichain.lab.gym.envs.embodied_env import EmbodiedEnvCfg
+from embodichain.lab.gym.envs.managers.cfg import SceneEntityCfg
+from embodichain.lab.gym.envs.managers.observations import (
+    get_fluid_summary,
+    get_fluid_volume_in_region,
+)
+from embodichain.lab.gym.utils.gym_utils import config_to_cfg
+from embodichain.lab.gym.utils.registration import discover_task_packages, make
 from embodichain.lab.sim import SimulationManager
-from embodichain.lab.sim.cfg import RenderCfg
 from embodichain.utils.config_paths import resolve_config_path
 from embodichain.utils.utility import load_config
 
 __all__ = ["main"]
 
-_TASK = "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/"
+_TASK = (
+    "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/"
+    "task.franka.fluid.yaml"
+)
 
 
-def _generate(source_path: Path, output: Path) -> None:
-    declaration = load_config(str(source_path))
-    cfg = config_to_cfg(declaration, source_path=source_path)
+def _configure(
+    task: Path,
+    *,
+    num_envs: int,
+    renderer: str,
+    output: Path,
+    particle_spacing: float | None = None,
+    record_trajectory: bool = False,
+) -> tuple[str, EmbodiedEnvCfg]:
+    declaration = load_config(str(task))
+    cfg = config_to_cfg(declaration, source_path=task)
     if cfg.task_program is None:
-        raise ValueError("Source deployment must select a Task Program.")
-    cfg.num_envs = 1
+        raise ValueError("The online fluid deployment must select a Task Program.")
+    solver = cfg.sim_cfg.physics_cfg.solver_cfg
+    if solver.get("solver_type") != "sph" or solver.get("rigid_solver_cfg") is None:
+        raise ValueError("Online fluid execution requires SPH with rigid_solver_cfg.")
+    if num_envs < 1:
+        raise ValueError("num_envs must be positive.")
+    cfg.num_envs = num_envs
     cfg.sim_cfg.headless = True
-    cfg.sim_cfg.render_cfg = RenderCfg(renderer="hybrid")
+    cfg.sim_cfg.render_cfg.renderer = renderer
     cfg.enable_sensor = False
-    if hasattr(cfg.events, "record_demo"):
-        delattr(cfg.events, "record_demo")
-    cfg.record_trajectory = True
-    env = make(declaration["id"], cfg=cfg)
-    try:
-        env.reset(seed=0)
-        result = execute_demo_episode(env)
-        report = result.to_metadata()
-        (output / "task_program_result.json").write_text(
-            json.dumps(report, indent=2) + "\n"
-        )
-        if not result.completed or not result.all_success:
-            raise RuntimeError("Task Program failed; fluid replay will not start.")
-        path = output / "task_program_trajectory.pt"
-        env.save_trajectory(str(path))
-        data = load_trajectory(path)
-        poses = data["states"]["rigid_objects", "cup", "pose"][0].cpu().numpy()
-        lift = float(np.max(poses[:, 2]) - poses[0, 2])
-        z_axis = Rotation.from_quat(poses[:, 3:]).as_matrix()[:, :, 2]
-        tilt = float(np.rad2deg(np.arccos(np.clip(z_axis[:, 2], -1, 1))).max())
-        return_error = float(np.linalg.norm(poses[-1, :3] - poses[0, :3]))
-        accepted = lift >= 0.05 and tilt >= 45.0 and return_error <= 0.03
-        qualification = {
-            "kind": "task_program",
-            "accepted": accepted,
-            "source_config": str(source_path),
-            "program_hash": data["meta"].get("program_hash"),
-            "lift_m": lift,
-            "max_tilt_deg": tilt,
-            "return_error_m": return_error,
-            "task_program_completed": result.completed,
-        }
-        data["meta"]["fluid_source"] = qualification
-        torch.save(data, path)
-        (output / "source_qualification.json").write_text(
-            json.dumps(qualification, indent=2) + "\n"
-        )
-        if not accepted:
-            raise RuntimeError(
-                "Measured cup lift/tilt/return failed source qualification."
-            )
-        print(json.dumps(qualification), flush=True)
-    finally:
-        env.close(exit_process=False)
-    del env
-    SimulationManager.flush_cleanup_queue()
-
-
-def _replay(args, trajectory: Path, output: Path) -> None:
-    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
-
-    path = resolve_config_path(args.replay_task).resolve()
-    cfg = config_to_cfg(load_config(str(path)), source_path=path)
-    cfg.num_envs = args.num_envs
-    cfg.sim_cfg.headless = True
-    cfg.sim_cfg.render_cfg.renderer = args.renderer
-    if args.renderer == "no-render":
-        cfg.enable_sensor = False
+    cfg.record_trajectory = record_trajectory
+    if renderer == "no-render":
         cfg.events.record_camera = None
+        for fluid in cfg.fluid_object:
+            fluid.render.mode = "none"
     else:
         cfg.events.record_camera.params["save_path"] = str(output / "videos")
-        cfg.events.record_camera.params["max_env_num"] = cfg.num_envs
-    if args.particle_spacing is not None:
-        # Resample the configured fill volume at the smoke-test resolution.
+        cfg.events.record_camera.params["max_env_num"] = num_envs
+    if particle_spacing is not None:
+        if not np.isfinite(particle_spacing) or particle_spacing <= 0:
+            raise ValueError("particle_spacing must be finite and positive.")
         fluid = cfg.fluid_object[0]
         if fluid.spawn.kind == "points":
-            raise ValueError(
-                "particle-spacing override requires a volume source; imported points have fixed sampling."
-            )
-        scale = args.particle_spacing / fluid.particle_spacing
+            raise ValueError("particle-spacing requires a procedural volume source.")
+        scale = particle_spacing / fluid.particle_spacing
         if fluid.render.render_radius is not None:
             fluid.render.render_radius *= scale
         for key in ("cell_size", "kernel_radius"):
             if key in fluid.render.mesh_surface:
                 fluid.render.mesh_surface[key] *= scale
-        fluid.particle_spacing = args.particle_spacing
-        fluid.particle_radius = 0.3 * args.particle_spacing
-        cfg.sim_cfg.physics_cfg.solver_cfg["particle_support_radius"] = (
-            2.25 * args.particle_spacing
-        )
-    env = FluidReplayEnv(cfg, trajectory=str(trajectory), speed=args.speed)
+        fluid.particle_spacing = particle_spacing
+        fluid.particle_radius = 0.3 * particle_spacing
+        solver["particle_support_radius"] = 2.25 * particle_spacing
+    return declaration["id"], cfg
+
+
+def _sample(env: Any) -> dict[str, Any]:
+    summary = get_fluid_summary(env, {}, SceneEntityCfg(uid="water"))
+    if not torch.isfinite(summary).all() or not torch.all(summary[:, -1] == 1):
+        raise RuntimeError("Fluid state became non-finite.")
+    received = get_fluid_volume_in_region(
+        env,
+        {},
+        [SceneEntityCfg(uid="water"), SceneEntityCfg(uid="receiver")],
+        lower=[-0.1, -0.1, 0.0045],
+        upper=[0.1, 0.1, 0.0675],
+        sphere_center=[0, 0, 0.088],
+        sphere_radius=0.093,
+    )
+    return {
+        "time_s": (env.elapsed_steps * env.step_dt).cpu().tolist(),
+        "received_ml": (received[:, 0] * 1e6).cpu().tolist(),
+        "summary": summary.cpu().tolist(),
+        "cup_pose": env.sim.get_rigid_object("cup").get_local_pose().cpu().tolist(),
+    }
+
+
+def _cup_qualification(poses: list[np.ndarray]) -> dict[str, Any]:
+    measured = np.stack(poses, axis=1)
+    lift = measured[..., 2].max(axis=1) - measured[:, 0, 2]
+    vertical = 1 - 2 * (measured[..., 3] ** 2 + measured[..., 4] ** 2)
+    tilt = np.rad2deg(np.arccos(np.clip(vertical, -1, 1))).max(axis=1)
+    return_error = np.linalg.norm(measured[:, -1, :3] - measured[:, 0, :3], axis=1)
+    return {
+        "lift_m": lift.tolist(),
+        "max_tilt_deg": tilt.tolist(),
+        "return_error_m": return_error.tolist(),
+        "accepted": ((lift >= 0.05) & (tilt >= 45) & (return_error <= 0.03)).tolist(),
+    }
+
+
+def _run(args: argparse.Namespace, output: Path) -> dict[str, Any]:
+    path = resolve_config_path(args.task).resolve()
+    task_id, cfg = _configure(
+        path,
+        num_envs=args.num_envs,
+        renderer=args.renderer,
+        output=output,
+        particle_spacing=args.particle_spacing,
+        record_trajectory=args.record_trajectory,
+    )
+    discover_task_packages()
+    env = make(task_id, cfg=cfg)
     try:
-        obs, info = env.reset(seed=0)
-        duration = (
-            env.replay_duration
-            if args.seconds is None
-            else min(args.seconds, env.replay_duration)
-        )
-        steps = int(np.ceil(duration / env.step_dt))
-        action = torch.zeros(
-            (env.num_envs, len(env.active_joint_ids)), device=env.device
-        )
-        samples = []
+        env.reset(seed=0)
+        cup = env.sim.get_rigid_object("cup")
+        poses = [cup.get_local_pose().cpu().numpy().copy()]
+        samples = [_sample(env)]
         started = time.perf_counter()
-        for index in range(steps):
-            obs, reward, terminated, truncated, info = env.step(action)
-            if args.partial_reset_step is not None and index == args.partial_reset_step:
-                before = (
-                    env.sim.get_fluid_object("water").data.particle_state_w[1].clone()
-                )
-                obs, info = env.reset(
-                    options={
-                        "reset_ids": torch.tensor([0], device=env.device),
-                        "save_data": False,
-                    }
-                )
-                torch.testing.assert_close(
-                    env.sim.get_fluid_object("water").data.particle_state_w[1],
-                    before,
-                    rtol=0,
-                    atol=0,
-                )
-            if index % max(1, round(1 / env.step_dt)) == 0 or index == steps - 1:
-                from embodichain.lab.gym.envs.managers.observations import (
-                    get_fluid_summary,
-                    get_fluid_volume_in_region,
-                )
-                from embodichain.lab.gym.envs.managers.cfg import SceneEntityCfg
+        sample_stride = max(1, round(1 / env.step_dt))
 
-                summary = get_fluid_summary(env, obs, SceneEntityCfg(uid="water"))
-                received = get_fluid_volume_in_region(
-                    env,
-                    obs,
-                    [SceneEntityCfg(uid="water"), SceneEntityCfg(uid="receiver")],
-                    lower=[-0.1, -0.1, 0.0045],
-                    upper=[0.1, 0.1, 0.0675],
-                    sphere_center=[0, 0, 0.088],
-                    sphere_radius=0.093,
-                )
-                if not torch.isfinite(summary).all() or not torch.all(
-                    summary[:, -1] == 1
-                ):
-                    raise RuntimeError("Fluid state became non-finite.")
-                sample = {
-                    "time_s": info["fluid_replay_time_s"].cpu().tolist(),
-                    "received_ml": (received[:, 0] * 1e6).cpu().tolist(),
-                    "summary": summary.cpu().tolist(),
-                }
-                samples.append(sample)
-                print(json.dumps(sample), flush=True)
+        def observe(actions: Iterable[Any], description: str) -> Iterator[Any]:
+            print(description, flush=True)
+            for action in actions:
+                yield action
+                poses.append(cup.get_local_pose().cpu().numpy().copy())
+                if int(env.elapsed_steps.max()) % sample_stride == 0:
+                    sample = _sample(env)
+                    samples.append(sample)
+                    print(json.dumps(sample), flush=True)
+
+        def stop() -> bool:
+            return (
+                args.seconds is not None
+                and float(env.elapsed_steps.max()) * env.step_dt >= args.seconds
+            )
+
+        result = execute_demo_episode(env, progress=observe, should_stop=stop)
+        if result.completed and args.settle_seconds:
+            target = env.robot.get_qpos()[:, env.active_joint_ids].clone()
+            for _ in range(round(args.settle_seconds / env.step_dt)):
+                env.step(target)
+                poses.append(cup.get_local_pose().cpu().numpy().copy())
+        samples.append(_sample(env))
+        qualification = _cup_qualification(poses)
         videos = []
         if args.renderer != "no-render":
-            # Closing discards uncommitted recordings. Commit the last episode
-            # explicitly, including short qualification runs without a reset.
             env.event_manager.get_functor("record_camera").save_and_clear()
             name = cfg.events.record_camera.params["name"]
             videos = [
-                str(Path("videos") / f"episode_0_{name}_env{env_id}.mp4")
-                for env_id in range(env.num_envs)
+                str(Path("videos") / f"episode_0_{name}_env{i}.mp4")
+                for i in range(env.num_envs)
             ]
+        if args.record_trajectory:
+            env.save_trajectory(str(output / "online_trajectory.pt"))
         report = {
-            "source": env.source_qualification,
-            "coupling": "one-way measured Task Program state replay",
+            "task": str(path),
+            "coupling": "online one-way rigid-to-fluid",
             "num_envs": env.num_envs,
             "renderer": args.renderer,
             "videos": videos,
@@ -210,96 +196,71 @@ def _replay(args, trajectory: Path, output: Path) -> None:
             "particle_count_per_env": env.sim.get_fluid_object("water").num_particles,
             "fluid_source": cfg.fluid_object[0].spawn.to_dict(),
             "particle_spacing_m": cfg.fluid_object[0].particle_spacing,
-            "density_kg_m3": cfg.fluid_object[0].density,
-            "partial_reset_step": args.partial_reset_step,
-            "replay_complete": info["fluid_replay_finished"].cpu().tolist(),
+            "physics_dt": cfg.sim_cfg.physics_dt,
+            "control_dt": env.step_dt,
+            "task_program": result.to_metadata(),
+            "cup": qualification,
             "wall_seconds": time.perf_counter() - started,
             "samples": samples,
         }
+        report["accepted"] = bool(
+            result.completed
+            and result.all_success
+            and all(qualification["accepted"])
+            and all(v >= 50 for v in samples[-1]["received_ml"])
+        )
         (output / "fluid_gym_result.json").write_text(
             json.dumps(report, indent=2) + "\n"
         )
+        print(
+            json.dumps(
+                {
+                    key: report[key]
+                    for key in ("accepted", "coupling", "cup", "wall_seconds")
+                }
+            ),
+            flush=True,
+        )
+        if args.seconds is None and not report["accepted"]:
+            raise RuntimeError(
+                "Online pouring failed physical qualification; inspect fluid_gym_result.json."
+            )
+        return report
     finally:
         env.close(exit_process=False)
-    del env
-    SimulationManager.flush_cleanup_queue()
+        del env
+        SimulationManager.flush_cleanup_queue()
 
 
 def main() -> None:
-    """Run Task Program source generation and independent fluid evaluation."""
+    """Execute the configured Task Program and fluid simulation in one World."""
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--source-task", default=_TASK + "task.franka.newton.yaml")
-    parser.add_argument("--replay-task", default=_TASK + "task.franka.fluid.yaml")
+    parser.add_argument("--task", default=_TASK)
     parser.add_argument(
-        "--output", type=Path, default=Path("outputs/fluid-task-program")
+        "--output", type=Path, default=Path("outputs/fluid-task-program-online")
     )
-    parser.add_argument("--trajectory", type=Path)
     parser.add_argument("--num-envs", type=int, default=2)
-    parser.add_argument("--speed", type=float, default=2.0)
     parser.add_argument(
-        "--renderer",
-        choices=["fast-rt", "no-render"],
-        default="fast-rt",
-        help="FastRT renders liquid surfaces; NoRender is for physics-only checks.",
+        "--renderer", choices=["fast-rt", "no-render"], default="fast-rt"
     )
     parser.add_argument("--seconds", type=float)
     parser.add_argument("--particle-spacing", type=float)
-    parser.add_argument("--partial-reset-step", type=int)
+    parser.add_argument("--settle-seconds", type=float, default=2.0)
     parser.add_argument(
-        "--phase",
-        choices=["all", "generate", "replay"],
-        default="all",
-        help=argparse.SUPPRESS,
+        "--record-trajectory",
+        action="store_true",
+        help="Optionally save measured online states for inspection.",
     )
     args = parser.parse_args()
-    import math
-
-    if args.num_envs < 1 or not math.isfinite(args.speed) or args.speed <= 0:
-        parser.error("num-envs and speed must be positive")
     if args.seconds is not None and (
-        not math.isfinite(args.seconds) or args.seconds <= 0
+        not np.isfinite(args.seconds) or args.seconds <= 0
     ):
         parser.error("seconds must be finite and positive")
-    if args.particle_spacing is not None and (
-        not math.isfinite(args.particle_spacing) or args.particle_spacing <= 0
-    ):
-        parser.error("particle-spacing must be finite and positive")
-    if args.partial_reset_step is not None and (
-        args.num_envs < 2 or args.partial_reset_step < 0
-    ):
-        parser.error(
-            "partial-reset-step requires at least two environments and a nonnegative step"
-        )
+    if not np.isfinite(args.settle_seconds) or args.settle_seconds < 0:
+        parser.error("settle-seconds must be finite and non-negative")
     output = args.output.resolve()
     output.mkdir(parents=True, exist_ok=True)
-    discover_task_packages()
-    source = resolve_config_path(args.source_task).resolve()
-    if args.phase == "generate":
-        _generate(source, output)
-        return
-    trajectory = (
-        args.trajectory.resolve()
-        if args.trajectory is not None
-        else output / "task_program_trajectory.pt"
-    )
-    if args.trajectory is None and args.phase == "all":
-        bootstrap = "import json,runpy,sys; sys.path[:]=json.loads(sys.argv.pop(1)); runpy.run_module('embodichain.lab.scripts.run_fluid_task_program',run_name='__main__')"
-        subprocess.run(
-            [
-                sys.executable,
-                "-c",
-                bootstrap,
-                json.dumps(sys.path),
-                "--phase",
-                "generate",
-                "--source-task",
-                str(source),
-                "--output",
-                str(output),
-            ],
-            check=True,
-        )
-    _replay(args, trajectory, output)
+    _run(args, output)
 
 
 if __name__ == "__main__":
diff --git a/embodichain/lab/sim/cfg/rigid_object.py b/embodichain/lab/sim/cfg/rigid_object.py
index 430a6b6ba..c0436b415 100644
--- a/embodichain/lab/sim/cfg/rigid_object.py
+++ b/embodichain/lab/sim/cfg/rigid_object.py
@@ -49,6 +49,13 @@ class RigidObjectCfg(ObjectBaseCfg):
     visual_shape: ShapeCfg | None = None
     """Optional independent render geometry; shape remains collision geometry."""
 
+    particle_collision_shape: ShapeCfg | None = None
+    """Optional Newton SDF mesh for particles on this same rigid body.
+
+    Rigid contacts and mass use :attr:`shape`. This boundary follows the body's
+    live pose and twist and contributes no mass or rigid contact geometry.
+    """
+
     render_enabled: bool = True
     """Whether to create native render geometry for this rigid object."""
 
@@ -79,8 +86,9 @@ def resolve_asset_physics_mode(self) -> AssetPhysicsMode:
     def from_dict(cls, init_dict: Dict[str, Any]) -> RigidObjectCfg:
         """Parse a rigid object and normalize legacy mesh collision ownership."""
         data = dict(init_dict)
-        if isinstance(data.get("visual_shape"), Mapping):
-            data["visual_shape"] = ShapeCfg.from_dict(data["visual_shape"])
+        for key in ("visual_shape", "particle_collision_shape"):
+            if isinstance(data.get(key), Mapping):
+                data[key] = ShapeCfg.from_dict(data[key])
         attrs_value = data.get("attrs")
         if isinstance(attrs_value, Mapping) and "mesh_collision_props" in attrs_value:
             shape_value = data.get("shape")
diff --git a/embodichain/lab/sim/objects/backends/newton.py b/embodichain/lab/sim/objects/backends/newton.py
index 33b164404..e68b5c5c9 100644
--- a/embodichain/lab/sim/objects/backends/newton.py
+++ b/embodichain/lab/sim/objects/backends/newton.py
@@ -204,7 +204,8 @@ def _configure_newton_mimic_compliance(
     backend = get_newton_backend(result.world)
     if (
         backend is None
-        or backend.solver_type != "mujoco_warp"
+        or backend.solver_type not in {"mujoco_warp", "sph"}
+        or backend.mujoco_solver is None
         or backend.cfg.requires_grad
         or (backend.model is not None and backend.model.requires_grad)
     ):
@@ -284,13 +285,19 @@ def _configure_newton_mimic_compliance(
             f"Newton model has no mimic constraint for joint pair {error.args[0]}."
         ) from error
 
-    solver = runtime.solver
+    solver = backend.mujoco_solver
     mapping = getattr(solver, "mjc_eq_to_newton_mimic", None)
     mjw_model = getattr(solver, "mjw_model", None)
     if mapping is None or mjw_model is None:
         raise RuntimeError("MuJoCo-Warp did not materialize Newton mimic rows.")
 
     mapping_values = np.asarray(mapping.numpy())
+    index_map = backend.get_mujoco_index_map(model.AttributeFrequency.CONSTRAINT_MIMIC)
+    if index_map is not None:
+        local_to_global = np.asarray(index_map.numpy())
+        valid = mapping_values >= 0
+        mapping_values = mapping_values.copy()
+        mapping_values[valid] = local_to_global[mapping_values[valid]]
     selected = np.isin(mapping_values, constraint_rows)
     if int(selected.sum()) != len(constraint_rows):
         raise RuntimeError(
@@ -299,7 +306,8 @@ def _configure_newton_mimic_compliance(
     eq_solref = np.asarray(mjw_model.eq_solref.numpy()).copy()
     mimic_solref = _default_mujoco_mimic_solref(
         float(backend.cfg.dt),
-        int(backend.cfg.num_substeps),
+        int(backend.cfg.num_substeps)
+        * int(getattr(backend.cfg.solver_cfg, "rigid_solver_substeps", 1)),
     )
     eq_solref[selected] = mimic_solref
     mjw_model.eq_solref.assign(eq_solref)
diff --git a/embodichain/lab/sim/physics/newton.py b/embodichain/lab/sim/physics/newton.py
index 7cf5bcd51..28e17ad5e 100644
--- a/embodichain/lab/sim/physics/newton.py
+++ b/embodichain/lab/sim/physics/newton.py
@@ -159,16 +159,19 @@ def invalidate_fluid_state(self, env_ids: Sequence[int] | None = None) -> None:
         if backend is None or backend.solver_type != "sph":
             raise RuntimeError("Prepared SPH backend required.")
         backend.invalidate_cuda_graph("fluid state write")
-        if getattr(backend.solver, "supports_world_reset", False):
+        solver = getattr(backend, "sph_solver", backend.solver)
+        if getattr(solver, "supports_world_reset", False):
             mask = [
                 env_ids is None or i in env_ids
                 for i in range(backend.model.world_count)
             ] + [False]
-            backend.clear_solver_state(
-                wp.array(mask, dtype=wp.bool, device=backend.model.device)
+            solver.reset(
+                backend._runtime.current_state,
+                world_mask=wp.array(mask, dtype=wp.bool, device=backend.model.device),
+                flags=0,
             )
             return
-        boundary = backend.solver.sdf_boundary
+        boundary = solver.sdf_boundary
         if boundary is not None and boundary._body_q_prev is not None:
             boundary._body_q_prev.assign(backend.state_0.body_q)
 
@@ -178,7 +181,7 @@ def fluid_diagnostics(self, fluid: FluidObject) -> dict[str, object]:
         import torch
 
         backend = get_newton_backend(self._manager._world)
-        solver = backend.solver
+        solver = getattr(backend, "sph_solver", backend.solver)
         density = wp.to_torch(solver.particle_density)
         values = []
         for entity in fluid._entities:
diff --git a/embodichain/lab/sim/spawn/descriptors.py b/embodichain/lab/sim/spawn/descriptors.py
index c21c2e575..4a1e3705e 100644
--- a/embodichain/lab/sim/spawn/descriptors.py
+++ b/embodichain/lab/sim/spawn/descriptors.py
@@ -335,6 +335,39 @@ def rigid_desc_from_cfg(
         0 if cfg.visual_shape is None and cfg.render_enabled else None
     )
 
+    collisions = [collision]
+    if cfg.particle_collision_shape is not None:
+        boundary_shape = cfg.particle_collision_shape
+        if newton_solver_type is None:
+            raise ValueError("particle_collision_shape requires Newton physics.")
+        if (
+            not isinstance(boundary_shape, MeshCfg)
+            or boundary_shape.collision is None
+            or boundary_shape.collision.approximation != "sdf"
+        ):
+            raise ValueError(
+                "particle_collision_shape requires a MeshCfg with sdf collision."
+            )
+        boundary_cfg = cfg.copy()
+        boundary_cfg.shape = boundary_shape
+        boundary_geometry = _compile_geometry(boundary_cfg)[0]
+        boundary = CollisionDesc.from_geometry(
+            boundary_geometry, approximation=CollisionApproximation.SDF
+        )
+        boundary.enable_collision = False
+        boundary.newton = replace(
+            _compile_newton_collision(
+                physics,
+                newton_solver_type=newton_solver_type,
+                author_shape_defaults=True,
+                mesh_collision=boundary_shape.collision,
+            ),
+            has_particle_collision=True,
+            contributes_to_mass=False,
+        )
+        collision.newton = replace(collision.newton, has_particle_collision=False)
+        collisions.append(boundary)
+
     descriptor = ObjectDesc(
         name=uid,
         pose=_pose_from_cfg(cfg),
@@ -349,7 +382,7 @@ def rigid_desc_from_cfg(
             if cfg.render_enabled
             else []
         ),
-        collisions=[collision],
+        collisions=collisions,
         physics=_compile_rigid_physics(physics, cfg.body_type),
         per_env=per_env,
         body_scale=_vector3(cfg.body_scale, field_name="body_scale"),
diff --git a/embodichain_tasks/configs/components/embodiments/franka_panda_fluid_replay.yaml b/embodichain_tasks/configs/components/embodiments/franka_panda_fluid_replay.yaml
deleted file mode 100644
index cd05135d5..000000000
--- a/embodichain_tasks/configs/components/embodiments/franka_panda_fluid_replay.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-embodiment_id: franka_panda_fluid_replay
-simulation:
-  class_type: FrankaPanda
-  robot_type: panda
-  uid: FrankaPanda
-  attrs:
-    collision_props:
-      backend: newton
-      has_particle_collision: false
-sensor:
-- sensor_type: Camera
-  uid: overview
-  width: 640
-  height: 480
-  intrinsics:
-  - 550.0
-  - 550.0
-  - 320.0
-  - 240.0
-  extrinsics:
-    eye:
-    - -1.4
-    - -1.4
-    - 1.2
-    target:
-    - -0.43
-    - -0.03
-    - 0.47
-    up:
-    - 0
-    - 0
-    - 1
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
index c17513d6c..1b666e694 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
@@ -1,141 +1,93 @@
-# Franka 单臂马克杯倒水
+# Franka 在线倒水
 
-使用现有 `Pick → simulation.pour → Place` Task Program 生成并验证空杯轨迹，
-再在独立的 Newton SPH 环境中回放机械臂关节和杯子实测状态，观察接水效果。
-流体接入使用 `SimulationManager` 的公共接口，求解器参数直接复用 DexSim `SPHSolverCfg`。
+Sim demo 和带水 Gym 部署均在同一个 World 中在线推进：MuJoCo-Warp 求解机械臂驱动、
+重力和杯把抓持接触，DFSPH 读取当前刚体位姿和速度。当前是单向耦合，水没有反作用力。
+初始填充由参数采样，不需要预录关节/杯子轨迹或粒子文件。
 
-## 运行要求与资产
+## 运行
 
-需要支持原生 SPH、volume map、液面重建和 OptiX 的 DexSim / Newton CUDA 运行时。
-多环境还要求 SPH 的 world 隔离和选择性重置能力；旧运行时保留单环境门禁。
-当前批量实现依赖尚未发布的 DexSim 扩展，发布 wheel / AOT 验收状态见 PR。
-
-运行时网格和可选演示资料通过标准资产缓存下载：
+Gym 使用普通 `EmbodiedEnv`，通过 Task Program 的 `Pick → MoveHeldObject → Pour → Place`
+生成动作目标，和其它任务一样由 `env.step(action)` 实时执行：
 
 ```bash
-embodichain data download --name PourCupAssets
-# 仅复现原始 Sim 演示时需要：
-embodichain data download --name PourCupDemoData
+python -m embodichain.lab.scripts.run_fluid_task_program \
+  --num-envs 2 --renderer fast-rt --output outputs/fluid-task-program-online
 ```
 
-| 资产包 | 内容 |
-| --- | --- |
-| `PourCupAssets` | 杯子显示网格、杯壁、接水碗、台面和地面碰撞网格 |
-| `PourCupDemoData` | 原始已验证轨迹、参考视频、最终粒子快照和验证报告 |
-
-未预下载时，`get_data_path("PourCupAssets/cup.glb")` 等路径会自动下载并缓存。
-`EMBODICHAIN_DATA_ROOT` 可覆盖缓存根目录；数据包托管于
-[Hugging Face task_assets](https://huggingface.co/datasets/DexForceAI/embodichain_data/tree/main/task_assets)。
-代码仓库和 wheel 仅携带下载器与配置，默认初始水体直接在内存生成。
+也可使用普通任务入口：
 
-## Task Program → 流体 Gym
+```bash
+embodichain run-env \
+  --gym-config embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml \
+  --headless --renderer fast-rt
+```
 
-从源码仓库根目录运行：
+独立 Sim demo 直接创建 `SimulationManager`，从当前场景规划 Atomic Action 关节目标，
+每帧设置驱动目标并推进真实动力学：
 
 ```bash
-python -m embodichain.lab.scripts.run_fluid_task_program --num-envs 2 --renderer fast-rt
+python scripts/tutorials/sim/franka_dfsph_pour_demo.py --headless --renderer fast-rt
 ```
 
-脚本先在 MuJoCo 中执行并验收抬杯、倾倒、放回，再启动独立 SPH Gym。
-默认输出到 `outputs/fluid-task-program/`，包含源轨迹、源验收报告、接水量统计，
-以及 `videos/episode_0_fluid_gym_env0.mp4` 等每环境独立视频。
-录制通过 `record_camera` 事件调用 `record_camera_data`，使用 `per_env: true`、
-`fps: null`，按实际控制频率保存（本配置为 24 fps）。
+Gym 的 `--particle-spacing 0.003 --renderer no-render` 可用于较快的物理检查；
+`--seconds 2` 只验证起始片段，不能作为完整倒水验收。Sim 对应参数是 `--spacing`。
+两种入口默认均为 1.5 mm；粗分辨率会改变水量和边界精度。
 
-复用本工具生成的合格轨迹，或进行粗粒度局部重置检查：
+`env.fluid.yaml` 拥有求解器、场景、初始水体、观测和录制；`task.franka.fluid.yaml`
+选择 Franka 真实接触配置、Task Program、语义绑定和执行策略。
+带水程序先验证实际抬杯，再将杯子移动到相对接水碗定义的倾倒位置，最后倾倒并放回。
+`env.newton.yaml` 与 `env.yaml` 仍提供独立的空杯任务。
 
-```bash
-python -m embodichain.lab.scripts.run_fluid_task_program \
-  --trajectory outputs/fluid-task-program/task_program_trajectory.pt --num-envs 2
-
-python -m embodichain.lab.scripts.run_fluid_task_program \
-  --trajectory outputs/fluid-task-program/task_program_trajectory.pt \
-  --num-envs 2 --renderer no-render --seconds 0.125 \
-  --particle-spacing 0.006 --partial-reset-step 1
-```
+## 求解与碰撞几何
 
-流体阶段默认使用 FastRT。当前液面几何只发布到 OptiX 场景，Hybrid 的 Vulkan
-主表面无法显示，因此流体对象会拒绝 Hybrid；空杯轨迹生成仍可使用 Hybrid。
-`no-render` 仅用于物理检查。粗分辨率短测不能替代完整倒水效果验证。
+直接复用 DexSim 的 `SPHSolverCfg`，通过 `rigid_solver_cfg` 选择现有
+`MJWarpSolverCfg`，没有另建 EmbodiChain SPH 配置类。外层物理步为 1/240 s，
+含 6 个 SPH 子步；每个 SPH 子步含 7 个 MuJoCo 微步，控制周期为 0.025 s。
+两种求解器共享模型、状态和仿真时钟，微步不会重复增加时间。
 
-`FluidReplayEnv` 提供标准 `reset` / `step`、按环境独立时钟、流体选择性重置、
-summary 和区域体积观测。它将单行合格轨迹广播到多环境，要求固定机器人根位姿
-和单个 Newton 子步。回放由源状态驱动，不接受 action manager 或策略 action 数据录制。
-耦合为单向：杯壁影响水，水不反作用于机械臂，SPH 阶段不重新求解抓持接触。
-Task Program 验收和流体接水结果分别报告。
+杯子是一个动态刚体，质量 0.30 kg：`shape` 的凸分解网格提供杯身/杯把的刚体接触，
+`particle_collision_shape` 的杯内 SDF 提供流体边界。后者挂在同一个刚体上，
+不增加质量、不参与刚体接触，也不通过位姿跟随器或固定关节伪造抓持。
+机器人使用已有 Franka firm-grip 配置，保留关节驱动和指腹的抗扭/滚动摩擦。
 
-## 初始水体配置
+当前要求兼容的 DexSim Newton CUDA 实现，包含 SPH 与 MuJoCo 的在线组合、
+world 隔离及选择性重置支持。实测运行时使用隔离 JIT 构建；发布 wheel/AOT 尚需单独验收。
+场景准备后粒子数量和物理拓扑固定。
 
-`env.fluid.yaml` 声明 SPH 参数、刚体边界、流体和观测。
-初始水体由杯子局部坐标系下的腔体截面描述，单位为米：
+## 初始填充
 
 ```yaml
 spawn:
   kind: revolved_volume
   reference_uid: cup
-  profile: [[-0.044, 0.031], [0.05, 0.0385]]  # [局部 Z, 腔体内半径]
+  profile: [[-0.044, 0.031], [0.05, 0.0385]]
   fill_level: 0.91
   clearance: 0.003
 ```
 
-截面绕局部 Z 轴旋转并封闭两端。`fill_level` 是完整腔体高度的比例，
-液面裁剪与壁面间隙分别处理；它不表示稳定后的体积百分比。
-默认 1.5 mm 规则网格采样得到 76,607 个粒子，名义体积约 258.55 mL。
-局部点集只生成一次并保存在内存；reset 根据杯子位姿和 arena 偏移恢复选中环境，
-回放起始的静置阶段用于液体稳定化。
-
-修改配置后重新构建会重新采样。`--particle-spacing` 同时按间距比例缩放显示半径、
-液面网格间距和重建核半径。外部点集 / NPZ 导入仍受支持，但其采样分辨率固定，
-不接受脚本的间距覆盖。
-
-## 独立 Sim 演示
-
-```bash
-python scripts/tutorials/sim/franka_dfsph_pour_demo.py --headless
-```
-
-默认从 `PourCupDemoData/trajectory.pt` 获取原始已验证轨迹，使用 OfflineRT 渲染；
-运行时杯子网格来自 `PourCupAssets/cup.glb`，接水碗由脚本生成。
-去掉 `--headless` 可打开原生窗口；`--renderer fast-rt` 可切换到 FastRT。
-`--trajectory` 可指定其他轨迹，`--seconds 2` 可限制运行时长。
-默认结果保存到 `outputs/franka_pour_cup/dfsph_stable_bowl/`，包括视频、关键帧、
-粒子状态和 `validation.json`。原始演示的环境版本与统计见下载包中的报告。
+截面绕杯子局部 Z 轴旋转。`fill_level` 是腔体高度比例；壁面间隙不再裁剪液面。
+1.5 mm 网格生成 76,607 粒，名义体积约 258.55 mL。点集只在声明时采样一次并保存在内存，
+reset 恢复选中环境的初始状态，不额外推进其它行的物理时间。机械臂接近期间
+容器和液体自然稳定。需要在移动后的容器中重新填充时使用
+`reset_fluid_in_container` 事件。NPZ 仍可用于外部点集导入，默认路径不读取或生成 NPZ。
 
-## 空杯 Task Program 部署
+## 录制与验收
 
-以下两个部署只生成空杯任务，不包含 SPH 或接水碗。
-Newton + MuJoCo-Warp：
+当前原生液面位于 OptiX 场景，使用 FastRT；Hybrid 的 Vulkan 主表面无法显示液体，
+因此流体门禁会拒绝 Hybrid，包括复用已初始化 Hybrid 的进程。纯物理检查使用 NoRender。
 
-```bash
-embodichain run-env \
-  --gym-config embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml \
-  --headless --renderer hybrid --record_trajectory \
-  --trajectory_save_dir outputs/franka_pour_cup/newton_smooth_mug_trajectories
-```
+Gym 的 `record_camera` 事件使用现有 `record_camera_data`，配置 `per_env: true`、
+`fps: null`，每个环境以控制频率 40 fps、960 × 720 分辨率录制杯口与接水碗的独立视角，输出
+`videos/episode_0_fluid_gym_env0.mp4` 等文件。Sim 使用相同视角的 record camera，
+输出 `fluid_sim_env0.mp4` 等文件。离屏读取前显式发布最新渲染状态。
 
-Default：
+Gym runner 在结束时保存最后一段视频，并写入 `fluid_gym_result.json`，分别记录
+Task Program 结果、实测抬升/倾角/放回误差、接水量及墙钟耗时。
+执行策略的 `projected` 完成状态不能单独证明物理成功；完整验收还要求实际杯子
+抬升至少 5 cm、倾角至少 45°、放回误差不超过 3 cm，且碗内最终接水量至少 50 mL。
+`--record-trajectory` 仅用于可选的实测状态诊断，不作为下一阶段输入。
+Sim 报告为 `validation.json`。旧回放证据归档在 `PourCupDemoData`，不能用来证明在线效果。
 
-```bash
-embodichain run-env \
-  --gym-config embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml \
-  --headless --renderer hybrid --record_trajectory \
-  --trajectory_save_dir outputs/franka_pour_cup/mug_trajectories
-```
-
-| 配置 | 职责 |
-| --- | --- |
-| `task.franka*.yaml` | 运行入口及环境、机器人、程序和执行策略选择 |
-| `env.yaml` / `env.newton.yaml` | 空杯场景、物理后端、接触参数、录制和复位 |
-| `env.fluid.yaml` | SPH 场景、初始水体、流体观测和每环境相机录制 |
-| `task_program/program*.yaml` | Pick → Pour → Place、稳定等待和终点验证 |
-| `task_program/integration*.yaml` | 语义绑定、侧抓位姿、抬升距离和倾倒指令 |
-| `components/embodiments/franka_panda_*.yaml` | 机器人、传感器、接触参数和技能声明 |
-| `components/execution_policies/trajectory_open_loop_*.yaml` | 轨迹插值和动作后稳定策略 |
-
-组件路径以 `embodichain_tasks/configs/` 为根。静态检查空杯部署：
-
-```bash
-python .agents/skills/add-task-program/scripts/inspect_deployment.py \
-  embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml \
-  embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml
-```
+任务网格由标准资产缓存中的 `PourCupAssets` 提供，托管于
+[Hugging Face task_assets](https://huggingface.co/datasets/DexForceAI/embodichain_data/tree/main/task_assets)。
+本 PR 不重新提交网格、预录轨迹、粒子快照或视频文件。
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
index 521d6ed7d..fcda972f1 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
@@ -7,8 +7,8 @@ arena_space: 2.5
 max_episode_steps: 2000
 max_episodes: 1
 physics_config:
-  physics_dt: 0.0006944444444444445
-  num_substeps: 1
+  physics_dt: 0.004166666666666667
+  num_substeps: 6
   collision_cfg: null
   solver_cfg:
     solver_type: sph
@@ -27,6 +27,18 @@ physics_config:
     volume_map_quadrature_samples: 6
     volume_map_volume_scale: 0.8
     sdf_boundary_enabled: false
+    rigid_solver_cfg:
+      solver_type: mujoco_warp
+      solver: newton
+      integrator: implicitfast
+      iterations: 50
+      ls_iterations: 100
+      cone: elliptic
+      impratio: 1000.0
+      use_mujoco_contacts: true
+      enable_multiccd: true
+      rne_mode: disabled
+    rigid_solver_substeps: 7
 simulation:
   light:
     direct:
@@ -60,20 +72,41 @@ simulation:
       rect_height: 0.7
   background:
   - uid: cup_platform
-    body_type: kinematic
+    body_type: static
     shape:
+      shape_type: Cube
+      size:
+      - 0.4
+      - 0.5
+      - 0.5
+    init_pos:
+    - -0.9
+    - 0.0
+    - 0.25
+    attrs:
+      collision_props:
+        backend: newton
+        condim: 6
+      material_props:
+        backend: newton
+        dynamic_friction: 1.0
+        ke: 400000.0
+        kd: 1265.0
+        torsional_friction: 0.1
+        rolling_friction: 0.03
+    particle_collision_shape:
       shape_type: Mesh
       fpath: PourCupAssets/fluid_table.obj
       collision:
         approximation: sdf
-        force_sdf: false
+        force_sdf: true
         sdf_resolution: 192
         sdf_narrow_band_range:
         - -0.025
         - 0.025
         sdf_padding: 0.012
         sdf_texture_format: uint16
-      visual_material:
+      visual_material: &id001
         uid: fluid_table
         base_color:
         - 0.23
@@ -83,19 +116,12 @@ simulation:
         roughness: 0.6
         metallic: 0.5
         ior: 1.0
-    init_pos:
-    - -0.91
-    - -0.08
-    - 0.25
-    attrs:
-      collision_props:
-        backend: newton
-        has_particle_collision: true
-      material_props:
-        backend: newton
-        dynamic_friction: 0.2
+    visual_shape:
+      shape_type: Mesh
+      fpath: PourCupAssets/fluid_table.obj
+      visual_material: *id001
   - uid: fluid_floor
-    body_type: kinematic
+    body_type: static
     shape:
       shape_type: Mesh
       fpath: PourCupAssets/fluid_floor.obj
@@ -120,36 +146,8 @@ simulation:
         backend: newton
         dynamic_friction: 0.2
     render_enabled: false
-  - uid: cup
-    body_type: kinematic
-    shape:
-      shape_type: Mesh
-      fpath: PourCupAssets/mug_wall.obj
-      collision:
-        approximation: sdf
-        force_sdf: false
-        sdf_resolution: 192
-        sdf_narrow_band_range:
-        - -0.025
-        - 0.025
-        sdf_padding: 0.012
-        sdf_texture_format: uint16
-    init_pos:
-    - -0.75
-    - 0
-    - 0.552
-    attrs:
-      collision_props:
-        backend: newton
-        has_particle_collision: true
-      material_props:
-        backend: newton
-        dynamic_friction: 0.2
-    visual_shape:
-      shape_type: Mesh
-      fpath: PourCupAssets/cup.glb
   - uid: receiver
-    body_type: kinematic
+    body_type: static
     shape:
       shape_type: Mesh
       fpath: PourCupAssets/receiver_wall.obj
@@ -174,7 +172,7 @@ simulation:
         ior: 1.0
     init_pos:
     - -0.755
-    - -0.137
+    - -0.15
     - 0.5
     attrs:
       collision_props:
@@ -191,8 +189,10 @@ simulation:
     spawn:
       kind: revolved_volume
       profile:
-      - [-0.044, 0.031]
-      - [0.05, 0.0385]
+      - - -0.044
+        - 0.031
+      - - 0.05
+        - 0.0385
       fill_level: 0.91
       clearance: 0.003
       reference_uid: cup
@@ -228,8 +228,54 @@ simulation:
         laplacian_iters: 4
         laplacian_alpha: 0.11
         update_period: 1
+  rigid_object:
+  - uid: cup
+    shape:
+      shape_type: Mesh
+      fpath: PourCupAssets/cup.glb
+      compute_uv: true
+      collision:
+        approximation: convex_decomposition
+        max_hulls: 32
+    body_type: dynamic
+    init_pos:
+    - -0.75
+    - 0.0
+    - 0.552
+    body_scale:
+    - 1.0
+    - 1.0
+    - 1.0
+    attrs:
+      mass_props:
+        mass: 0.3
+      collision_props:
+        backend: newton
+        contact_offset: 0.0005
+        rest_offset: 0.0001
+        condim: 6
+        has_particle_collision: true
+      material_props:
+        backend: newton
+        dynamic_friction: 1.2
+        ke: 400000.0
+        kd: 1265.0
+        torsional_friction: 0.1
+        rolling_friction: 0.03
+    particle_collision_shape:
+      shape_type: Mesh
+      fpath: PourCupAssets/mug_wall.obj
+      collision:
+        approximation: sdf
+        force_sdf: true
+        sdf_resolution: 192
+        sdf_narrow_band_range:
+        - -0.025
+        - 0.025
+        sdf_padding: 0.012
+        sdf_texture_format: uint16
 env:
-  sim_steps_per_control: 60
+  sim_steps_per_control: 6
   events:
     record_camera:
       func: record_camera_data
@@ -238,15 +284,37 @@ env:
       is_global: true
       params:
         name: fluid_gym
-        resolution: [640, 480]
-        intrinsics: [550.0, 550.0, 320.0, 240.0]
-        eye: [-1.15, -0.85, 0.95]
-        target: [-0.72, -0.12, 0.63]
-        up: [0.0, 0.0, 1.0]
+        resolution:
+        - 960
+        - 720
+        intrinsics:
+        - 1000.0
+        - 1000.0
+        - 480.0
+        - 360.0
+        eye:
+        - -1.12
+        - -0.73
+        - 0.96
+        target:
+        - -0.76
+        - -0.07
+        - 0.61
+        up:
+        - 0.0
+        - 0.0
+        - 1.0
         max_env_num: 16
         per_env: true
         fps: null
         save_path: outputs/fluid-task-program/videos
+    refill_water:
+      func: reset_fluid_in_container
+      mode: reset
+      params:
+        entity_cfgs:
+        - uid: water
+        - uid: cup
   control_parts:
   - arm
   - eef
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml
index 57635c5dd..2c159d783 100644
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml
@@ -1,12 +1,26 @@
-id: PourCupFluidReplay-Franka-v1
+id: PourCup-Franka-Fluid-v1
 seed: 0
 enable_sensor: false
 environment:
   component: env.fluid.yaml
+task_program:
+  program: task_program/program.fluid.yaml
+  integration: task_program/integration.fluid.yaml
+  execution_policy: ../../../../components/execution_policies/trajectory_open_loop_gentle.yaml
 embodiment:
-  component: ../../../../components/embodiments/franka_panda_fluid_replay.yaml
+  component: ../../../../components/embodiments/franka_panda_firm_grip_newton.yaml
   overrides:
     init_rot:
-    - 0
-    - 0
-    - 180
+    - 0.0
+    - 0.0
+    - 180.0
+    init_qpos:
+    - 0.0023885604459792376
+    - -0.09648692607879639
+    - -0.0022385704796761274
+    - -2.388152599334717
+    - 0.00024301925441250205
+    - 3.862462282180786
+    - 0.7850000262260437
+    - 0.04
+    - 0.04
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.fluid.yaml
new file mode 100644
index 000000000..9b247f142
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.fluid.yaml
@@ -0,0 +1,97 @@
+integration_id: pour_cup_fluid_franka_v1
+program_id: pick_aim_pour_return_cup
+requires:
+  scene_contract: pour_cup_fluid_scene_v1
+  embodiment_contract: single_arm_parallel_gripper
+scene_binding:
+  contract_id: pour_cup_fluid_scene_v1
+  registry_id: task_program_pour_cup_fluid
+  rigid_objects:
+  - entity_id: cup
+    simulation_uid: cup
+    dynamics: dynamic
+    semantic_type: cup
+    affordances:
+    - entity_id: cup_grasp
+      kind: antipodal_grasp
+      internal_axis:
+      - 1.0
+      - 0.0
+      - 0.0
+  - entity_id: receiver
+    simulation_uid: receiver
+    dynamics: static
+    semantic_type: bowl
+profile:
+  defaults:
+    pick_up:
+      primary: primary_manipulator
+    pour:
+      primary: primary_manipulator
+    place:
+      primary: primary_manipulator
+    move_held_object:
+      primary: primary_manipulator
+  action_options:
+    pick:
+      kind: pick_up
+      hand_interp_steps: 20
+      grasp_settle_steps: 20
+      lift_height: 0.12
+      pre_grasp_distance: 0.08
+      approach_direction:
+      - -1.0
+      - 0.0
+      - 0.0
+      fixed_object_to_eef:
+      - 0.0
+      - 0.0
+      - -1.0
+      - 0.065
+      - 0.0
+      - 1.0
+      - 0.0
+      - 0.0
+      - 1.0
+      - 0.0
+      - 0.0
+      - 0.0
+      - 0.0
+      - 0.0
+      - 0.0
+      - 1.0
+    simulation.pour:
+      kind: pour
+      rotate_angle: 1.3962634015954636
+    place:
+      kind: place
+      hand_interp_steps: 20
+      release_settle_steps: 20
+      preserve_current_object_orientation: false
+    simulation.move_held_object:
+      kind: move_held_object
+  effect_monitors: {}
+runtime_services:
+  registered_semantic_lowerers:
+  - kind: move_held_object
+    target_id: receiver_pour_pose
+    reference_entity_id: receiver
+    relative_pose:
+    - 1.0
+    - 0.0
+    - 0.0
+    - 0.005
+    - 0.0
+    - 1.0
+    - 0.0
+    - 0.09
+    - 0.0
+    - 0.0
+    - 1.0
+    - 0.17
+    - 0.0
+    - 0.0
+    - 0.0
+    - 1.0
+  - kind: pour
+    object_id: cup
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.fluid.yaml
new file mode 100644
index 000000000..2aa890ee6
--- /dev/null
+++ b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.fluid.yaml
@@ -0,0 +1,96 @@
+program_id: pick_aim_pour_return_cup
+targets:
+  cup_return_pose:
+    kind: cyclic_pose
+    values:
+    - position:
+      - -0.75
+      - 0.0
+      - 0.552
+      quaternion_xyzw:
+      - 0.0
+      - 0.0
+      - 0.0
+      - 1.0
+  cup_lift_pose:
+    kind: cyclic_pose
+    values:
+    - position:
+      - -0.75
+      - 0.0
+      - 0.672
+      quaternion_xyzw:
+      - 0.0
+      - 0.0
+      - 0.0
+      - 1.0
+  cup_pour_pose:
+    kind: cyclic_pose
+    values:
+    - position:
+      - -0.75
+      - -0.06
+      - 0.67
+      quaternion_xyzw:
+      - 0.0
+      - 0.0
+      - 0.0
+      - 1.0
+program:
+  kind: sequence
+  items:
+  - kind: segment
+    name: pick_cup
+    steps:
+      kind: invoke
+      call:
+        kind: pick
+        object: cup
+        grasp: cup_grasp
+    validators:
+    - kind: object_near_target
+      object: cup
+      target: cup_lift_pose
+      position_tolerance: 0.03
+  - kind: segment
+    name: aim_cup_above_bowl
+    steps:
+      kind: invoke
+      call:
+        kind: registered
+        call_id: simulation.move_held_object
+        arguments:
+          target: receiver_pour_pose
+    validators:
+    - kind: object_near_target
+      object: cup
+      target: cup_pour_pose
+      position_tolerance: 0.03
+  - kind: segment
+    name: pour_water
+    steps:
+      kind: invoke
+      call:
+        kind: registered
+        call_id: simulation.pour
+        arguments:
+          object: cup
+  - kind: segment
+    name: return_cup
+    steps:
+      kind: invoke
+      call:
+        kind: place
+        object: cup
+        at:
+          kind: target_ref
+          target: cup_return_pose
+    post:
+    - kind: wait_stable
+      entity: cup
+    validators:
+    - kind: object_near_target
+      object: cup
+      target: cup_return_pose
+      position_tolerance: 0.03
+instruction: Pour water from the cup into the bowl and return the cup to the table.
diff --git a/embodichain_tasks/embodichain_tasks/manipulation/tableware/pour_cup.py b/embodichain_tasks/embodichain_tasks/manipulation/tableware/pour_cup.py
deleted file mode 100644
index e3d81473a..000000000
--- a/embodichain_tasks/embodichain_tasks/manipulation/tableware/pour_cup.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-
-"""Fluid replay deployment for Task Program-generated pouring trajectories."""
-
-from __future__ import annotations
-from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
-from embodichain.lab.gym.utils.registration import register_env
-
-__all__ = ["PourCupFluidReplayEnv"]
-
-
-@register_env("PourCupFluidReplay-Franka-v1", max_episode_steps=2000)
-class PourCupFluidReplayEnv(FluidReplayEnv):
-    """Evaluate a qualified Franka pouring trajectory with live SPH fluids."""
diff --git a/scripts/tutorials/sim/franka_dfsph_pour_demo.py b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
index b8962e631..fccc5733d 100644
--- a/scripts/tutorials/sim/franka_dfsph_pour_demo.py
+++ b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
@@ -13,587 +13,296 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ----------------------------------------------------------------------------
-"""EmbodiChain pouring demo using the public fluid and rigid-boundary APIs.
+"""Drive a live Franka grasp/pour/place in SimulationManager with one-way SPH.
 
-The robot and mug replay the recorded MuJoCo grasp trajectory. Water is simulated
-by DFSPH with volume-map boundaries, without reaction forces on the robot.
-Volume maps use baked mesh SDFs; the additional SDF penalty force is disabled.
-The manager owns fluid declaration, state binding and render publication.
+Atomic Actions plan controller targets from the current scene. MuJoCo solves
+joint drives, gravity and grasp contact; DFSPH reads the live cup boundary.
+The Gym counterpart uses a configured Task Program for the same physical scene.
 """
 
 from __future__ import annotations
 
 import argparse
 import json
-import time
 from pathlib import Path
+import time
 
-import cv2
 import imageio.v2 as imageio
 import numpy as np
 import torch
-import trimesh
-from scipy.interpolate import PchipInterpolator
-from scipy.spatial.transform import Rotation, Slerp
-
-from dexsim.engine.newton_physics import SPHSolverCfg
 
-from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
-from embodichain.lab.sim.cfg import (
-    LightCfg,
-    NewtonPhysicsCfg,
-    RenderCfg,
-    RigidObjectCfg,
-    FluidObjectCfg,
-    FluidSpawnCfg,
-    FluidRenderCfg,
+from embodichain.lab.scripts.run_fluid_task_program import (
+    _configure,
+    _cup_qualification,
 )
-from embodichain.data import get_data_path
-from embodichain.lab.sim.material import VisualMaterialCfg
-from embodichain.lab.visualization import VisualizationCfg
-from embodichain_robots import FrankaPandaCfg
+from embodichain.lab.sim import SimulationManager
+from embodichain.lab.sim.atomic_actions import (
+    AxisAlignAffordance,
+    ControlPartCommandProfile,
+    GraspGoal,
+    HeldObjectPoseGoal,
+    MoveHeldObjectOptions,
+    MotionPolicy,
+    ObjectSemantics,
+    PickUpOptions,
+    PlaceGoal,
+    PlaceOptions,
+    PourGoal,
+    PourOptions,
+    create_simulation_atomic_action_engine,
+)
+from embodichain.lab.sim.motion.motion_generator import MotionGenerator, MotionGenCfg
+from embodichain.lab.sim.motion.planners import ToppraPlannerCfg
 from embodichain.lab.sim.sensors import CameraCfg
 
 __all__ = ["main"]
 
 ROOT = Path(__file__).resolve().parents[3]
-DEFAULT_TRAJECTORY = "PourCupDemoData/trajectory.pt"
-CUP_ASSET = "PourCupAssets/cup.glb"
-RECEIVER_CENTER = np.array([-0.755, -0.137, 0.50])
-BOWL_OUTER_RADIUS = 0.097
-BOWL_INNER_RADIUS = 0.0915
-BOWL_SPHERE_Z = 0.088
-BOWL_HEIGHT = 0.066
-BOWL_BOTTOM_Z = 0.006
-
-
-def _pose(position: np.ndarray, rotation: np.ndarray | None = None) -> np.ndarray:
-    pose = np.eye(4, dtype=np.float32)
-    pose[:3, 3] = position
-    if rotation is not None:
-        pose[:3, :3] = rotation
-    return pose
-
-
-def _add_boundary(
-    sim: SimulationManager,
-    name: str,
-    mesh: trimesh.Trimesh,
-    pose: np.ndarray,
-    *,
-    resolution: int = 192,
-    material: VisualMaterialCfg | None = None,
-    visual_asset: Path | None = None,
-    render_enabled: bool = True,
-):
-    if material is not None:
-        material = material.copy()
-        material.uid = f"{name}_material"
-    return sim.add_rigid_object(
-        RigidObjectCfg.from_dict(
-            {
-                "uid": name,
-                "body_type": "kinematic",
-                "init_local_pose": pose,
-                "render_enabled": render_enabled,
-                "shape": {
-                    "shape_type": "Mesh",
-                    "vertices": np.asarray(mesh.vertices, dtype=np.float32),
-                    "triangles": np.asarray(mesh.faces, dtype=np.int32),
-                    "visual_material": None if material is None else material.to_dict(),
-                    "collision": {
-                        "approximation": "sdf",
-                        "force_sdf": False,
-                        "sdf_resolution": resolution,
-                        "sdf_narrow_band_range": [-0.025, 0.025],
-                        "sdf_padding": 0.012,
-                        "sdf_texture_format": "uint16",
-                    },
-                },
-                "visual_shape": (
-                    None
-                    if visual_asset is None
-                    else {"shape_type": "Mesh", "fpath": str(visual_asset)}
-                ),
-                "attrs": {
-                    "collision_props": {
-                        "backend": "newton",
-                        "has_particle_collision": True,
-                    },
-                    "material_props": {"backend": "newton", "dynamic_friction": 0.2},
-                },
-            }
-        )
-    )
-
-
-def _particle_summary(
-    positions: np.ndarray,
-    cup_pose: np.ndarray,
-    spacing: float,
-    velocities: np.ndarray,
-) -> dict[str, float | int]:
-    local = (positions - cup_pose[:3, 3]) @ cup_pose[:3, :3]
-    radius = 0.031 + (local[:, 2] + 0.044) * (0.0075 / 0.094)
-    in_mug = (
-        (np.linalg.norm(local[:, :2], axis=1) < radius + 0.003)
-        & (local[:, 2] > -0.049)
-        & (local[:, 2] < 0.053)
-    )
-    receiver = positions - RECEIVER_CENTER
-    bowl_radius = np.sqrt(
-        np.maximum(0.0, BOWL_INNER_RADIUS**2 - (receiver[:, 2] - BOWL_SPHERE_Z) ** 2)
-    )
-    in_receiver = (
-        (np.linalg.norm(receiver[:, :2], axis=1) < bowl_radius + spacing)
-        & (receiver[:, 2] > BOWL_BOTTOM_Z - spacing)
-        & (receiver[:, 2] < BOWL_HEIGHT + spacing)
-    )
-    summary = {
-        "in_mug": int(in_mug.sum()),
-        "in_receiver": int(in_receiver.sum()),
-        "below_table": int((positions[:, 2] < 0.49).sum()),
-        "outside_containers": int((~(in_mug | in_receiver)).sum()),
-        "received_ml": float(in_receiver.sum() * spacing**3 * 1e6),
-        "min_z": float(positions[:, 2].min()),
-    }
-    if in_receiver.any():
-        bowl_speed = np.linalg.norm(velocities[in_receiver], axis=1)
-        summary["bowl_speed_rms_m_s"] = float(np.sqrt(np.mean(bowl_speed**2)))
-        summary["bowl_speed_p95_m_s"] = float(np.quantile(bowl_speed, 0.95))
-        summary["bowl_height_p99_m"] = float(
-            np.quantile(receiver[in_receiver, 2], 0.99)
-        )
-    return summary
+TASK = (
+    ROOT
+    / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
+)
 
 
 def main() -> None:
-    """Replay the arm and simulate water in the same EmbodiChain world."""
+    """Plan fresh joint commands and solve the robot, cup and fluid online."""
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument(
-        "--trajectory",
-        type=Path,
-        help=f"Recorded trajectory path; defaults to cached {DEFAULT_TRAJECTORY}.",
+        "--output", type=Path, default=ROOT / "outputs/fluid-sim-online"
     )
+    parser.add_argument("--spacing", type=float, default=0.0015)
+    parser.add_argument("--seconds", type=float)
+    parser.add_argument("--num-envs", type=int, default=1)
     parser.add_argument(
-        "--output",
-        type=Path,
-        default=ROOT / "outputs/franka_pour_cup/dfsph_stable_bowl",
+        "--renderer", choices=["fast-rt", "no-render"], default="fast-rt"
     )
-    parser.add_argument("--seconds", type=float, default=None)
-    parser.add_argument("--start-time", type=float, default=0.0)
-    parser.add_argument("--speed", type=float, default=2.0)
-    parser.add_argument("--spacing", type=float, default=0.0015)
-    parser.add_argument("--volume-map-resolution", type=int, default=192)
-    parser.add_argument("--viscosity", type=float, default=0.0006)
-    parser.add_argument("--surface-tension", type=float, default=0.002)
-    parser.add_argument("--fps", type=int, default=24)
-    parser.add_argument("--physics-hz", type=int, default=1440)
-    parser.add_argument("--substeps", type=int, default=1)
-    parser.add_argument("--renderer", choices=["rt", "fast-rt"], default="rt")
     parser.add_argument("--headless", action="store_true")
     args = parser.parse_args()
-    if min(args.speed, args.spacing, args.fps, args.substeps, args.physics_hz) <= 0:
-        parser.error("speed, spacing, fps, substeps and physics-hz must be positive")
-    args.output.mkdir(parents=True, exist_ok=True)
-    if args.trajectory is None:
-        args.trajectory = Path(get_data_path(DEFAULT_TRAJECTORY))
-    cup_asset = Path(get_data_path(CUP_ASSET))
-    data = torch.load(args.trajectory, map_location="cpu", weights_only=False)
-    joints = data["states"]["robot", "qpos"][0].numpy()
-    cup_states = data["states"]["rigid_objects", "cup", "pose"][0].numpy()
-    recorded_times = np.arange(len(joints)) * float(data["meta"]["dt"])
-    joint_curve = PchipInterpolator(recorded_times, joints)
-    position_curve = PchipInterpolator(recorded_times, cup_states[:, :3])
-    orientation_curve = Slerp(recorded_times, Rotation.from_quat(cup_states[:, 3:]))
-    if not 0.0 <= args.start_time < recorded_times[-1]:
-        parser.error("start-time must lie inside the recorded trajectory")
-    duration = 1.0 + (recorded_times[-1] - args.start_time) / args.speed + 1.0
-    if args.seconds is not None:
-        duration = min(duration, args.seconds)
-    # Update the moving boundary at every solver step. With coarse pose writes
-    # and many solver substeps, DFSPH estimates an impulsive boundary velocity.
-    dt = 1.0 / args.physics_hz
-    frame_stride = round(1.0 / (dt * args.fps))
-    if not np.isclose(frame_stride * dt * args.fps, 1.0):
-        parser.error("fps must divide physics-hz")
-    spacing = args.spacing
-    grid = np.arange(-0.04, 0.0401, spacing)
-    xx, yy, zz = np.meshgrid(grid, grid, np.arange(-0.036, 0.044, spacing))
-    local_points = np.column_stack([xx.ravel(), yy.ravel(), zz.ravel()])
-    inside_radius = 0.031 + (local_points[:, 2] + 0.044) * (0.0075 / 0.094)
-    local_points = local_points[
-        np.linalg.norm(local_points[:, :2], axis=1) < inside_radius - 0.003
-    ]
-    initial_pose = _pose(
-        position_curve(args.start_time), orientation_curve(args.start_time).as_matrix()
-    )
-    points = (local_points @ initial_pose[:3, :3].T + initial_pose[:3, 3]).astype(
-        np.float32
+    if args.seconds is not None and (
+        not np.isfinite(args.seconds) or args.seconds <= 0
+    ):
+        parser.error("seconds must be finite and positive")
+    output = args.output.resolve()
+    output.mkdir(parents=True, exist_ok=True)
+    _, cfg = _configure(
+        TASK,
+        num_envs=args.num_envs,
+        renderer=args.renderer,
+        output=output,
+        particle_spacing=args.spacing,
     )
-    sim = SimulationManager(
-        SimulationManagerCfg(
-            headless=True,
-            visualization=VisualizationCfg(),
-            width=1280,
-            height=960,
-            num_envs=1,
-            arena_space=2.5,
-            device="cuda",
-            physics_dt=dt,
-            render_cfg=RenderCfg(renderer=args.renderer, spp=8),
-            physics_cfg=NewtonPhysicsCfg(
-                num_substeps=args.substeps,
-                collision_cfg=None,
-                solver_cfg=SPHSolverCfg(
-                    sph_method="dfsph",
-                    particle_diameter=spacing,
-                    particle_support_radius=2.25 * spacing,
-                    rest_density=1000.0,
-                    viscosity=args.viscosity,
-                    surface_tension_coefficient=args.surface_tension,
-                    surface_normal_threshold=0.08,
-                    pressure_relaxation=0.2,
-                    divergence_max_iterations=10,
-                    density_max_iterations=20,
-                    max_neighbors=192,
-                    max_velocity=2.0,
-                    volume_map_boundary_enabled=True,
-                    volume_map_resolution=args.volume_map_resolution,
-                    volume_map_quadrature_samples=6,
-                    volume_map_volume_scale=0.8,
-                    sdf_boundary_enabled=False,
-                ),
-            ),
-        )
-    )
-    writer = None
-    close_writer = None
+    cfg.sim_cfg.num_envs = cfg.num_envs
+    sim = SimulationManager(cfg.sim_cfg)
+    writers = []
+    robot = cup = camera = engine = None
     try:
-        robot_cfg = FrankaPandaCfg.from_dict(
-            {
-                "uid": "FrankaPanda",
-                "init_rot": [0.0, 0.0, 180.0],
-                "init_qpos": joint_curve(args.start_time).tolist(),
-                "attrs": {
-                    "collision_props": {
-                        "backend": "newton",
-                        "has_particle_collision": False,
-                    }
-                },
-            }
-        )
-        robot = sim.add_robot(robot_cfg)
-        # VisualMaterialCfg colors use sRGB; preserve the native demo
-        # linear baseColor [0.88, 0.96, 1.0] after conversion.
-        water_material = VisualMaterialCfg(
-            base_color=[0.9452768, 0.982207, 1.0, 1.0],
-            roughness=0.025,
-            metallic=0.0,
-            ior=1.333,
-            transmission=0.96,
-            volume_thickness=0.04,
-            volume_attenuation_distance=0.6,
-            volume_attenuation_color=(0.80, 0.92, 1.0),
-        )
-        _add_boundary(
-            sim,
-            "pour_table",
-            trimesh.creation.box([0.42, 0.70, 0.50]),
-            _pose(np.array([-0.91, -0.08, 0.25])),
-            resolution=96,
-            material=VisualMaterialCfg(
-                base_color=[0.23, 0.29, 0.34, 1.0], roughness=0.6, metallic=0.5, ior=1.0
-            ),
-        )
-        _add_boundary(
-            sim,
-            "fluid_floor",
-            trimesh.creation.box([2.5, 2.5, 0.10]),
-            _pose(np.array([-0.5, 0.0, -0.055])),
-            resolution=128,
-            render_enabled=False,
-        )
-        mug_mesh = trimesh.creation.revolve(
-            [
-                [0.0, -0.05],
-                [0.035, -0.05],
-                [0.0425, 0.05],
-                [0.0385, 0.05],
-                [0.031, -0.044],
-                [0.0, -0.044],
-            ],
-            sections=128,
-        )
-        mug = _add_boundary(
-            sim, "liquid_mug", mug_mesh, initial_pose, visual_asset=cup_asset
-        )
-        if args.substeps > 1:
-            boundary_times = (
-                np.arange(round(duration / dt) * args.substeps) * dt / args.substeps
-            )
-            source_times = np.clip(
-                args.start_time + (boundary_times - 1.0) * args.speed,
-                args.start_time,
-                recorded_times[-1],
-            )
-            boundary_poses = np.concatenate(
-                (
-                    position_curve(source_times),
-                    orientation_curve(source_times).as_quat(),
-                ),
-                axis=1,
-            )
-            sim.register_kinematic_rigid_trajectory(
-                "liquid_mug", boundary_poses[None], fps=args.physics_hz * args.substeps
-            )
-        outer_z = np.linspace(0.0, BOWL_HEIGHT, 64)
-        inner_z = np.linspace(BOWL_HEIGHT, BOWL_BOTTOM_Z, 64)
-        outer_r = np.sqrt(BOWL_OUTER_RADIUS**2 - (outer_z - BOWL_SPHERE_Z) ** 2)
-        inner_r = np.sqrt(BOWL_INNER_RADIUS**2 - (inner_z - BOWL_SPHERE_Z) ** 2)
-        bowl_profile = np.vstack(
-            [
-                [0.0, 0.0],
-                np.column_stack([outer_r, outer_z]),
-                np.column_stack([inner_r, inner_z]),
-                [0.0, BOWL_BOTTOM_Z],
-            ]
+        robot = sim.add_robot(cfg.robot)
+        for object_cfg in [*cfg.background, *cfg.rigid_object]:
+            sim.add_rigid_object(object_cfg)
+        for fluid_cfg in cfg.fluid_object:
+            sim.add_fluid_object(fluid_cfg)
+        for light_cfg in cfg.light.direct:
+            sim.add_light(light_cfg)
+        sim.prepare()
+        robot.build_pk_serial_chain()
+        sim.reset_objects_state()
+        cup = sim.get_rigid_object("cup")
+        # Live gravity/contact settling before observing the planning scene.
+        sim.update(step=24, render_final_step=False)
+        initial_pose = cup.get_local_pose(to_matrix=True).clone()
+        fill_pose = cup.get_local_pose().clone()
+        fill_pose[:, :3] += sim.arena_offsets
+        sim.get_fluid_object("water").reset_to_pose(fill_pose)
+        object_to_eef = torch.tensor(
+            [[0, 0, -1, 0.065], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1]],
+            dtype=torch.float32,
+            device=sim.device,
         )
-        bowl_mesh = trimesh.creation.revolve(bowl_profile, sections=192)
-        _add_boundary(
-            sim,
-            "receiver",
-            bowl_mesh,
-            _pose(RECEIVER_CENTER),
-            material=VisualMaterialCfg(
-                base_color=[0.82, 0.85, 0.88, 1.0],
-                roughness=0.22,
-                metallic=0.5,
-                ior=1.0,
+        semantics = ObjectSemantics(
+            entity_id="cup",
+            label="cup",
+            geometry={},
+            affordance=AxisAlignAffordance(
+                mesh_vertices=cup.get_vertices(env_ids=[0], scale=True)[0],
+                mesh_triangles=cup.get_triangles(env_ids=[0])[0],
+                internal_axis=torch.tensor([1.0, 0, 0], device=sim.device),
             ),
         )
-        fluid = sim.add_fluid_object(
-            FluidObjectCfg(
-                uid="water",
-                density=1000.0,
-                particle_spacing=spacing,
-                particle_radius=0.30 * spacing,
-                spawn=FluidSpawnCfg(kind="points", positions=points),
-                render=FluidRenderCfg(
-                    mode="mesh_surface",
-                    render_radius=0.30 * spacing,
-                    material=water_material,
-                    clip_below_z=0.49,
-                    mesh_surface={
-                        "gpu_enabled": True,
-                        "cell_size": 0.54 * spacing,
-                        "max_res": 512,
-                        "kernel_radius": 1.01 * spacing,
-                        "iso_density": 0.57,
-                        "density_smooth_iters": 2,
-                        "aabb_trim_fraction": 0.0,
-                        "min_component_tris": 32,
-                        "laplacian_iters": 4,
-                        "laplacian_alpha": 0.11,
-                        "update_period": 1,
-                    },
-                ),
+        motion = MotionGenerator(
+            MotionGenCfg(
+                planner_cfg=ToppraPlannerCfg(
+                    robot_uid=robot.uid, sim_instance_id=sim.instance_id
+                )
             )
         )
-        sim.add_light(
-            LightCfg(
-                uid="key", light_type="sun", intensity=4.0, direction=[-0.3, 0.2, -1.0]
-            )
+        engine = create_simulation_atomic_action_engine(
+            motion,
+            (cup,),
+            control_profiles={
+                "eef": ControlPartCommandProfile.joint_positions(
+                    open=torch.tensor([0.04, 0.04], device=sim.device),
+                    grasp=torch.zeros(2, device=sim.device),
+                )
+            },
         )
-        sim.add_light(
-            LightCfg(
-                uid="fill",
-                light_type="rect",
-                intensity=6.0,
-                init_pos=[-0.6, -0.4, 1.4],
-                direction=[0.0, 0.0, -1.0],
-                rect_width=0.8,
-                rect_height=0.8,
-            )
+        bindings = {"primary": {"motion": "arm", "grasp": "eef"}}
+        policy = MotionPolicy(
+            strategy="ik_interp", sample_count=360, dynamic_collision_mode="off"
         )
-        sim.prepare()
-        diagnostics = sim.get_fluid_diagnostics("water")
-        boundary_info = {
-            "method": "volume_map",
-            "volume_map_enabled": diagnostics["volume_map_enabled"],
-            "sdf_penalty_enabled": diagnostics["sdf_penalty_enabled"],
-            "volume_map_resolution": args.volume_map_resolution,
-            "boundary_shape_count": diagnostics["boundary_shape_count"],
-            "rest_density_kg_m3": diagnostics["rest_density_kg_m3"],
-            "viscosity": diagnostics["viscosity"],
-        }
-        print(json.dumps(boundary_info), flush=True)
-        camera = sim.add_sensor(
-            CameraCfg(
-                uid="demo_camera",
-                width=1280,
-                height=960,
-                intrinsics=[1100.0, 1100.0, 640.0, 480.0],
-                extrinsics=CameraCfg.ExtrinsicsCfg(
-                    eye=[-1.4, -1.4, 1.2],
-                    target=[-0.43, -0.03, 0.47],
-                    up=[0.0, 0.0, 1.0],
+        receiver_pose = sim.get_rigid_object("receiver").get_local_pose(to_matrix=True)
+        receiver_to_cup = torch.eye(4, device=sim.device)
+        receiver_to_cup[:3, 3] = torch.tensor([0.005, 0.09, 0.17], device=sim.device)
+        pour_pose = receiver_pose @ receiver_to_cup
+        invocations = (
+            engine.make_invocation(
+                "pick_up",
+                GraspGoal(semantics),
+                control_parts=bindings,
+                motion_policy=policy,
+                skill_options=PickUpOptions(
+                    fixed_object_to_eef=object_to_eef,
+                    approach_direction=torch.tensor([-1.0, 0, 0], device=sim.device),
+                    pre_grasp_distance=0.08,
+                    lift_height=0.12,
+                    hand_interp_steps=20,
+                    grasp_settle_steps=20,
                 ),
-            )
-        )
-        close_camera = sim.add_sensor(
-            CameraCfg(
-                uid="water_camera",
-                width=960,
-                height=720,
-                intrinsics=[1150.0, 1150.0, 480.0, 360.0],
-                extrinsics=CameraCfg.ExtrinsicsCfg(
-                    eye=[-1.12, -0.73, 0.96],
-                    target=[-0.76, -0.07, 0.61],
-                    up=[0.0, 0.0, 1.0],
+            ),
+            engine.make_invocation(
+                "move_held_object",
+                HeldObjectPoseGoal(pour_pose),
+                control_parts=bindings,
+                motion_policy=policy,
+                skill_options=MoveHeldObjectOptions(),
+            ),
+            engine.make_invocation(
+                "pour",
+                PourGoal(),
+                control_parts=bindings,
+                motion_policy=policy,
+                skill_options=PourOptions(rotate_angle=np.deg2rad(80)),
+            ),
+            engine.make_invocation(
+                "place",
+                PlaceGoal(initial_pose @ object_to_eef),
+                control_parts=bindings,
+                motion_policy=policy,
+                skill_options=PlaceOptions(
+                    hand_interp_steps=20, release_settle_steps=20
                 ),
-            )
-        )
-        if not args.headless:
-            sim.open_window()
-        writer = imageio.get_writer(
-            args.output / "franka_dfsph_pour.mp4",
-            fps=args.fps,
-            codec="libx264",
-            quality=8,
-            macro_block_size=1,
+            ),
         )
-        close_writer = imageio.get_writer(
-            args.output / "water_closeup.mp4",
-            fps=args.fps,
-            codec="libx264",
-            quality=8,
-            macro_block_size=1,
+        control_dt = cfg.sim_steps_per_control * sim.sim_config.physics_dt
+        plan = engine.compile(
+            invocations, engine.initial_context(control_dt=control_dt)
         )
-        metrics = []
-        started = time.perf_counter()
-        previous_cup_pose = initial_pose
-        print(
-            f"DFSPH: {len(points)} particles, {len(points) * spacing**3 * 1e6:.1f} mL, "
-            f"{duration:.2f} s",
-            flush=True,
-        )
-        for step in range(round(duration / dt)):
-            elapsed = step * dt
-            source_time = float(
-                np.clip(
-                    args.start_time + (elapsed - 1.0) * args.speed,
-                    args.start_time,
-                    recorded_times[-1],
+        if not bool(plan.plan_success.all()):
+            raise RuntimeError("Atomic Action planning failed.")
+        if args.renderer != "no-render":
+            params = cfg.events.record_camera.params
+            camera = sim.add_sensor(
+                CameraCfg(
+                    uid="fluid_sim_record",
+                    width=params["resolution"][0],
+                    height=params["resolution"][1],
+                    intrinsics=params["intrinsics"],
+                    visualization_role="record",
+                    extrinsics=CameraCfg.ExtrinsicsCfg(
+                        eye=params["eye"], target=params["target"], up=params["up"]
+                    ),
                 )
             )
-            robot.set_qpos(
-                torch.as_tensor(
-                    joint_curve(source_time), dtype=torch.float32, device=sim.device
-                ).unsqueeze(0),
-                target=False,
-            )
-            cup_pose = _pose(
-                position_curve(source_time), orientation_curve(source_time).as_matrix()
-            )
-            if args.substeps == 1:
-                mug.set_kinematic_state(
-                    torch.from_numpy(cup_pose).unsqueeze(0),
-                    torch.from_numpy(
-                        (cup_pose[:3, 3] - previous_cup_pose[:3, 3]) / dt
-                    ).unsqueeze(0),
-                    torch.from_numpy(
-                        Rotation.from_matrix(
-                            cup_pose[:3, :3] @ previous_cup_pose[:3, :3].T
-                        ).as_rotvec()
-                        / dt
-                    ).unsqueeze(0),
+            writers = [
+                imageio.get_writer(
+                    output / f"fluid_sim_env{i}.mp4",
+                    fps=1 / control_dt,
+                    codec="libx264",
+                    quality=8,
+                    macro_block_size=1,
                 )
-            previous_cup_pose = cup_pose
-            sim.update(step=1, render_final_step=False)
-            if step % frame_stride == 0:
+                for i in range(sim.num_envs)
+            ]
+            if not args.headless:
+                sim.open_window()
+        positions = plan.trajectory.positions
+        poses = [cup.get_local_pose().cpu().numpy().copy()]
+        samples = []
+        started = time.perf_counter()
+        count = positions.shape[1] + round(2 / control_dt)
+        if args.seconds is not None:
+            count = min(count, int(np.ceil(args.seconds / control_dt)))
+        for i in range(count):
+            # Set targets only. Every measured joint/cup pose comes from physics.
+            robot.set_qpos(positions[:, min(i, positions.shape[1] - 1)], target=True)
+            sim.update(step=cfg.sim_steps_per_control, render_final_step=False)
+            poses.append(cup.get_local_pose().cpu().numpy().copy())
+            if camera is not None:
                 with sim.render_frame():
                     sim.sync_render_state()
                     camera.update()
-                    close_camera.update()
-                    rgb = camera.get_data()["color"][0, :, :, :3].cpu().numpy()
-                    close_rgb = (
-                        close_camera.get_data()["color"][0, :, :, :3].cpu().numpy()
-                    )
-                writer.append_data(rgb)
-                close_writer.append_data(close_rgb)
-                if step % args.physics_hz == 0:
-                    cv2.imwrite(
-                        str(args.output / f"frame_{int(elapsed):02d}.png"),
-                        cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR),
-                    )
-                    cv2.imwrite(
-                        str(args.output / f"close_{int(elapsed):02d}.png"),
-                        cv2.cvtColor(close_rgb, cv2.COLOR_RGB2BGR),
-                    )
-            if step % args.physics_hz == 0 or step == round(duration / dt) - 1:
-                if args.substeps > 1:
-                    cup_pose = mug.get_local_pose(to_matrix=True)[0].cpu().numpy()
-                positions = fluid.data.particle_pos_w[0].cpu().numpy()
-                if not np.isfinite(positions).all():
-                    raise RuntimeError("Non-finite DFSPH particle state")
-                velocities = fluid.data.particle_vel_w[0].cpu().numpy()
-                summary = _particle_summary(positions, cup_pose, spacing, velocities)
-                summary["time_s"] = elapsed + dt
-                speed = np.linalg.norm(velocities, axis=1)
-                density = (
-                    sim.get_fluid_diagnostics("water")["particle_density"][0]
-                    .cpu()
-                    .numpy()
+                    rgb = camera.get_data()["color"][..., :3].cpu().numpy()
+                for env_id, writer in enumerate(writers):
+                    writer.append_data(rgb[env_id])
+            if i % round(1 / control_dt) == 0 or i == count - 1:
+                fluid = sim.get_fluid_object("water")
+                state = fluid.data.particle_state_w
+                if not bool(torch.isfinite(state).all()):
+                    raise RuntimeError("Non-finite online fluid state.")
+                local = state[..., :3] - sim.arena_offsets[:, None, :]
+                receiver_pose = sim.get_rigid_object("receiver").get_local_pose(
+                    to_matrix=True
+                )
+                receiver = torch.bmm(
+                    local - receiver_pose[:, None, :3, 3], receiver_pose[:, :3, :3]
                 )
-                summary["speed_p99_m_s"] = float(np.quantile(speed, 0.99))
-                summary["density_p99_kg_m3"] = float(np.quantile(density, 0.99))
-                metrics.append(summary)
-                np.savez_compressed(
-                    args.output / f"particles_{int(elapsed):02d}.npz",
-                    positions=positions,
-                    velocities=velocities,
-                    cup_pose=cup_pose,
-                    time_s=elapsed + dt,
+                received = (
+                    (receiver[..., 2] > 0.0045)
+                    & (receiver[..., 2] < 0.0675)
+                    & (
+                        torch.linalg.vector_norm(
+                            receiver - torch.tensor([0, 0, 0.088], device=sim.device),
+                            dim=-1,
+                        )
+                        < 0.093
+                    )
                 )
-                print(json.dumps(summary), flush=True)
+                sample = {
+                    "time_s": (i + 1) * control_dt,
+                    "cup_pose": poses[-1].tolist(),
+                    "received_ml": (received.sum(dim=1) * args.spacing**3 * 1e6)
+                    .cpu()
+                    .tolist(),
+                }
+                samples.append(sample)
+                print(json.dumps(sample), flush=True)
         report = {
-            "physics": "Newton SPH / DFSPH",
-            "rigid_motion": "kinematic replay of verified MuJoCo trajectory",
-            "coupling": "one-way moving volume-map boundaries; no fluid reaction on robot",
-            "boundary": boundary_info,
-            "bowl": {
-                "center_m": RECEIVER_CENTER.tolist(),
-                "outer_diameter_m": float(2.0 * outer_r[-1]),
-                "height_m": BOWL_HEIGHT,
-                "shape": "curved ceramic bowl with flat foot",
-            },
-            "trajectory": str(args.trajectory.resolve()),
-            "particle_count": len(points),
-            "spacing_m": spacing,
-            "source_start_time_s": args.start_time,
-            "rest_density_kg_m3": diagnostics["rest_density_kg_m3"],
-            "viscosity": diagnostics["viscosity"],
-            "surface_tension_coefficient": args.surface_tension,
-            "render_aabb_trim_fraction": 0.0,
-            "render_only_cull_below_z_m": 0.49,
-            "nominal_volume_ml": len(points) * spacing**3 * 1e6,
-            "simulation_dt": dt,
-            "substeps": args.substeps,
-            "duration_s": duration,
+            "coupling": "online one-way rigid-to-fluid",
+            "controller": "fresh Atomic Action joint targets",
+            "cup": _cup_qualification(poses),
+            "num_envs": sim.num_envs,
+            "particle_count_per_env": sim.get_fluid_object("water").num_particles,
+            "control_dt": control_dt,
+            "simulated_seconds": count * control_dt,
             "wall_seconds": time.perf_counter() - started,
-            "samples": metrics,
+            "samples": samples,
         }
-        (args.output / "validation.json").write_text(
-            json.dumps(report, indent=2) + "\n"
+        report["accepted"] = bool(
+            all(report["cup"]["accepted"])
+            and all(v >= 50 for v in samples[-1]["received_ml"])
         )
-        print(f"Saved {args.output}", flush=True)
+        (output / "validation.json").write_text(json.dumps(report, indent=2) + "\n")
+        print(json.dumps(report["cup"]), flush=True)
+        if args.seconds is None and not report["accepted"]:
+            raise RuntimeError(
+                "Online Sim pouring failed physical qualification; inspect validation.json."
+            )
     finally:
-        if writer is not None:
+        for writer in writers:
             writer.close()
-        if close_writer is not None:
-            close_writer.close()
+        robot = cup = camera = engine = None
         sim.destroy(exit_process=False)
+        del sim
+        SimulationManager.flush_cleanup_queue()
 
 
 if __name__ == "__main__":
diff --git a/tests/gym/envs/test_fluid_integration.py b/tests/gym/envs/test_fluid_integration.py
index 67016e99e..d491abb59 100644
--- a/tests/gym/envs/test_fluid_integration.py
+++ b/tests/gym/envs/test_fluid_integration.py
@@ -40,6 +40,17 @@
 )
 
 
+def _replay_cfg():
+    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    cfg.task_program = None
+    cup = cfg.rigid_object.pop(0)
+    cup.body_type = "kinematic"
+    cfg.background.append(cup)
+    cfg.sim_cfg.physics_cfg.num_substeps = 1
+    cfg.sim_cfg.physics_cfg.solver_cfg.pop("rigid_solver_cfg", None)
+    return cfg
+
+
 @pytest.fixture(autouse=True)
 def task_mesh_cache(tmp_path, monkeypatch):
     """Resolve task meshes locally so configuration tests never download them."""
@@ -67,7 +78,7 @@ def resolve(name, extra_modules=None):
     return directory
 
 
-def test_fluid_deployment_resolves_sources_and_observation_entities(task_mesh_cache):
+def test_fluid_deployment_resolves_sources_and_observation_entities():
     cfg = config_to_cfg(load_config(TASK), source_path=TASK)
     assert cfg.sim_cfg.render_cfg.renderer == "fast-rt"
     assert cfg.events.record_camera.params["per_env"] is True
@@ -84,10 +95,10 @@ def test_fluid_deployment_resolves_sources_and_observation_entities(task_mesh_ca
         isinstance(item, SceneEntityCfg)
         for item in cfg.observations.received_volume.params["entity_cfgs"]
     )
-    cup = next(item for item in cfg.background if item.uid == "cup")
-    assert cup.visual_shape.fpath == str(task_mesh_cache / "cup.glb")
-    assert cup.shape.fpath == str(task_mesh_cache / "mug_wall.obj")
-    assert cup.shape.collision.force_sdf is False
+    cup = next(item for item in cfg.rigid_object if item.uid == "cup")
+    assert Path(cup.particle_collision_shape.fpath).is_absolute()
+    assert cup.body_type == "dynamic"
+    assert cfg.task_program is not None
 
 
 def test_fluid_component_rejects_duplicate_inline_ownership():
@@ -143,7 +154,7 @@ def test_fluid_replay_rejects_unqualified_recording_before_world_creation():
     from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
     from tensordict import TensorDict
 
-    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    cfg = _replay_cfg()
     source = {
         "meta": {"num_envs": 1, "num_steps": 2, "lengths": [2]},
         "states": TensorDict({}, batch_size=[1, 2]),
@@ -191,7 +202,7 @@ def test_replay_adopts_source_root_before_scene_creation(monkeypatch):
     from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
     from embodichain.lab.gym.envs.embodied_env import EmbodiedEnv
 
-    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    cfg = _replay_cfg()
     cfg.dataset = {}  # Empty mapping is a supported Python configuration.
     captured = []
 
@@ -210,7 +221,7 @@ def initialize(self, cfg, **kwargs):
 def test_replay_rejects_misleading_policy_recording(kind):
     from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
 
-    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    cfg = _replay_cfg()
     if kind == "trajectory":
         cfg.record_trajectory = True
     elif kind == "actions":
@@ -224,7 +235,7 @@ def test_replay_rejects_misleading_policy_recording(kind):
 def test_replay_rejects_moving_root_before_scene_creation():
     from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
 
-    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
+    cfg = _replay_cfg()
     source = _qualified_source()
     source["states"]["robot", "root_pose"][0, 1, 0] = 0.2
     with pytest.raises(ValueError, match="fixed-root"):
diff --git a/tests/gym/envs/test_fluid_replay_runtime.py b/tests/gym/envs/test_fluid_replay_runtime.py
index 91ad4755d..838e1628a 100644
--- a/tests/gym/envs/test_fluid_replay_runtime.py
+++ b/tests/gym/envs/test_fluid_replay_runtime.py
@@ -46,6 +46,13 @@ def setup_method(self):
             / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
         )
         cfg = config_to_cfg(load_config(task), source_path=task)
+        cfg.task_program = None
+        cup_cfg = cfg.rigid_object.pop(0)
+        cup_cfg.body_type = "kinematic"
+        cfg.background.append(cup_cfg)
+        cfg.events.settle_cup = None
+        cfg.sim_cfg.physics_cfg.num_substeps = 1
+        cfg.sim_cfg.physics_cfg.solver_cfg.pop("rigid_solver_cfg", None)
         cfg.sim_cfg.render_cfg = RenderCfg(renderer="no-render")
         cfg.sim_cfg.headless = True
         cfg.enable_sensor = False
@@ -59,7 +66,9 @@ def setup_method(self):
             particle_support_radius=0.0225,
         )
         for obj in cfg.background:
-            obj.shape.collision.sdf_resolution = 16
+            for shape in (obj.shape, obj.particle_collision_shape):
+                if shape is not None and getattr(shape, "collision", None) is not None:
+                    shape.collision.sdf_resolution = 16
         fluid = cfg.fluid_object[0]
         fluid.particle_spacing = 0.01
         fluid.particle_radius = 0.003
diff --git a/tests/lab/scripts/test_run_fluid_task_program.py b/tests/lab/scripts/test_run_fluid_task_program.py
index 3d58be4e9..5fa46f360 100644
--- a/tests/lab/scripts/test_run_fluid_task_program.py
+++ b/tests/lab/scripts/test_run_fluid_task_program.py
@@ -14,24 +14,23 @@
 # limitations under the License.
 # ----------------------------------------------------------------------------
 
-"""Fluid replay launcher renderer and final recording commit contracts."""
+"""Online fluid deployment and measured physical qualification."""
 
 from __future__ import annotations
 
-import argparse
-import json
 from pathlib import Path
-from types import SimpleNamespace
-from unittest.mock import Mock
-import sys
+import numpy as np
 import pytest
-import torch
 
-from embodichain.lab.scripts import run_fluid_task_program as runner
+from embodichain.lab.scripts.run_fluid_task_program import (
+    _configure,
+    _cup_qualification,
+)
 
 pytestmark = pytest.mark.no_sim
+ROOT = Path(__file__).resolve().parents[3]
 TASK = (
-    Path(__file__).resolve().parents[3]
+    ROOT
     / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
 )
 
@@ -63,219 +62,101 @@ def resolve(name, extra_modules=None):
     return directory
 
 
-@pytest.mark.parametrize("renderer", [None, "fast-rt"])
-def test_launcher_selects_supported_renderers(renderer, monkeypatch, tmp_path):
-    args = [
-        "fluid",
-        "--trajectory",
-        str(tmp_path / "source.pt"),
-        "--output",
-        str(tmp_path),
-    ]
-    if renderer is not None:
-        args += ["--renderer", renderer]
-    monkeypatch.setattr(sys, "argv", args)
-    monkeypatch.setattr(runner, "discover_task_packages", lambda: None)
-    replay = Mock()
-    monkeypatch.setattr(runner, "_replay", replay)
-    runner.main()
-    assert replay.call_args.args[0].renderer == "fast-rt"
-
-
-@pytest.mark.parametrize("spacing", [None, 0.003, 0.006])
-def test_final_episode_is_committed_through_record_camera(
-    spacing, monkeypatch, tmp_path
-):
-    from embodichain.lab.gym.envs import fluid_replay
-    from embodichain.lab.gym.envs.managers import observations
-
-    recorder = SimpleNamespace(save_and_clear=Mock())
-    created = []
-    decode_cfg = runner.config_to_cfg
-
-    def configured_render_settings(*args, **kwargs):
-        cfg = decode_cfg(*args, **kwargs)
-        cfg.sim_cfg.render_cfg.spp = 8
-        return cfg
-
-    monkeypatch.setattr(runner, "config_to_cfg", configured_render_settings)
-
-    class Environment:
-        def __init__(self, cfg, **kwargs):
-            created.append(cfg)
-            self.num_envs = cfg.num_envs
-            self.device = torch.device("cpu")
-            self.step_dt = 1 / 24
-            self.control_frequency = 24
-            self.replay_duration = self.step_dt
-            self.active_joint_ids = [0]
-            self.event_manager = SimpleNamespace(get_functor=lambda name: recorder)
-            self.sim = SimpleNamespace(
-                get_fluid_object=lambda uid: SimpleNamespace(num_particles=8)
-            )
-            self.source_qualification = {"accepted": True}
-            self.close = Mock()
-
-        def reset(self, **kwargs):
-            return {}, {}
-
-        def step(self, action):
-            info = {
-                "fluid_replay_time_s": torch.full((self.num_envs,), self.step_dt),
-                "fluid_replay_finished": torch.ones(self.num_envs, dtype=torch.bool),
-            }
-            return {}, None, None, None, info
-
-    monkeypatch.setattr(fluid_replay, "FluidReplayEnv", Environment)
-    monkeypatch.setattr(
-        observations,
-        "get_fluid_summary",
-        lambda env, *args: torch.ones(env.num_envs, 5),
+def test_online_task_uses_live_grasp_and_same_body_particle_boundary(tmp_path):
+    task_id, cfg = _configure(TASK, num_envs=2, renderer="fast-rt", output=tmp_path)
+    assert task_id == "PourCup-Franka-Fluid-v1"
+    assert cfg.task_program is not None
+    cup = next(c for c in cfg.rigid_object if c.uid == "cup")
+    assert cup.body_type == "dynamic"
+    assert Path(cup.particle_collision_shape.fpath).is_absolute()
+    assert cup.particle_collision_shape.collision.approximation == "sdf"
+    assert cup.shape.collision.approximation == "convex_decomposition"
+    solver = cfg.sim_cfg.physics_cfg.solver_cfg
+    assert solver["rigid_solver_cfg"]["solver_type"] == "mujoco_warp"
+    assert solver["rigid_solver_substeps"] == 7
+    assert cfg.events.record_camera.params["per_env"] is True
+    assert cfg.events.record_camera.params["max_env_num"] == 2
+    assert cfg.record_trajectory is False
+    assert cfg.sim_steps_per_control * cfg.sim_cfg.physics_dt == pytest.approx(0.025)
+    assert not hasattr(cfg.events, "settle_cup")
+
+
+def test_no_render_coarse_override_resamples_and_keeps_physical_scale(tmp_path):
+    _, cfg = _configure(
+        TASK, num_envs=1, renderer="no-render", output=tmp_path, particle_spacing=0.003
     )
-    monkeypatch.setattr(
-        observations,
-        "get_fluid_volume_in_region",
-        lambda env, *args, **kwargs: torch.zeros(env.num_envs, 1),
-    )
-    args = argparse.Namespace(
-        replay_task=str(TASK),
-        num_envs=3,
-        renderer="fast-rt",
-        particle_spacing=spacing,
-        speed=2,
-        seconds=None,
-        partial_reset_step=None,
-    )
-    runner._replay(args, tmp_path / "source.pt", tmp_path)
-    recorder.save_and_clear.assert_called_once_with()
-    cfg = created[0]
-    assert cfg.sim_cfg.render_cfg.spp == 8
-    assert cfg.events.record_camera.params["max_env_num"] == 3
-    assert cfg.events.record_camera.params["save_path"] == str(tmp_path / "videos")
-    result = json.loads((tmp_path / "fluid_gym_result.json").read_text())
-    assert result["renderer"] == "fast-rt"
-    assert result["fluid_source"]["kind"] == "revolved_volume"
-    assert result["fluid_source"]["file_path"] is None
-    expected_spacing = spacing or 0.0015
-    assert result["particle_spacing_m"] == expected_spacing
     fluid = cfg.fluid_object[0]
-    scale = expected_spacing / 0.0015
-    assert fluid.render.render_radius == pytest.approx(0.00045 * scale)
-    assert fluid.render.mesh_surface["cell_size"] == pytest.approx(0.00081 * scale)
-    assert fluid.render.mesh_surface["kernel_radius"] == pytest.approx(0.00225 * scale)
+    assert fluid.render.mode == "none"
+    assert fluid.particle_radius == pytest.approx(0.0009)
+    assert fluid.render.mesh_surface["kernel_radius"] == pytest.approx(0.0045)
     assert cfg.sim_cfg.physics_cfg.solver_cfg[
         "particle_support_radius"
-    ] == pytest.approx(2.25 * expected_spacing)
-    assert result["video_fps"] == 24
-    assert result["videos"] == [
-        f"videos/episode_0_fluid_gym_env{i}.mp4" for i in range(3)
-    ]
-
-
-@pytest.mark.parametrize("reset_step", [0, 1])
-def test_partial_reset_reports_current_row_clocks_and_observations(
-    reset_step, monkeypatch, tmp_path
-):
-    from embodichain.lab.gym.envs import fluid_replay
-    from embodichain.lab.gym.envs.managers import observations
+    ] == pytest.approx(0.00675)
+    assert cfg.events.record_camera is None
 
-    class Environment:
-        def __init__(self, cfg, **kwargs):
-            self.num_envs = cfg.num_envs
-            self.device = torch.device("cpu")
-            self.step_dt = 1 / 24
-            self.replay_duration = 2 * self.step_dt
-            self.active_joint_ids = [0]
-            self.source_qualification = {"accepted": True}
-            self.close = Mock()
-            self.clock = torch.zeros(self.num_envs)
-            self.water = SimpleNamespace(
-                num_particles=1,
-                data=SimpleNamespace(particle_state_w=torch.zeros(self.num_envs, 1, 6)),
-            )
-            self.sim = SimpleNamespace(get_fluid_object=lambda uid: self.water)
 
-        def snapshot(self):
-            return {"clock": self.clock.clone()}, {
-                "fluid_replay_time_s": self.clock.clone(),
-                "fluid_replay_finished": self.clock >= self.replay_duration,
-            }
+def test_physical_qualification_does_not_accept_motion_commands_alone():
+    initial = np.array([[0, 0, 0.5, 0, 0, 0, 1], [1, 0, 0.5, 0, 0, 0, 1]], dtype=float)
+    moving = initial.copy()
+    moving[0, 2] += 0.12
+    moving[0, 3] = np.sin(np.deg2rad(35))
+    moving[0, 6] = np.cos(np.deg2rad(35))
+    result = _cup_qualification([initial, moving, initial])
+    assert result["accepted"] == [True, False]
+    assert result["max_tilt_deg"] == pytest.approx([70, 0])
+    assert result["lift_m"] == pytest.approx([0.12, 0])
 
-        def reset(self, *, seed=None, options=None):
-            ids = options["reset_ids"] if options else torch.arange(self.num_envs)
-            self.clock[ids] = 0
-            self.water.data.particle_state_w[ids] = 0
-            return self.snapshot()
 
-        def step(self, action):
-            self.clock += self.step_dt
-            self.water.data.particle_state_w[..., 0] += 1
-            obs, info = self.snapshot()
-            return obs, None, None, None, info
+@pytest.mark.parametrize("renderer", ["fast-rt", "no-render"])
+def test_online_runner_commits_last_recording_before_close(
+    renderer, tmp_path, monkeypatch
+):
+    import argparse
+    from types import SimpleNamespace
+    from unittest.mock import Mock
+    import torch
+    from embodichain.lab.scripts import run_fluid_task_program as runner
 
-    monkeypatch.setattr(fluid_replay, "FluidReplayEnv", Environment)
-    monkeypatch.setattr(
-        observations,
-        "get_fluid_summary",
-        lambda env, obs, *args: torch.stack(
-            (obs["clock"], torch.ones(env.num_envs)), dim=-1
-        ),
+    order = []
+    recorder = SimpleNamespace(
+        save_and_clear=Mock(side_effect=lambda: order.append("save"))
     )
-    monkeypatch.setattr(
-        observations,
-        "get_fluid_volume_in_region",
-        lambda env, *args, **kwargs: torch.zeros(env.num_envs, 1),
-    )
-    args = argparse.Namespace(
-        replay_task=str(TASK),
+    env = SimpleNamespace(
         num_envs=2,
-        renderer="no-render",
-        particle_spacing=None,
-        speed=2,
-        seconds=None,
-        partial_reset_step=reset_step,
+        step_dt=0.05,
+        control_frequency=20,
+        elapsed_steps=torch.zeros(2),
+        reset=Mock(),
+        event_manager=SimpleNamespace(get_functor=lambda name: recorder),
+        sim=SimpleNamespace(
+            get_rigid_object=lambda uid: SimpleNamespace(
+                get_local_pose=lambda: torch.tensor([[0.0, 0, 0, 0, 0, 0, 1]]).repeat(
+                    2, 1
+                )
+            ),
+            get_fluid_object=lambda uid: SimpleNamespace(num_particles=1),
+        ),
+        close=Mock(side_effect=lambda **kwargs: order.append("close")),
     )
-
-    runner._replay(args, tmp_path / "source.pt", tmp_path)
-
-    result = json.loads((tmp_path / "fluid_gym_result.json").read_text())
-    step_dt = 1 / 24
-    expected_times = (
-        [[0, step_dt], [step_dt, 2 * step_dt]]
-        if reset_step == 0
-        else [[step_dt, step_dt], [0, 2 * step_dt]]
+    result = SimpleNamespace(
+        completed=False, all_success=False, to_metadata=lambda: {"completed": False}
     )
-    assert len(result["samples"]) == len(expected_times)
-    for sample, expected in zip(result["samples"], expected_times):
-        assert sample["time_s"] == pytest.approx(expected)
-        assert [row[0] for row in sample["summary"]] == pytest.approx(expected)
-    assert result["replay_complete"] == [False, True]
-
-
-def test_spacing_override_rejects_fixed_points_before_scene_creation(
-    monkeypatch, tmp_path
-):
-    from embodichain.lab.gym.utils.gym_utils import config_to_cfg
-    from embodichain.lab.sim.cfg import FluidSpawnCfg
-    from embodichain.utils.utility import load_config
-
-    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
-    cfg.fluid_object[0].spawn = FluidSpawnCfg(positions=[[0, 0, 0]])
-    monkeypatch.setattr(runner, "config_to_cfg", lambda *args, **kwargs: cfg)
+    monkeypatch.setattr(runner, "make", lambda *args, **kwargs: env)
+    monkeypatch.setattr(runner, "discover_task_packages", lambda: None)
+    monkeypatch.setattr(runner, "execute_demo_episode", lambda *args, **kwargs: result)
+    monkeypatch.setattr(runner, "_sample", lambda env: {"received_ml": [0.0, 0.0]})
+    monkeypatch.setattr(runner.SimulationManager, "flush_cleanup_queue", lambda: None)
     args = argparse.Namespace(
-        replay_task=str(TASK),
+        task=str(TASK),
         num_envs=2,
-        renderer="fast-rt",
-        particle_spacing=0.006,
-        speed=2,
+        renderer=renderer,
+        particle_spacing=None,
+        record_trajectory=False,
+        seconds=0.05,
+        settle_seconds=0,
     )
-    with pytest.raises(ValueError, match="fixed sampling"):
-        runner._replay(args, tmp_path / "unused.pt", tmp_path)
-
-
-def test_fluid_launcher_rejects_invisible_hybrid_backend(monkeypatch, capsys):
-    monkeypatch.setattr(sys, "argv", ["fluid", "--renderer", "hybrid"])
-    with pytest.raises(SystemExit) as error:
-        runner.main()
-    assert error.value.code == 2
-    assert "invalid choice" in capsys.readouterr().err
+    report = runner._run(args, tmp_path)
+    assert report["accepted"] is False
+    assert order == (["save", "close"] if renderer == "fast-rt" else ["close"])
+    assert len(report["videos"]) == (2 if renderer == "fast-rt" else 0)
+    assert (tmp_path / "fluid_gym_result.json").is_file()
diff --git a/tests/sim/objects/test_fluid_online_runtime.py b/tests/sim/objects/test_fluid_online_runtime.py
new file mode 100644
index 000000000..30f1b3868
--- /dev/null
+++ b/tests/sim/objects/test_fluid_online_runtime.py
@@ -0,0 +1,285 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Online rigid boundaries, mass ownership and selected fluid resets."""
+
+from __future__ import annotations
+
+import pytest
+from pathlib import Path
+import subprocess
+import sys
+import torch
+import trimesh
+
+from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
+from embodichain.lab.sim.cfg import (
+    FluidObjectCfg,
+    FluidSpawnCfg,
+    FluidRenderCfg,
+    NewtonPhysicsCfg,
+    RenderCfg,
+    RigidObjectCfg,
+)
+from embodichain.lab.sim.shapes import CubeCfg, MeshCfg, MeshCollisionCfg
+
+pytestmark = [pytest.mark.gpu, pytest.mark.subprocess_sim]
+
+
+class _FluidOnlineRuntime:
+    def test_live_dynamic_boundary_and_selected_reset(self):
+        from dexsim.engine.newton_physics.backend_registry import get_newton_backend
+
+        mesh = trimesh.creation.box(extents=[0.1] * 3)
+        sim = SimulationManager(
+            SimulationManagerCfg(
+                headless=True,
+                device="cuda",
+                num_envs=2,
+                arena_space=0,
+                physics_dt=0.001,
+                render_cfg=RenderCfg(renderer="no-render"),
+                physics_cfg=NewtonPhysicsCfg(
+                    num_substeps=1,
+                    collision_cfg=None,
+                    solver_cfg={
+                        "solver_type": "sph",
+                        "sph_method": "dfsph",
+                        "rigid_solver_cfg": {"rne_mode": "disabled"},
+                        "rigid_solver_substeps": 2,
+                        "particle_support_radius": 0.0225,
+                        "volume_map_resolution": 16,
+                        "volume_map_quadrature_samples": 2,
+                    },
+                ),
+            )
+        )
+        body = fluid = None
+        try:
+            body = sim.add_rigid_object(
+                RigidObjectCfg.from_dict(
+                    {
+                        "uid": "boundary",
+                        "shape": CubeCfg(size=[0.1] * 3).to_dict(),
+                        "particle_collision_shape": MeshCfg(
+                            vertices=mesh.vertices.tolist(),
+                            triangles=mesh.faces.tolist(),
+                            collision=MeshCollisionCfg(
+                                approximation="sdf",
+                                sdf_resolution=32,
+                                sdf_narrow_band_range=(-0.05, 0.05),
+                                sdf_padding=0.02,
+                            ),
+                        ).to_dict(),
+                        "body_type": "dynamic",
+                        "init_pos": [0, 0, 1],
+                        "attrs": {"mass_props": {"mass": 0.3}},
+                    }
+                )
+            )
+            fluid = sim.add_fluid_object(
+                FluidObjectCfg(
+                    uid="water",
+                    particle_spacing=0.01,
+                    particle_radius=0.003,
+                    spawn=FluidSpawnCfg(
+                        kind="points",
+                        positions=[
+                            [0.055, -0.005, 0.995],
+                            [0.055, 0.005, 0.995],
+                            [0.055, -0.005, 1.005],
+                            [0.055, 0.005, 1.005],
+                        ],
+                    ),
+                    render=FluidRenderCfg(mode="none"),
+                )
+            )
+            sim.prepare()
+            backend = get_newton_backend(sim._world)
+            assert backend.solver.sph_solver.sdf_shape_inventory.shape_count == 2
+            torch.testing.assert_close(
+                torch.as_tensor(backend.model.body_mass.numpy()),
+                torch.tensor([0.3, 0.3]),
+            )
+            torch.testing.assert_close(
+                torch.as_tensor(backend.model.body_inertia.numpy())[
+                    :, [0, 1, 2], [0, 1, 2]
+                ],
+                torch.full((2, 3), 0.0005),
+                rtol=0.001,
+                atol=1e-7,
+            )
+            body.set_velocity(
+                lin_vel=torch.tensor([[0.5, 0, 0], [0.5, 0, 0]], device=sim.device)
+            )
+            sim.update(step=200, render_final_step=False)
+            assert backend.sim_time == pytest.approx(0.2)
+            assert sim.physics.cuda_graph_status == "captured"
+            pose = body.get_local_pose()
+            assert bool((pose[:, 0] > 0.095).all())
+            assert bool((fluid.data.particle_pos_w[..., 0] > 0.12).all())
+            # Water has no reaction force on the prescribed initial rigid momentum.
+            torch.testing.assert_close(
+                pose[:, 0],
+                torch.full((2,), 0.1, device=sim.device),
+                rtol=0.01,
+                atol=0.001,
+            )
+            particles = fluid.data.particle_state_w.clone()
+            fluid.reset([0])
+            torch.testing.assert_close(
+                fluid.data.particle_state_w[1], particles[1], rtol=0, atol=0
+            )
+            torch.testing.assert_close(body.get_local_pose(), pose, rtol=0, atol=0)
+            sim.reset_objects_state(env_ids=[0])
+            torch.testing.assert_close(
+                body.get_local_pose()[1], pose[1], rtol=0, atol=0
+            )
+            sim.update(step=1, render_final_step=False)
+            assert bool(torch.isfinite(fluid.data.particle_state_w).all())
+        finally:
+            body = fluid = None
+            sim.destroy(exit_process=False)
+            SimulationManager.flush_cleanup_queue()
+
+    def test_mimic_follower_uses_inner_rigid_solver(self):
+        from embodichain_robots import FrankaPandaCfg
+
+        sim = SimulationManager(
+            SimulationManagerCfg(
+                headless=True,
+                device="cuda",
+                num_envs=1,
+                physics_dt=0.001,
+                render_cfg=RenderCfg(renderer="no-render"),
+                physics_cfg=NewtonPhysicsCfg(
+                    num_substeps=1,
+                    collision_cfg=None,
+                    solver_cfg={
+                        "solver_type": "sph",
+                        "rigid_solver_cfg": {"rne_mode": "disabled"},
+                        "rigid_solver_substeps": 2,
+                        "volume_map_boundary_enabled": False,
+                        "sdf_boundary_enabled": False,
+                    },
+                ),
+            )
+        )
+        robot = fluid = None
+        try:
+            robot = sim.add_robot(FrankaPandaCfg.from_dict({}))
+            fluid = sim.add_fluid_object(
+                FluidObjectCfg(
+                    uid="water",
+                    particle_spacing=0.01,
+                    init_pos=(2, 0, 2),
+                    spawn=FluidSpawnCfg(kind="box", size=(0.02,) * 3),
+                    render=FluidRenderCfg(mode="none"),
+                )
+            )
+            sim.prepare()
+            assert robot._newton_mimic_compliance_configured
+            joint_ids = robot.get_joint_ids("eef", remove_mimic=True)
+            robot.set_qpos(
+                torch.zeros((1, len(joint_ids)), device=sim.device), joint_ids=joint_ids
+            )
+            sim.update(step=500, render_final_step=False)
+            fingers = robot.get_qpos(name="eef")
+            assert bool((fingers.abs() < 0.004).all()), fingers
+            assert float((fingers[:, 0] - fingers[:, 1]).abs().max()) < 0.002
+        finally:
+            robot = fluid = None
+            sim.destroy(exit_process=False)
+            SimulationManager.flush_cleanup_queue()
+
+    def test_normal_gym_partial_reset_does_not_advance_peer(self):
+        from embodichain.lab.gym.utils.gym_utils import config_to_cfg
+        from embodichain.lab.gym.utils.registration import discover_task_packages, make
+        from embodichain.utils.utility import load_config
+
+        task = (
+            Path(__file__).resolve().parents[3]
+            / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
+        )
+        discover_task_packages()
+        declaration = load_config(task)
+        cfg = config_to_cfg(declaration, source_path=task)
+        cfg.num_envs = 2
+        cfg.enable_sensor = False
+        cfg.sim_cfg.render_cfg = RenderCfg(renderer="no-render")
+        cfg.sim_cfg.headless = True
+        cfg.sim_steps_per_control = 1
+        cfg.events.record_camera = None
+        cfg.sim_cfg.physics_cfg.solver_cfg.update(
+            volume_map_boundary_enabled=False, sdf_boundary_enabled=False
+        )
+        for obj in cfg.background + cfg.rigid_object:
+            for shape in (obj.shape, obj.particle_collision_shape):
+                collision = getattr(shape, "collision", None)
+                if collision is not None and collision.approximation == "sdf":
+                    collision.sdf_resolution = 16
+        fluid_cfg = cfg.fluid_object[0]
+        fluid_cfg.particle_spacing = 0.01
+        fluid_cfg.particle_radius = 0.003
+        fluid_cfg.spawn = FluidSpawnCfg(
+            kind="box", size=(0.02,) * 3, reference_uid="cup"
+        )
+        fluid_cfg.render = FluidRenderCfg(mode="none")
+        env = make(declaration["id"], cfg=cfg)
+        try:
+            env.reset(seed=0)
+            action = env.robot.get_qpos()[:, env.active_joint_ids].clone()
+            for _ in range(3):
+                env.step(action)
+            fluid = env.sim.get_fluid_object("water")
+            before_water = fluid.data.particle_state_w[1].clone()
+            before_robot = env.robot.get_qpos()[1].clone()
+            before_cup = env.sim.get_rigid_object("cup").get_local_pose()[1].clone()
+            env.reset(options={"reset_ids": [0], "save_data": False})
+            torch.testing.assert_close(
+                fluid.data.particle_state_w[1], before_water, rtol=0, atol=0
+            )
+            torch.testing.assert_close(
+                env.robot.get_qpos()[1], before_robot, rtol=0, atol=0
+            )
+            torch.testing.assert_close(
+                env.sim.get_rigid_object("cup").get_local_pose()[1],
+                before_cup,
+                rtol=0,
+                atol=0,
+            )
+            assert env.elapsed_steps.tolist() == [0, 3]
+        finally:
+            env.close(exit_process=False)
+            del env
+            SimulationManager.flush_cleanup_queue()
+
+
+def test_online_fluid_boundaries_and_mimic_reset_in_fresh_process():
+    result = subprocess.run(
+        [sys.executable, str(Path(__file__).resolve())],
+        capture_output=True,
+        text=True,
+        timeout=180,
+    )
+    assert result.returncode == 0, result.stdout + result.stderr
+
+
+if __name__ == "__main__":
+    worker = _FluidOnlineRuntime()
+    worker.test_live_dynamic_boundary_and_selected_reset()
+    worker.test_mimic_follower_uses_inner_rigid_solver()
+    worker.test_normal_gym_partial_reset_does_not_advance_peer()
diff --git a/tests/sim/spawn/test_descriptors.py b/tests/sim/spawn/test_descriptors.py
index 11b57a2a0..f535ad149 100644
--- a/tests/sim/spawn/test_descriptors.py
+++ b/tests/sim/spawn/test_descriptors.py
@@ -2340,3 +2340,51 @@ def test_rigid_descriptor_rejects_orientation_without_principal_moments() -> Non
     )
     with pytest.raises(ValueError, match="requires explicit principal inertia"):
         rigid_desc_from_cfg(cfg)
+
+
+@pytest.mark.parametrize("solver", ["sph", "mujoco_warp"])
+def test_particle_boundary_shares_body_without_replacing_rigid_contact(solver):
+    from embodichain.lab.sim.cfg import RigidObjectCfg
+    from embodichain.lab.sim.shapes import MeshCollisionCfg
+    from embodichain.lab.sim.spawn.descriptors import rigid_desc_from_cfg
+
+    cfg = RigidObjectCfg(
+        uid="cup",
+        shape=MeshCfg(
+            fpath="/assets/cup.glb",
+            collision=MeshCollisionCfg(
+                approximation="convex_decomposition", max_hulls=32
+            ),
+        ),
+        particle_collision_shape=MeshCfg(
+            fpath="/assets/cavity.obj",
+            collision=MeshCollisionCfg(approximation="sdf", sdf_resolution=192),
+        ),
+    )
+    desc, _ = rigid_desc_from_cfg(cfg, newton_solver_type=solver)
+    assert len(desc.collisions) == 2
+    contact, boundary = desc.collisions
+    assert contact.approximation == CollisionApproximation.CONVEX_DECOMPOSITION
+    assert contact.newton.has_particle_collision is False
+    assert boundary.approximation == CollisionApproximation.SDF
+    assert boundary.enable_collision is False
+    assert boundary.newton.has_particle_collision is True
+    assert boundary.newton.contributes_to_mass is False
+    assert boundary.newton.sdf_max_resolution == 192
+    assert desc.renders[0].file_path == "/assets/cup.glb"
+    assert cfg.shape.collision.approximation == "convex_decomposition"
+
+
+def test_particle_boundary_requires_newton_and_sdf():
+    from embodichain.lab.sim.cfg import RigidObjectCfg
+    from embodichain.lab.sim.spawn.descriptors import rigid_desc_from_cfg
+
+    cfg = RigidObjectCfg(
+        uid="cup",
+        shape=MeshCfg(fpath="/assets/cup.glb"),
+        particle_collision_shape=MeshCfg(fpath="/assets/cavity.obj"),
+    )
+    with pytest.raises(ValueError, match="Newton"):
+        rigid_desc_from_cfg(cfg)
+    with pytest.raises(ValueError, match="sdf"):
+        rigid_desc_from_cfg(cfg, newton_solver_type="sph")

From eb6b9aa85f53f41a80799260cef4e69c41195375 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Thu, 22 Oct 2026 15:13:09 +0800
Subject: [PATCH 13/19] refactor(fluid): separate rc1 foundation from pouring
 tasks

---
 agent_context/MAP.yaml                        |  10 +-
 .../topics/data-assets/data-assets.md         |   9 +-
 .../topics/env-framework/env-framework.md     |   2 -
 .../topics/env-framework/fluid-replay.md      |  69 ----
 .../topics/manager-functor/manager-functor.md |  17 -
 .../topics/simulation-system/fluids.md        | 121 +++---
 .../embodichain/embodichain.lab.gym.rst       |   2 +-
 docs/source/api_reference/public_api.rst      | 106 ++----
 embodichain/data/assets/__init__.py           |   4 +-
 embodichain/data/assets/task_assets.py        |  78 ----
 embodichain/data/download.py                  |   1 -
 embodichain/lab/gym/envs/embodied_env.py      |   8 -
 embodichain/lab/gym/envs/fluid_replay.py      | 331 ----------------
 embodichain/lab/gym/envs/managers/events.py   |  32 --
 .../lab/gym/envs/managers/observations.py     | 131 -------
 embodichain/lab/gym/envs/managers/record.py   | 117 +-----
 .../lab/gym/utils/_component_composition.py   |  25 +-
 embodichain/lab/gym/utils/gym_utils.py        |  42 +--
 .../lab/scripts/run_fluid_task_program.py     | 267 -------------
 embodichain/lab/sim/_runtime_controls.py      |  56 ---
 embodichain/lab/sim/cfg/rigid_object.py       |  16 -
 .../lab/sim/objects/backends/newton.py        |  14 +-
 embodichain/lab/sim/objects/rigid_object.py   |  85 +----
 embodichain/lab/sim/shapes.py                 |  11 -
 embodichain/lab/sim/sim_manager.py            |  61 ---
 embodichain/lab/sim/spawn/descriptors.py      |  70 +---
 .../embodiments/franka_panda_firm_grip.yaml   |  88 -----
 .../franka_panda_firm_grip_newton.yaml        |  91 -----
 .../trajectory_open_loop_gentle.yaml          |  29 --
 .../trajectory_open_loop_pour_cup.yaml        |  29 --
 .../manipulation/tableware/pour_cup/README.md |  93 -----
 .../tableware/pour_cup/env.fluid.yaml         | 356 ------------------
 .../tableware/pour_cup/env.newton.yaml        | 116 ------
 .../manipulation/tableware/pour_cup/env.yaml  |  90 -----
 .../tableware/pour_cup/task.franka.fluid.yaml |  26 --
 .../pour_cup/task.franka.newton.yaml          |  26 --
 .../tableware/pour_cup/task.franka.yaml       |  16 -
 .../task_program/integration.fluid.yaml       |  97 -----
 .../task_program/integration.newton.yaml      |  67 ----
 .../pour_cup/task_program/integration.yaml    |  67 ----
 .../pour_cup/task_program/program.fluid.yaml  |  96 -----
 .../pour_cup/task_program/program.newton.yaml |  40 --
 .../pour_cup/task_program/program.yaml        |  40 --
 scripts/tutorials/sim/create_fluid.py         | 153 ++++++++
 .../tutorials/sim/franka_dfsph_pour_demo.py   | 309 ---------------
 tests/data/assets/test_task_assets.py         | 102 -----
 tests/data/test_download_prefix.py            |  13 +-
 tests/gym/envs/managers/test_record.py        | 149 --------
 tests/gym/envs/test_fluid_integration.py      | 280 --------------
 tests/gym/envs/test_fluid_replay_runtime.py   | 178 ---------
 tests/gym/envs/test_official_task_layout.py   |   1 -
 tests/gym/utils/test_gym_utils.py             |  37 --
 .../scripts/test_run_fluid_task_program.py    | 162 --------
 tests/sim/objects/test_fluid_batch_runtime.py | 125 ------
 tests/sim/objects/test_fluid_object.py        |  53 ---
 .../sim/objects/test_fluid_online_runtime.py  | 285 --------------
 tests/sim/objects/test_fluid_runtime.py       |   9 -
 tests/sim/spawn/test_descriptors.py           |  48 ---
 tests/sim/test_fluid_cfg.py                   |  37 --
 tests/sim/test_sim_manager.py                 |  21 +-
 tests/test_task_program_package_data.py       |   6 -
 61 files changed, 293 insertions(+), 4727 deletions(-)
 delete mode 100644 agent_context/topics/env-framework/fluid-replay.md
 delete mode 100644 embodichain/data/assets/task_assets.py
 delete mode 100644 embodichain/lab/gym/envs/fluid_replay.py
 delete mode 100644 embodichain/lab/scripts/run_fluid_task_program.py
 delete mode 100644 embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip.yaml
 delete mode 100644 embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip_newton.yaml
 delete mode 100644 embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_gentle.yaml
 delete mode 100644 embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_pour_cup.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.fluid.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.newton.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.fluid.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.newton.yaml
 delete mode 100644 embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.yaml
 create mode 100644 scripts/tutorials/sim/create_fluid.py
 delete mode 100644 scripts/tutorials/sim/franka_dfsph_pour_demo.py
 delete mode 100644 tests/data/assets/test_task_assets.py
 delete mode 100644 tests/gym/envs/test_fluid_integration.py
 delete mode 100644 tests/gym/envs/test_fluid_replay_runtime.py
 delete mode 100644 tests/lab/scripts/test_run_fluid_task_program.py
 delete mode 100644 tests/sim/objects/test_fluid_batch_runtime.py
 delete mode 100644 tests/sim/objects/test_fluid_online_runtime.py

diff --git a/agent_context/MAP.yaml b/agent_context/MAP.yaml
index 51b65af44..43bda60df 100644
--- a/agent_context/MAP.yaml
+++ b/agent_context/MAP.yaml
@@ -45,7 +45,7 @@ topics:
   keywords: [BaseEnv, EmbodiedEnv, EnvCfg, register_env, list-task, run-env, target_control_frequency,
     sim_steps_per_control, step_dt, environment.component, embodiment.component, file-owned physics backend,
     physics_config, ControllerAction, EnvProfiler, startup_summary, dexsim_startup_info,
-    FluidReplayEnv, fluid Gym, 流体 Gym, ExpertTrajectoryCfg, joint_command_mode, position_velocity, qpos qvel, expert action schema,
+    ExpertTrajectoryCfg, joint_command_mode, position_velocity, qpos qvel, expert action schema,
     task_instruction, resolve_demo_instruction,
     show-task, task catalog, task gallery, 任务画廊, physical_objective, 物理目标]
   paths: [topics/env-framework/env-framework.md]
@@ -64,13 +64,11 @@ topics:
   - embodichain/lab/gym/envs/_startup_summary.py
   - embodichain/lab/gym/envs/embodied_env.py
   - embodichain/lab/gym/envs/expert_trajectory.py
-  - embodichain/lab/gym/envs/fluid_replay.py
-  - embodichain/lab/scripts/run_fluid_task_program.py
   - embodichain/lab/gym/envs/demo.py
   - embodichain/lab/gym/utils/profiler.py
   watch_paths: [embodichain/lab/gym/envs/base_env.py, embodichain/lab/gym/envs/embodied_env.py, embodichain/lab/gym/envs/expert_trajectory.py,
-    embodichain/lab/gym/envs/demo.py, embodichain/lab/gym/envs/fluid_replay.py,
-    embodichain/lab/scripts/run_fluid_task_program.py, embodichain/lab/gym/utils/, embodichain/lab/gym/envs/task_program/, embodichain/cli/, embodichain/lab/scripts/run_env.py,
+    embodichain/lab/gym/envs/demo.py,
+    embodichain/lab/gym/utils/, embodichain/lab/gym/envs/task_program/, embodichain/cli/, embodichain/lab/scripts/run_env.py,
     embodichain_tasks/, tests/gym/envs/, embodichain/lab/gym/envs/objectives/,
     embodichain/lab/scripts/evaluate_task_objective.py, tests/lab/scripts/test_evaluate_task_objective.py]
   related_topics: [simulation-system, manager-functor, sim-visualization, task-programs, data-pipeline]
@@ -378,7 +376,7 @@ topics:
   title: Data Assets
   aliases: [data assets, data path, asset download, 数据资产, 资产下载, 数据路径]
   keywords: [get_data_path, get_data_class, EmbodiChainDataset, EMBODICHAIN_DATA_ROOT, EMBODICHAIN_DATASET_ROOT,
-    CATEGORY_MODULES, download lock, get_registry, task_assets, PourCupAssets, PourCupDemoData]
+    CATEGORY_MODULES, download lock, get_registry]
   paths: [topics/data-assets/data-assets.md]
   source_of_truth:
   - embodichain/data/dataset.py
diff --git a/agent_context/topics/data-assets/data-assets.md b/agent_context/topics/data-assets/data-assets.md
index dec418a1a..40233e576 100644
--- a/agent_context/topics/data-assets/data-assets.md
+++ b/agent_context/topics/data-assets/data-assets.md
@@ -35,9 +35,6 @@ base URL for asset archives (default: the hf-mirror.com copy of
 The final nested asset path is not independently verified. A successful lookup
 can trigger download/extraction and still name a nonexistent nested file.
 Inspect `get_data_class()` and package imports when a newly added class is invisible.
-Gym `config_to_cfg()` applies the same resolver to file-backed `shape` and
-`visual_shape` meshes in backgrounds, rigid objects and rigid-object groups.
-An independent visual mesh must resolve through the cache before native creation.
 
 ## Download and registry boundaries
 
@@ -49,11 +46,7 @@ receive the project's locking/repair behavior.
 
 The CLI imports the fixed `CATEGORY_MODULES` and finds `DownloadDataset`
 subclasses defined in each module. Adding an asset module alone does not add a
-CLI category. The `task` category exposes `assets/task_assets.py`: `PourCupAssets`
-provides runtime meshes and `PourCupDemoData` provides recorded demo inputs and
-evidence. They use `task_assets/<ClassName>.zip`, honor the configured prefix
-with a Hub fallback, and reuse the shared MD5-checked cache. Task configurations
-use class-prefixed paths; demo/media files are not wheel resources. `get_registry()` also appends public classes from
+CLI category. `get_registry()` also appends public classes from
 `assets/locomotion_assets.py` to the robot category and excludes underscore-prefixed
 helper classes. These seven classes use `robot_assets/<ClassName>.zip` on the
 configured remote sources, with the model entry at the ZIP root.
diff --git a/agent_context/topics/env-framework/env-framework.md b/agent_context/topics/env-framework/env-framework.md
index c2d2ad5e0..e8995ecea 100644
--- a/agent_context/topics/env-framework/env-framework.md
+++ b/agent_context/topics/env-framework/env-framework.md
@@ -27,8 +27,6 @@ Lightweight RL environments have a separate owner in
 
 Read [configuration and registration](configuration.md) for task-first layout,
 component ownership, path resolution, config-owned IDs, and task listing.
-Read [fluid Gym integration](fluid-replay.md) for online Task Program execution
-with rigid/SPH dynamics, fluid configuration and coordinate conventions.
 Read [execution](execution.md) for hooks, bridge acceptance, reset ordering,
 wrappers, and replay. Read [profiling](profiling.md) only for instrumentation.
 
diff --git a/agent_context/topics/env-framework/fluid-replay.md b/agent_context/topics/env-framework/fluid-replay.md
deleted file mode 100644
index 7e1a69182..000000000
--- a/agent_context/topics/env-framework/fluid-replay.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Fluid Gym integration
-
-`EmbodiedEnvCfg.fluid_object` owns fixed-count fluid declarations. Physical
-components own these alongside rigid entities; `config_to_cfg()` resolves
-fluid point files and explicit relative rigid/visual mesh paths against the
-owning component. Spawn rigid references are resolved before fluid binding.
-The default pour deployment generates its fill from a local revolved profile;
-no particle asset file is required. Pouring meshes use `PourCupAssets/...`
-references resolved through the [data asset cache](../data-assets/data-assets.md),
-so installed task packages carry configuration without mesh payloads. The runner's spacing override resamples the
-same volume recipe and rejects fixed point imports. It scales explicit render
-radius, mesh cell size and reconstruction kernel radius by the spacing ratio,
-keeping the surface reconstruction consistent with the new particle density. Fill definitions and
-in-memory template ownership are documented under
-[simulation fluids](../simulation-system/fluids.md).
-
-`managers/observations.py` provides particle state, summary and volume-in-region
-observations. Particle arrays are world-frame; rigid `get_local_pose()` is
-arena-local. Region measurement and `events.reset_fluid_in_container()` add
-arena offsets before comparing or placing particles. Region volume is particle
-count times nominal spacing cubed, not a reconstructed-surface measurement.
-
-## Online Task Program workflow
-
-`lab/scripts/run_fluid_task_program.py` runs the configured pour-cup deployment
-through ordinary `EmbodiedEnv` and `execute_demo_episode()`. The environment
-component selects DexSim SPH with an existing MuJoCo configuration nested under
-`rigid_solver_cfg`. Robot drive targets, dynamic grasp contacts and fluid state
-advance in one World, with no recorded trajectory input. SPH reads live rigid
-boundaries; fluid reaction forces are not fed back into the rigid solver.
-The fluid program measures cup lift and alignment, transports the held cup
-to a receiver-relative pour pose through the existing MoveHeldObject lowerer,
-then pours and returns it. The standalone Sim tutorial uses the same physical
-component and relative pour target with freshly planned Atomic Action targets.
-
-Program completion and physical qualification are separate: the runner measures
-cup lift, maximum tilt, final return error and received volume. Projected effect
-assurance alone does not prove grasp or pouring success. Optional trajectory
-recording stores measured online state for inspection. `--seconds` qualifies
-only a bounded fragment, not completion of the task.
-
-`FluidReplayEnv` remains an explicit diagnostic utility for accepted historical
-recordings; it is not the default deployment or task runner. It requires a
-separate kinematic configuration, one Newton substep and fixed-root source
-states, and rejects action managers and policy dataset/trajectory recording.
-
-## Rendering and recording
-
-The pouring environment and runner select `fast-rt`. Hybrid is rejected for
-native fluid rendering: OptiX-only liquid geometry is absent from its Vulkan
-primary surface. Particle-state and video-decode checks alone do not qualify
-liquid visibility; use the image-based fluid regression linked below. The
-default record view focuses on the cup and receiver; the mesh kernel radius
-is 1.5 times the particle spacing to retain a connected surface after settling.
-Its interval `record_camera` event uses the existing `record_camera_data`
-functor with `per_env=True` and `fps=None`, recording every environment view at
-control frequency. The runner only redirects the output directory and sets
-the recording limit to the resolved environment count. It explicitly commits
-at normal completion before close, which otherwise discards pending frames.
-Selected-row recording and camera consumption are owned by the
-[recording functor](../manager-functor/manager-functor.md#camera-recording).
-NoRender qualification removes the recorder before constructing the scene.
-
-Production configuration/measurement checks are in
-`tests/gym/envs/test_fluid_integration.py`; live isolation checks are in
-`tests/sim/objects/test_fluid_batch_runtime.py` and
-`tests/sim/objects/test_fluid_online_runtime.py`; actual primary-surface visibility
-is checked by `tests/sim/objects/test_fluid_visibility.py`. Generated trajectories/videos
-are local qualification evidence, not packaged task assets.
diff --git a/agent_context/topics/manager-functor/manager-functor.md b/agent_context/topics/manager-functor/manager-functor.md
index 5245ebd73..a17474895 100644
--- a/agent_context/topics/manager-functor/manager-functor.md
+++ b/agent_context/topics/manager-functor/manager-functor.md
@@ -9,7 +9,6 @@
 | Observation manager + built-in functors | `managers/observation_manager.py`, `managers/observations.py` |
 | Reward manager + built-in functors | `managers/reward_manager.py`, `managers/rewards.py` |
 | Event manager + built-in functors | `managers/event_manager.py`, `managers/events.py` |
-| Episode camera recording events | `managers/record.py` |
 | Action manager + built-in terms | `managers/action_manager.py`, `managers/actions.py` |
 | Dataset manager + built-in recorders | `managers/dataset_manager.py`, `managers/datasets.py`, `managers/async_datasets.py` |
 | Randomization functors (event sub-type) | `managers/randomization/` (spatial, visual, physics, geometry) |
@@ -107,22 +106,6 @@ Event and observation calls have per-functor profiling through
 `ManagerBase._call_functor()`; reward/action/dataset calls do not yet use that
 helper. See [profiling](../env-framework/profiling.md).
 
-### Camera recording
-
-`record_camera_data` is an interval event, independent of policy dataset
-recording. It renders its camera group before reading the buffers because
-interval events precede observation rendering. Views use arena-relative camera
-extrinsics. `per_env=True` retains separate owned CPU frames and episode counters.
-It captures only the event's selected `env_ids` within `max_env_num`, preserving
-their original arena IDs; `None` captures all views within that limit. Selected
-save/discard only touches those rows. The default remains a tiled video.
-`fps=None` follows control frequency for an every-step recording event.
-
-Reset commits or discards frames via `save_and_clear` / `discard_and_clear`.
-Close discards uncommitted frames, so a host ending without reset must explicitly
-commit its last episode. Source and regression tests live in `record.py` and
-`tests/gym/envs/managers/test_record.py`.
-
 ## Change sites and validation
 
 Change the selected manager's dispatch for scheduling changes; change its
diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
index 80a3df4ed..877914348 100644
--- a/agent_context/topics/simulation-system/fluids.md
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -1,78 +1,69 @@
 # Initial-fill fluids
 
-`cfg/fluid.py` owns source, object and render configuration. Numerical solver
-configuration remains DexSim `SPHSolverCfg`, accepted directly or through the
-existing `NewtonPhysicsCfg.solver_cfg` mapping with `solver_type: sph`.
+`cfg/fluid.py` owns source, object and render configuration. Numerical settings
+remain DexSim `SPHSolverCfg`, accepted directly or through
+`NewtonPhysicsCfg.solver_cfg` with `solver_type: sph`.
 `SimulationManager.add_fluid_object()` declares through SpawnScene and binds
 `objects/fluid.py` in place at `prepare()`.
 
-Fluids require Newton CUDA without gradients. Multiple environments require the
-native SPH world-isolation and selected-reset capability flags; older runtimes
-remain restricted to one environment. Fixed particle counts and shared
-density/spacing are required.
-The prepared particle scene has fixed topology: adding, replacing or removing
-rigid assets also requires a new scene. Arena spacing does not prove isolation:
-native kernels filter neighbors and boundaries by world and keep convergence
-statistics per world. State writes clear only selected solver caches through
-canonical reset masks, preserving shared boundary history.
+## Runtime requirements
 
-Fluid state uses world-frame `(instances, particles, 3|6)` Torch snapshots.
-Writes update Scene particle batches; resetting transforms a saved local
-particle template or restores the immutable initial state. Render topology is
-reconstructed and is not inherited from volume/surface deformables.
+The foundation supports the published `dexsim_engine==0.5.1rc1` artifact
+(`56c12415053617cb0ffad6881ed57a45541a6da6`) with Newton CUDA, no gradients,
+one environment and fixed particle topology. Its SPH solver has no world
+isolation or selected-world solver reset. Requests for multiple environments
+fail before declaration unless the loaded solver explicitly advertises both
+capabilities. Arena spacing alone does not establish isolation.
+
+The release provides SPH/DFSPH and particle/surface rendering. It does not
+provide `SPHSolverCfg.rigid_solver_cfg`, `rigid_solver_substeps`, or DexUni's
+`sph_cfg` prototype. Online robot pouring, batch fluid tasks, Gym declarations
+and replay belong to the dependent task layer and require their respective
+DexSim development implementations. Basic fluid use does not require them.
 
-An opt-in `SPHSolverCfg.rigid_solver_cfg` composes the existing MuJoCo solver
-with SPH using Newton-owned views on one model/clock. MuJoCo owns rigid bodies
-and joints; SPH owns particles and reads live body proxies. Rigid microsteps
-refine contact integration without adding simulation time. This is online
-one-way coupling; there is no fluid reaction feedback. `RigidObjectCfg` may
-provide `particle_collision_shape`, an SDF mesh on the same body, so rigid
-contact hulls and a hollow fluid boundary coexist without extra mass or a
-pose follower. The native adapter owns particle-only SDF cooking and mass
-exclusion. Selected fluid state writes invalidate SPH caches without clearing
-the rigid solver history.
+## State and sampling
 
-Kinematic rigid boundaries can be written with `set_kinematic_state()` or
-driven before each solver substep using `register_kinematic_rigid_trajectory()`.
-Registered host controls disable graph replay and rewind selected clocks on
-object reset.
+Fluid state uses world-frame `(instances, particles, 3|6)` Torch snapshots.
+Writes use Scene particle batches, invalidate captured graphs and reset
+supported solver history. The legacy single-world solver rebaselines optional
+SDF boundary pose history. Reset restores the immutable initial state or
+transforms a saved local template without stepping the world. Explicit state
+writes do not refresh density diagnostics until the next SPH step.
 
-A spawn reference is a declared rigid body's initial pose, not a persistent
-attachment. The source may be an explicit point import, a primitive, a capped
-Z-axis revolved profile, or a closed fill-volume mesh. Profiles contain ordered
-`(local Z, radius)` pairs. `fill_level` clips the source's full Z extent before
-sampling; solid-wall clearance does not inset that free-surface plane. Mesh
-volume sources must describe closed cavities, not cup walls.
+A spawn reference resolves a declared rigid body's initial pose once; it is
+not a persistent attachment. Sources include explicit points/NPZ, primitives,
+capped Z-axis revolved profiles and closed fill-volume meshes. Profile points
+are ordered `(local Z, radius)` pairs. `fill_level` clips the full source Z
+extent before sampling; clearance does not inset that free surface. Mesh
+sources describe closed cavities, rather than container walls. Point imports
+reject volume-only profile/fill-level controls.
 
 Sampling math belongs to `compute/geometry/particles.py`, independent of lab.
-The manager samples once at declaration and retains the local template in
-memory; replica binding and selected reset never reopen or regenerate a source.
-Changing spacing or fill level before construction regenerates the point count;
-prepared topology remains fixed. Points/NPZ imports already contain fixed
-sampling and reject volume-only profile/fill-level controls. The default pour
-scene uses a profile source with no particle file or disk cache. Gym reset restores/refills selected rows without advancing the shared world;
-normal approach actions provide physical settling. The standalone Sim host
-resets configured robot/object state before a short initialization warmup.
+The manager samples once at declaration and retains the local template;
+binding and reset do not reopen source files. Particle count is fixed after
+preparation, and all fluids share density and spacing. Adding, replacing or
+removing rigid assets after preparing a particle scene requires a new scene.
+
+## Rendering and validation
+
+Native fluids require FastRT (`fast-rt`) or OfflineRT (`rt`). Hybrid's Vulkan
+primary surface lacks the OptiX liquid geometry, so the manager rejects Hybrid
+before sampling or declaration, including an already initialized Hybrid
+process. Use `no-render` for physics only; fluid mode `none` requires it.
+Display clipping changes only a render copy. Viser consumes detached particle
+point clouds. NoRender needs no optical material.
+
+`scripts/tutorials/sim/create_fluid.py` demonstrates procedural box sampling,
+five primitive container walls, prepare, step, state reads and reset with one
+environment. It needs no robot, task configuration or downloaded assets:
 
-Native fluid geometry requires FastRT (`fast-rt`) or OfflineRT (`rt`). The
-manager rejects Hybrid in both the resolved configuration and the initialized
-process renderer before sampling, material creation or declaration. The gate
-covers every fluid render mode: requesting FastRT in an existing Hybrid process
-cannot bypass it. Start a fresh process to change the initialized renderer;
-pure physics runs use NoRender. The current bridge creates OptiX-only instances,
-absent from Hybrid's Vulkan primary surface.
-Validate camera pixels with visible and hidden fluid, not only particle counts.
-Native rendering is published through the existing Newton render bridge.
-Display clipping acts only on copied positions; Viser consumes detached point
-clouds. NoRender fluid mode does not require optical materials. Ordinary rigid
-objects may supply a separate `visual_shape`; SDF cooking stays on the collision
-shape or the separate particle boundary. Explicit `force_sdf=False` permits pre-baked mesh SDFs.
+```bash
+python scripts/tutorials/sim/create_fluid.py --headless --renderer no-render
+```
 
-Focused checks are `tests/sim/test_fluid_cfg.py`,
-`tests/sim/objects/test_fluid_object.py` and the serial CUDA lifecycle test
-`tests/sim/objects/test_fluid_runtime.py` and `test_fluid_batch_runtime.py`.
-`tests/sim/objects/test_fluid_visibility.py` checks real camera pixels after
-stepping and direct state writes, including display clipping.
-For YAML declarations, refill observations and online Task Program execution, see
-[fluid Gym integration](../env-framework/fluid-replay.md). The pouring tutorial is an end-to-end
-qualification workload, not a unit-test import target.
+Focused checks are `tests/compute/test_particles.py`,
+`tests/sim/test_fluid_cfg.py`, `tests/sim/objects/test_fluid_object.py` and
+`tests/visualization/test_scene_exporter.py`. Serial CUDA lifecycle coverage is
+`tests/sim/objects/test_fluid_runtime.py`; `test_fluid_visibility.py` checks
+camera pixels after stepping and direct state writes. Tutorial checks remain
+syntax/CLI smoke checks rather than tutorial import tests.
diff --git a/docs/source/api_reference/embodichain/embodichain.lab.gym.rst b/docs/source/api_reference/embodichain/embodichain.lab.gym.rst
index cb372c9dc..9d2ceb04f 100644
--- a/docs/source/api_reference/embodichain/embodichain.lab.gym.rst
+++ b/docs/source/api_reference/embodichain/embodichain.lab.gym.rst
@@ -64,7 +64,7 @@ Embodied Environment Classes
 
 .. autoclass:: EmbodiedEnvCfg
     :members:
-    :exclude-members: __init__, class_type, fluid_object
+    :exclude-members: __init__, class_type
 
     Configuration class for embodied environments with extended settings for lighting, observation management,
     and advanced simulation features.
diff --git a/docs/source/api_reference/public_api.rst b/docs/source/api_reference/public_api.rst
index b2814e2a3..4a70546ff 100644
--- a/docs/source/api_reference/public_api.rst
+++ b/docs/source/api_reference/public_api.rst
@@ -23,15 +23,15 @@ decoding and compiling the constrained Task Program schema surface.
    decode_mllm_task_program
 
 embodichain.compute.geometry.particles
------------------------------------------
-
-Deterministic initial-fill volume sampling without simulation dependencies.
+--------------------------------------
 
-.. currentmodule:: embodichain.compute.geometry.particles
+Deterministic lattice sampling for primitive, revolved and closed mesh fill volumes.
 
 .. autosummary::
 
-   sample_volume_particles
+   embodichain.compute.geometry.particles.sample_volume_particles
+
+.. autofunction:: embodichain.compute.geometry.particles.sample_volume_particles
 
 embodichain.data.assets.demo_assets
 -----------------------------------
@@ -83,25 +83,6 @@ embodichain.data.assets.solver_assets
 
    download_neural_ik_checkpoint
 
-embodichain.data.assets.task_assets
------------------------------------
-
-Task mesh bundles and reference demonstration data downloaded through the shared
-asset cache. Runtime meshes and demo recordings are separate downloads so task
-initialization does not require video assets.
-
-.. currentmodule:: embodichain.data.assets.task_assets
-
-.. autosummary::
-   :nosignatures:
-
-   PourCupAssets
-   PourCupDemoData
-
-.. autoclass:: PourCupAssets
-
-.. autoclass:: PourCupDemoData
-
 embodichain.data_pipeline.depth_video
 -------------------------------------
 
@@ -373,29 +354,35 @@ embodichain.lab.gym.envs.embodied_env
    EmbodiedEnv
 
 embodichain.lab.sim.cfg.fluid
---------------------------------
-
-Fixed-count fluid source and render configuration. Numerical solver parameters remain owned by DexSim SPHSolverCfg.
+-----------------------------
 
-.. currentmodule:: embodichain.lab.sim.cfg.fluid
+Fixed-count fluid sources, physical properties and native particle or surface display.
 
 .. autosummary::
 
-   FluidSpawnCfg
-   FluidRenderCfg
-   FluidObjectCfg
+   embodichain.lab.sim.cfg.fluid.FluidSpawnCfg
+   embodichain.lab.sim.cfg.fluid.FluidRenderCfg
+   embodichain.lab.sim.cfg.fluid.FluidObjectCfg
 
-embodichain.lab.sim.objects.fluid
-------------------------------------
+.. autoclass:: embodichain.lab.sim.cfg.fluid.FluidSpawnCfg
 
-Scene-bound fluid state with detached particle snapshots and explicit state writes. Fluid rendering has no fixed mesh-connectivity contract.
+.. autoclass:: embodichain.lab.sim.cfg.fluid.FluidRenderCfg
 
-.. currentmodule:: embodichain.lab.sim.objects.fluid
+.. autoclass:: embodichain.lab.sim.cfg.fluid.FluidObjectCfg
+
+embodichain.lab.sim.objects.fluid
+---------------------------------
+
+Scene-owned fluid state snapshots, explicit writes and initial-state restoration.
 
 .. autosummary::
 
-   FluidObject
-   FluidObjectData
+   embodichain.lab.sim.objects.fluid.FluidObject
+   embodichain.lab.sim.objects.fluid.FluidObjectData
+
+.. autoclass:: embodichain.lab.sim.objects.fluid.FluidObject
+
+.. autoclass:: embodichain.lab.sim.objects.fluid.FluidObjectData
 
 embodichain.lab.task_program.integrations.simulation.handover
 -------------------------------------------------------------
@@ -406,22 +393,6 @@ embodichain.lab.task_program.integrations.simulation.handover
 
    ConfiguredHandOverPoseProvider
 
-embodichain.lab.gym.envs.fluid_replay
--------------------------------------
-
-.. currentmodule:: embodichain.lab.gym.envs.fluid_replay
-
-One-way evaluation of qualified Task Program recordings with live SPH. Each
-row has an independent replay clock and reset. Policy action recording is
-unsupported because the measured source states drive motion.
-
-.. autosummary::
-
-   FluidReplayEnv
-
-.. autoclass:: FluidReplayEnv
-   :members:
-
 embodichain.lab.gym.envs.managers.action_manager
 ------------------------------------------------
 
@@ -662,21 +633,6 @@ embodichain.lab.scripts.run_env
    main
    preview
 
-embodichain.lab.scripts.run_fluid_task_program
-----------------------------------------------
-
-.. currentmodule:: embodichain.lab.scripts.run_fluid_task_program
-
-Generate and qualify the pour-cup Task Program in MuJoCo, then run fluid Gym
-replay in a separate world. The source trajectory and fluid metrics have
-separate acceptance records.
-
-.. autosummary::
-
-   main
-
-.. autofunction:: main
-
 embodichain.lab.sim
 -------------------
 
@@ -2480,20 +2436,6 @@ kernel policy; callers log and apply that seed to their own generators.
 .. autofunction:: resolve_seed
 
 
-embodichain_tasks.manipulation.tableware.pour_cup
--------------------------------------------------
-
-.. currentmodule:: embodichain_tasks.manipulation.tableware.pour_cup
-
-The registered Franka pouring replay deployment requires a previously
-qualified Task Program trajectory passed to the environment constructor.
-
-.. autosummary::
-
-   PourCupFluidReplayEnv
-
-.. autoclass:: PourCupFluidReplayEnv
-
 .. toctree::
    :hidden:
 
diff --git a/embodichain/data/assets/__init__.py b/embodichain/data/assets/__init__.py
index ede40878e..c6c7cb00a 100644
--- a/embodichain/data/assets/__init__.py
+++ b/embodichain/data/assets/__init__.py
@@ -14,7 +14,7 @@
 # limitations under the License.
 # ----------------------------------------------------------------------------
 
-"""Preset configuration objects for scene assets (robots, end-effectors, objects, materials, sensors, planners, solvers, demo scenes and task bundles) ready to reference from task configs."""
+"""Preset configuration objects for scene assets (robots, end-effectors, objects, materials, sensors, planners, solvers, demo scenes) ready to reference from task configs."""
 
 from __future__ import annotations
 
@@ -29,5 +29,3 @@
 from .planner_assets import *
 
 from .locomotion_assets import *
-
-from .task_assets import *
diff --git a/embodichain/data/assets/task_assets.py b/embodichain/data/assets/task_assets.py
deleted file mode 100644
index d27958c7f..000000000
--- a/embodichain/data/assets/task_assets.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-
-"""Downloadable task meshes and reference demonstration data."""
-
-from __future__ import annotations
-
-import open3d as o3d
-
-from embodichain.data.constants import (
-    EMBODICHAIN_DEFAULT_DATA_ROOT,
-    EMBODICHAIN_DOWNLOAD_PREFIX,
-)
-from embodichain.data.dataset import EmbodiChainDataset
-
-__all__ = ["PourCupAssets", "PourCupDemoData"]
-
-_HUB_PREFIX = (
-    "https://huggingface.co/datasets/DexForceAI/embodichain_data/resolve/main/"
-)
-
-
-class _TaskAsset(EmbodiChainDataset):
-    """Download one task archive through the shared locked dataset cache."""
-
-    archive_md5: str
-
-    def __init__(self, data_root: str | None = None) -> None:
-        """Initialize and extract the task bundle.
-
-        Args:
-            data_root: Optional cache root overriding the EmbodiChain default.
-        """
-        prefix = type(self).__name__
-        filename = f"task_assets/{prefix}.zip"
-        urls = [f"{EMBODICHAIN_DOWNLOAD_PREFIX}{filename}"]
-        if EMBODICHAIN_DOWNLOAD_PREFIX != _HUB_PREFIX:
-            urls.append(f"{_HUB_PREFIX}{filename}")
-        descriptor = o3d.data.DataDescriptor(urls, self.archive_md5)
-        super().__init__(
-            prefix,
-            descriptor,
-            EMBODICHAIN_DEFAULT_DATA_ROOT if data_root is None else data_root,
-        )
-
-
-class PourCupAssets(_TaskAsset):
-    """Cup, fluid boundary, receiver, table and floor meshes for cup pouring.
-
-    Resolve individual meshes with ``get_data_path("PourCupAssets/cup.glb")``.
-    The bundle contains no initial-fluid particle file.
-    """
-
-    archive_md5 = "f97857b639189be4daf717a873ab84cf"
-
-
-class PourCupDemoData(_TaskAsset):
-    """Recorded pouring trajectory, videos and original validation evidence.
-
-    ``trajectory.pt`` is a historical MuJoCo recording for diagnostic replay.
-    Online pouring tutorials and tasks do not load it. ``particles_30.npz`` is a final reference snapshot and is not used
-    to initialize fluids. Resolve files through ``get_data_path()``.
-    """
-
-    archive_md5 = "8eb96cb756ac08efe403417f50ad2bf2"
diff --git a/embodichain/data/download.py b/embodichain/data/download.py
index cf29717a1..b7661d15f 100644
--- a/embodichain/data/download.py
+++ b/embodichain/data/download.py
@@ -58,7 +58,6 @@
     "obj": "embodichain.data.assets.obj_assets",
     "robot": "embodichain.data.assets.robot_assets",
     "scene": "embodichain.data.assets.scene_assets",
-    "task": "embodichain.data.assets.task_assets",
     "w1": "embodichain.data.assets.w1_assets",
 }
 
diff --git a/embodichain/lab/gym/envs/embodied_env.py b/embodichain/lab/gym/envs/embodied_env.py
index 3f3973072..1fbd6110b 100644
--- a/embodichain/lab/gym/envs/embodied_env.py
+++ b/embodichain/lab/gym/envs/embodied_env.py
@@ -31,8 +31,6 @@
 import numpy as np
 import gymnasium as gym
 
-from embodichain.lab.sim.cfg.fluid import FluidObjectCfg
-
 from dataclasses import MISSING
 from typing import (
     TYPE_CHECKING,
@@ -200,9 +198,6 @@ class EnvLightCfg:
 
     articulation: List[ArticulationCfg] = []
 
-    fluid_object: list[FluidObjectCfg] = []
-    """Fixed-count fluids owned by the physical environment component."""
-
     events: Union[object, None] = None
     """Event settings. Defaults to None, in which case no events are applied through the event manager.
 
@@ -2714,9 +2709,6 @@ def _setup_interactive_objects(self) -> None:
         for cfg in self.cfg.rigid_object_group:
             self.sim.add_rigid_object_group(cfg=cfg)
 
-        for cfg in self.cfg.fluid_object:
-            self.sim.add_fluid_object(cfg)
-
     def preview_sensor_data(
         self,
         name: str,
diff --git a/embodichain/lab/gym/envs/fluid_replay.py b/embodichain/lab/gym/envs/fluid_replay.py
deleted file mode 100644
index c814ca7bf..000000000
--- a/embodichain/lab/gym/envs/fluid_replay.py
+++ /dev/null
@@ -1,331 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-
-"""One-way fluid Gym replay of an accepted Task Program recording."""
-
-from __future__ import annotations
-
-from collections.abc import Mapping
-from copy import deepcopy
-from typing import Any
-import math
-import numpy as np
-import torch
-from scipy.interpolate import PchipInterpolator
-from scipy.spatial.transform import Rotation, Slerp
-
-from embodichain.lab.gym.envs.embodied_env import EmbodiedEnv, EmbodiedEnvCfg
-from embodichain.lab.gym.utils.gym_utils import load_trajectory
-from embodichain.lab.sim.cfg import NewtonPhysicsCfg
-
-__all__ = ["FluidReplayEnv"]
-
-
-class FluidReplayEnv(EmbodiedEnv):
-    """Replay measured rigid states while continuing to solve fluid physics.
-
-    The source must have been generated and accepted by Task Program. This is
-    a replay/evaluation environment: policy actions do not control the robot.
-    Gym reset/step, sensors and configured observation/reward managers still
-    execute normally. No rigid-fluid force feedback is modeled.
-
-    Args:
-        cfg: Physical environment configuration, including fluid declarations.
-        trajectory: Accepted source recording (path or loaded trajectory dict).
-        speed: Positive source-time multiplier.
-        hold_seconds: Initial and final stationary intervals in simulation time.
-        kwargs: Forwarded environment constructor options.
-    """
-
-    def __init__(
-        self,
-        cfg: EmbodiedEnvCfg,
-        *,
-        trajectory: str | dict,
-        speed: float = 2.0,
-        hold_seconds: float = 1.0,
-        **kwargs: Any,
-    ) -> None:
-        if (
-            not math.isfinite(speed)
-            or speed <= 0
-            or not math.isfinite(hold_seconds)
-            or hold_seconds < 0
-        ):
-            raise ValueError(
-                "Replay speed must be positive and hold_seconds nonnegative."
-            )
-        if cfg.record_trajectory or cfg.actions is not None:
-            raise ValueError(
-                "Fluid replay does not support action managers or trajectory/action recording."
-            )
-        if (
-            not cfg.filter_dataset_saving
-            and cfg.dataset is not None
-            and any(
-                not key.startswith("_")
-                for key in (
-                    cfg.dataset
-                    if isinstance(cfg.dataset, Mapping)
-                    else vars(cfg.dataset)
-                )
-            )
-        ):
-            raise ValueError(
-                "Fluid replay does not support policy dataset recording; record measured fluid diagnostics separately."
-            )
-        self._source = load_trajectory(
-            deepcopy(trajectory) if isinstance(trajectory, dict) else trajectory
-        )
-        meta = self._source["meta"]
-        qualification = meta.get("fluid_source", {})
-        if (
-            qualification.get("kind") != "task_program"
-            or qualification.get("accepted") is not True
-            or not isinstance(qualification.get("program_hash"), str)
-            or not qualification["program_hash"]
-        ):
-            raise ValueError(
-                "Fluid replay requires a Task Program recording with accepted fluid_source qualification."
-            )
-        if int(meta["num_envs"]) != 1:
-            raise ValueError(
-                "The initial fluid replay adapter broadcasts one qualified source row."
-            )
-        count = int(meta["lengths"][0])
-        dt = float(meta["dt"])
-        if count < 2 or not math.isfinite(dt) or dt <= 0:
-            raise ValueError(
-                "Replay needs at least two source frames at a finite positive cadence."
-            )
-        self._source_times = np.arange(count, dtype=float) * dt
-        self._speed = speed
-        self._hold_seconds = hold_seconds
-        self.replay_duration = 2 * hold_seconds + self._source_times[-1] / speed
-        cfg = deepcopy(cfg)
-        physics = cfg.sim_cfg.physics_cfg
-        solver = getattr(physics, "solver_cfg", None)
-        solver_type = (
-            solver.get("solver_type")
-            if isinstance(solver, dict)
-            else getattr(solver, "solver_type", None)
-        )
-        if not isinstance(physics, NewtonPhysicsCfg) or solver_type != "sph":
-            raise ValueError("Fluid replay requires Newton SPH.")
-        if cfg.sim_cfg.physics_cfg.num_substeps != 1:
-            raise ValueError(
-                "Fluid Gym replay requires num_substeps=1; choose physics_dt for boundary cadence."
-            )
-        states = self._source["states"]
-        joints = states["robot", "qpos"][0, :count].cpu().numpy()
-        self._joint_curve = PchipInterpolator(self._source_times, joints)
-        cfg.robot.init_qpos = joints[0].tolist()
-        roots = states["robot", "root_pose"][0, :count].cpu().numpy()
-        root_rotations = Rotation.from_quat(roots[:, 3:]).as_matrix()
-        if not np.allclose(roots[:, :3], roots[0, :3], atol=1e-5) or not np.allclose(
-            root_rotations, root_rotations[0], atol=1e-5
-        ):
-            raise ValueError(
-                "Fluid replay currently supports fixed-root robot recordings only."
-            )
-        root_pose = np.eye(4, dtype=np.float32)
-        root_pose[:3, :3] = root_rotations[0]
-        root_pose[:3, 3] = roots[0, :3]
-        cfg.robot.init_local_pose = root_pose
-
-        self._rigid_curves = {}
-        objects = {item.uid: item for item in cfg.background + cfg.rigid_object}
-        for uid in meta.get("rigid_object_uids", []):
-            if uid not in objects:
-                continue
-            if objects[uid].body_type != "kinematic":
-                raise ValueError(f"Replayed rigid object {uid!r} must be kinematic.")
-            poses = states["rigid_objects", uid, "pose"][0, :count].cpu().numpy()
-            self._rigid_curves[uid] = (
-                PchipInterpolator(self._source_times, poses[:, :3]),
-                Slerp(self._source_times, Rotation.from_quat(poses[:, 3:])),
-            )
-            pose = np.eye(4, dtype=np.float32)
-            pose[:3, :3] = Rotation.from_quat(poses[0, 3:]).as_matrix()
-            pose[:3, 3] = poses[0, :3]
-            objects[uid].init_local_pose = pose
-        if not self._rigid_curves:
-            raise ValueError("No recorded rigid objects match the replay scene.")
-        self._replay_time = np.zeros(cfg.num_envs, dtype=np.float64)
-        self._previous_rigid = {}
-        super().__init__(cfg, **kwargs)
-        try:
-            if joints.shape[1] != self.robot.dof:
-                raise ValueError(
-                    "Source robot DOF count does not match the replay embodiment."
-                )
-            if "active_joint_ids" in meta and list(meta["active_joint_ids"]) != list(
-                self.active_joint_ids
-            ):
-                raise ValueError(
-                    "Source active joint order does not match the replay embodiment."
-                )
-            if "joint_names" in meta:
-                expected = [self.robot.joint_names[i] for i in self.active_joint_ids]
-                if list(meta["joint_names"]) != expected:
-                    raise ValueError(
-                        "Source joint names do not match the replay embodiment."
-                    )
-        except Exception:
-            self._cleanup_failed_initialization()
-            raise
-        self._replay_no_auto_reset = True
-
-    def step(
-        self, action: Any, **kwargs: Any
-    ) -> tuple[Any, torch.Tensor, torch.Tensor, torch.Tensor, dict]:
-        """Advance replay; reset is required after every row is exhausted."""
-        if np.all(self._replay_time >= self.replay_duration):
-            raise RuntimeError(
-                "Fluid replay is exhausted; reset before stepping again."
-            )
-        return super().step(action, **kwargs)
-
-    def check_truncated(self, obs: Any, info: dict) -> torch.Tensor:
-        """Signal each row's replay exhaustion independently."""
-        return super().check_truncated(obs, info) | torch.as_tensor(
-            self._replay_time >= self.replay_duration, device=self.device
-        )
-
-    def _step_action(self, action: Any) -> Any:
-        """Preserve the Gym lifecycle while motion comes from recorded states."""
-        return action
-
-    def _apply_replay_state(self, ids: list[int], dt: float) -> None:
-        if not ids:
-            return
-        source_times = np.clip(
-            (self._replay_time[ids] - self._hold_seconds) * self._speed,
-            0.0,
-            self._source_times[-1],
-        )
-        qpos = torch.as_tensor(
-            self._joint_curve(source_times), dtype=torch.float32, device=self.device
-        )
-        self.robot.set_qpos(qpos, env_ids=ids, target=False)
-        active = (self._replay_time[ids] >= self._hold_seconds) & (
-            self._replay_time[ids]
-            < self._hold_seconds + self._source_times[-1] / self._speed
-        )
-        qvel = (
-            self._joint_curve.derivative()(source_times) * self._speed * active[:, None]
-        )
-        self.robot.set_qvel(
-            torch.as_tensor(qvel, dtype=torch.float32, device=self.device),
-            env_ids=ids,
-            target=False,
-        )
-        if not hasattr(self, "_replay_offsets"):
-            self._replay_offsets = self.sim.arena_offsets.detach().cpu().numpy().copy()
-        offsets = self._replay_offsets[ids]
-        for uid, (positions, rotations) in self._rigid_curves.items():
-            poses = np.repeat(np.eye(4, dtype=np.float32)[None], len(ids), axis=0)
-            poses[:, :3, :3] = rotations(source_times).as_matrix()
-            poses[:, :3, 3] = positions(source_times) + offsets
-            previous = self._previous_rigid.get(uid)
-            if previous is None:
-                previous = np.repeat(
-                    np.eye(4, dtype=np.float32)[None], self.num_envs, axis=0
-                )
-                self._previous_rigid[uid] = previous
-                previous[ids] = poses
-            old = previous[ids].copy()
-            linear = (
-                (poses[:, :3, 3] - old[:, :3, 3]) / dt
-                if dt > 0
-                else np.zeros((len(ids), 3))
-            )
-            angular = (
-                Rotation.from_matrix(
-                    poses[:, :3, :3] @ old[:, :3, :3].transpose(0, 2, 1)
-                ).as_rotvec()
-                / dt
-                if dt > 0
-                else np.zeros((len(ids), 3))
-            )
-            self.sim.get_rigid_object(uid).set_kinematic_state(
-                torch.from_numpy(poses),
-                torch.from_numpy(linear),
-                torch.from_numpy(angular),
-                env_ids=ids,
-            )
-            previous[ids] = poses
-
-    def _advance_physics(self) -> None:
-        sensors = [
-            sensor for sensor in self.sensors.values() if sensor.requires_substep_update
-        ]
-        for sensor in sensors:
-            sensor.begin_control_step()
-        ids = list(range(self.num_envs))
-        for _ in range(self.cfg.sim_steps_per_control):
-            self._apply_replay_state(ids, self.physics_dt)
-            self.sim.update(step=1, render_final_step=False)
-            self._replay_time += self.physics_dt
-            for sensor in sensors:
-                sensor.update_physics_step(self.physics_dt)
-
-    def _initialize_episode(self, env_ids=None, **kwargs: Any) -> None:
-        super()._initialize_episode(env_ids=env_ids, **kwargs)
-        ids = (
-            list(range(self.num_envs))
-            if env_ids is None
-            else torch.as_tensor(env_ids).cpu().tolist()
-        )
-        self._replay_time[ids] = 0.0
-        self._apply_replay_state(ids, 0.0)
-        # Reset-mode events may have randomized the container; refill only the
-        # selected rows from its measured world pose and the local fill template.
-        for cfg in self.cfg.fluid_object:
-            if cfg.spawn.reference_uid is None:
-                continue
-            from embodichain.lab.gym.envs.managers.cfg import SceneEntityCfg
-            from embodichain.lab.gym.envs.managers.events import (
-                reset_fluid_in_container,
-            )
-
-            reset_fluid_in_container(
-                self,
-                ids,
-                [
-                    SceneEntityCfg(uid=cfg.uid),
-                    SceneEntityCfg(uid=cfg.spawn.reference_uid),
-                ],
-            )
-
-    @property
-    def source_qualification(self) -> dict:
-        """Return independent Task Program provenance and source acceptance."""
-        return deepcopy(self._source["meta"]["fluid_source"])
-
-    def get_info(self, **kwargs: Any) -> dict:
-        """Return normal Gym info with row-local replay time and completion."""
-        info = super().get_info(**kwargs)
-        info["fluid_replay_time_s"] = torch.as_tensor(
-            self._replay_time.copy(), device=self.device
-        )
-        info["fluid_replay_finished"] = torch.as_tensor(
-            self._replay_time >= self.replay_duration, device=self.device
-        )
-        info["fluid_source_accepted"] = torch.ones(
-            self.num_envs, dtype=torch.bool, device=self.device
-        )
-        return info
diff --git a/embodichain/lab/gym/envs/managers/events.py b/embodichain/lab/gym/envs/managers/events.py
index 0416d90ec..2de46336d 100644
--- a/embodichain/lab/gym/envs/managers/events.py
+++ b/embodichain/lab/gym/envs/managers/events.py
@@ -56,7 +56,6 @@
 
 
 __all__ = [
-    "reset_fluid_in_container",
     "apply_expansion_profile_reset",
     "replace_assets_from_group",
     "prepare_extra_attr",
@@ -810,34 +809,3 @@ def remove_rigid_constraint(
         name: Base constraint name to remove.
     """
     env.sim.remove_rigid_constraint(name, env_ids=env_ids)
-
-
-def reset_fluid_in_container(
-    env: EmbodiedEnv,
-    env_ids: Sequence[int] | torch.Tensor,
-    entity_cfgs: list[SceneEntityCfg],
-) -> None:
-    """Refill selected rows from the declared local template after container reset.
-
-    Args:
-        env: Owning environment.
-        env_ids: Selected environment indices.
-        entity_cfgs: Fluid and rigid container selections, in that order.
-    """
-    from embodichain.lab.sim.spawn.descriptors import _pose_from_cfg
-    from embodichain.utils.math import quat_from_matrix
-
-    if len(entity_cfgs) != 2:
-        raise ValueError("Expected fluid and container selections.")
-    fluid = env.sim.get_fluid_object(entity_cfgs[0].uid)
-    container = env.sim.get_rigid_object(entity_cfgs[1].uid)
-    if fluid is None or container is None:
-        raise ValueError("Unknown fluid or container.")
-    ids = torch.as_tensor(env_ids, device=env.device, dtype=torch.long)
-    local = torch.as_tensor(
-        _pose_from_cfg(fluid.cfg), dtype=torch.float32, device=env.device
-    )
-    world = container.get_local_pose(to_matrix=True)[ids] @ local
-    world[:, :3, 3] += env.sim.arena_offsets[ids]
-    pose = torch.cat((world[:, :3, 3], quat_from_matrix(world[:, :3, :3])), dim=-1)
-    fluid.reset_to_pose(pose, ids)
diff --git a/embodichain/lab/gym/envs/managers/observations.py b/embodichain/lab/gym/envs/managers/observations.py
index 211f6c74a..45374c9ef 100644
--- a/embodichain/lab/gym/envs/managers/observations.py
+++ b/embodichain/lab/gym/envs/managers/observations.py
@@ -1179,134 +1179,3 @@ def __call__(
         self._cache[uid] = result.clone()
 
         return result
-
-
-def get_fluid_particle_state(
-    env: EmbodiedEnv, obs: EnvObs, entity_cfg: SceneEntityCfg
-) -> torch.Tensor:
-    """Return opt-in world-frame particle state ``(E, N, 6)``.
-
-    Args:
-        env: Owning environment.
-        obs: Current observations, unused.
-        entity_cfg: Fluid selection.
-
-    Returns:
-        Independent positions/velocities; no full-particle recording is enabled implicitly.
-    """
-    del obs
-    fluid = env.sim.get_fluid_object(entity_cfg.uid)
-    if fluid is None:
-        raise ValueError(f"Unknown fluid {entity_cfg.uid!r}.")
-    return fluid.data.particle_state_w
-
-
-def get_fluid_summary(
-    env: EmbodiedEnv, obs: EnvObs, entity_cfg: SceneEntityCfg
-) -> torch.Tensor:
-    """Return mass, nominal volume, mean speed, RMS speed and finite fraction.
-
-    Args:
-        env: Owning environment.
-        obs: Current observations, unused.
-        entity_cfg: Fluid selection.
-
-    Returns:
-        ``(E, 5)`` GPU tensor in kg, cubic metres and metres/second.
-    """
-    state = get_fluid_particle_state(env, obs, entity_cfg)
-    fluid = env.sim.get_fluid_object(entity_cfg.uid)
-    speeds = state[..., 3:].norm(dim=-1)
-    return torch.stack(
-        (
-            speeds.new_full((env.num_envs,), fluid.particle_mass * fluid.num_particles),
-            speeds.new_full((env.num_envs,), fluid.nominal_volume),
-            speeds.mean(-1),
-            speeds.square().mean(-1).sqrt(),
-            torch.isfinite(state).all(-1).float().mean(-1),
-        ),
-        dim=-1,
-    )
-
-
-def get_fluid_volume_in_region(
-    env: EmbodiedEnv,
-    obs: EnvObs,
-    entity_cfgs: list[SceneEntityCfg],
-    lower: list[float],
-    upper: list[float],
-    sphere_center: list[float] | None = None,
-    sphere_radius: float | None = None,
-) -> torch.Tensor:
-    """Measure nominal fluid volume inside a rigid-local region.
-
-    Args:
-        env: Owning environment.
-        obs: Current observations, unused.
-        entity_cfgs: Fluid followed by the rigid reference object.
-        lower: Inclusive local AABB lower corner, metres.
-        upper: Inclusive local AABB upper corner, metres.
-        sphere_center: Optional local sphere centre intersected with the AABB.
-        sphere_radius: Positive sphere radius, metres, paired with sphere_center.
-
-    Returns:
-        ``(E, 1)`` contained rest volume in cubic metres.
-    """
-    from embodichain.utils.math import matrix_from_quat
-    import math
-
-    if len(entity_cfgs) != 2:
-        raise ValueError(
-            "Region measurement requires fluid and rigid-reference selections."
-        )
-    fluid = env.sim.get_fluid_object(entity_cfgs[0].uid)
-    rigid = env.sim.get_rigid_object(entity_cfgs[1].uid)
-    if fluid is None or rigid is None:
-        raise ValueError("Unknown fluid or region reference.")
-    bounds = torch.as_tensor([lower, upper], dtype=torch.float32, device=env.device)
-    if (
-        bounds.shape != (2, 3)
-        or not torch.isfinite(bounds).all()
-        or not (bounds[1] > bounds[0]).all()
-    ):
-        raise ValueError("Region bounds must be finite increasing 3D corners.")
-    pose = rigid.get_local_pose()
-    origin_w = pose[:, :3] + env.sim.arena_offsets
-    local = torch.bmm(
-        fluid.data.particle_pos_w - origin_w[:, None], matrix_from_quat(pose[:, 3:])
-    )
-    inside = ((local >= bounds[0]) & (local <= bounds[1])).all(-1)
-    if (sphere_center is None) != (sphere_radius is None):
-        raise ValueError("sphere_center and sphere_radius must be supplied together.")
-    if sphere_center is not None:
-        center = torch.as_tensor(sphere_center, dtype=torch.float32, device=env.device)
-        if (
-            center.shape != (3,)
-            or not torch.isfinite(center).all()
-            or not math.isfinite(sphere_radius)
-            or sphere_radius <= 0
-        ):
-            raise ValueError("Sphere needs a finite centre and positive radius.")
-        inside &= (local - center).square().sum(-1) <= sphere_radius**2
-    return inside.sum(-1, keepdim=True).float() * fluid.cfg.particle_spacing**3
-
-
-__all__ = [
-    "get_object_pose",
-    "get_rigid_object_pose",
-    "get_object_body_scale",
-    "get_object_uid",
-    "get_rigid_object_velocity",
-    "normalize_robot_joint_data",
-    "get_sensor_pose_in_robot_frame",
-    "get_sensor_intrinsics",
-    "compute_semantic_mask",
-    "get_robot_eef_pose",
-    "target_position",
-    "compute_exteroception",
-    "get_rigid_object_physics_attributes",
-    "get_articulation_joint_drive",
-    "get_fluid_particle_state",
-    "get_fluid_summary",
-    "get_fluid_volume_in_region",
-]
diff --git a/embodichain/lab/gym/envs/managers/record.py b/embodichain/lab/gym/envs/managers/record.py
index 9038c6de9..6f89c79b6 100644
--- a/embodichain/lab/gym/envs/managers/record.py
+++ b/embodichain/lab/gym/envs/managers/record.py
@@ -17,7 +17,6 @@
 from __future__ import annotations
 
 import os
-import math
 import random
 import threading
 from collections import deque
@@ -37,19 +36,23 @@
 
 
 class record_camera_data(Functor):
-    """Capture a third-person camera through an interval event.
-
-    By default, views are tiled into one video at 20 fps. Set ``per_env=True``
-    to save one video per environment, with independent episode counters and
-    selected-row capture/save/discard. ``fps=None`` uses the environment's control
-    frequency; use an explicit fps when capturing less often than every step.
-
-    Frames are committed by :meth:`save_and_clear` before reset. Closing the
-    environment discards uncommitted frames, so callers ending without reset
-    must explicitly commit the last recording.
+    """Record camera data in the environment. The camera is usually setup with third-person view, and
+    is used to record the scene during the episode. It is helpful for debugging and visualization.
+
+    Note:
+        Currently, the functor is implemented in `interval' mode such that, it can only save the
+        recorded frames when in :meth:`env.step()` function call. For example:
+        ```python
+        env.step()
+        # perform multiple steps in the same episode
+        env.reset()
+        env.step()  # the video of the first episode will be saved here.
+        ```
+        The final episode frames will not be saved in the current implementation.
+        We may improve it in the future.
     """
 
-    def __init__(self, cfg: FunctorCfg, env: EmbodiedEnv) -> None:
+    def __init__(self, cfg: FunctorCfg, env: EmbodiedEnv):
         """Initialize the functor.
 
         Args:
@@ -61,14 +64,6 @@ def __init__(self, cfg: FunctorCfg, env: EmbodiedEnv) -> None:
         """
         super().__init__(cfg, env)
 
-        self._per_env = cfg.params.get("per_env", False)
-        fps = cfg.params.get("fps", 20.0)
-        self._fps = float(env.control_frequency if fps is None else fps)
-        if not math.isfinite(self._fps) or self._fps <= 0:
-            raise ValueError("Recording fps must be finite and positive.")
-        self._env_frames: dict[int, list[np.ndarray]] = {}
-        self._env_episode_ids: dict[int, int] = {}
-
         # extract the used quantities (to enable type-hinting)
         self._name = cfg.params.get("name", "default")
         resolution = cfg.params.get("resolution", (640, 480))
@@ -149,58 +144,19 @@ def save_and_clear(self, env_ids: Union[torch.Tensor, None] = None) -> None:
         frames are saved before the episode is reset. This avoids the issue where the
         final episode's frames are lost because the save previously relied on detecting
         a reset inside :meth:`__call__`.
-
-        Args:
-            env_ids: Selected rows for separate videos, or all rows when None.
-                Tiled recording saves the whole shared frame buffer.
         """
         self._ensure_open()
-        if getattr(self, "_per_env", False):
-            for env_id in self._selected_recorded_envs(env_ids):
-                frames = self._env_frames[env_id]
-                if not frames:
-                    continue
-                episode = self._env_episode_ids.get(env_id, 0)
-                images_to_video(
-                    frames,
-                    self._save_path,
-                    f"episode_{episode}_{self._name}_env{env_id}",
-                    fps=self._fps,
-                )
-                self._env_episode_ids[env_id] = episode + 1
-                self._env_frames[env_id] = []
-            return
         if len(self._frames) > 0:
             video_name = f"episode_{self._current_episode}_{self._name}"
-            images_to_video(
-                self._frames, self._save_path, video_name, fps=getattr(self, "_fps", 20)
-            )
+            images_to_video(self._frames, self._save_path, video_name, fps=20)
 
             self._current_episode += 1
             self._frames = []
 
     def discard_and_clear(self, env_ids: Union[torch.Tensor, None] = None) -> None:
-        """Discard frames without creating an episode video.
-
-        Args:
-            env_ids: Selected rows for separate videos, or all rows when None.
-                Tiled recording discards the whole shared frame buffer.
-        """
-        if getattr(self, "_per_env", False):
-            for env_id in self._selected_recorded_envs(env_ids):
-                self._env_frames[env_id] = []
-            return
+        """Discard recorded frames without creating an episode video."""
         self._frames = []
 
-    def _selected_recorded_envs(self, env_ids: torch.Tensor | None) -> list[int]:
-        if env_ids is None:
-            return list(self._env_frames)
-        return [
-            int(env_id)
-            for env_id in torch.as_tensor(env_ids).reshape(-1).cpu().tolist()
-            if int(env_id) in self._env_frames
-        ]
-
     def finalize(self) -> None:
         """Discard uncommitted frames and close the recorder exactly once."""
         if not hasattr(self, "_finalize_lock"):
@@ -232,49 +188,14 @@ def __call__(
         ),
         max_env_num: int = 16,
         save_path: str = "./outputs/videos",
-        per_env: bool = False,
-        fps: float | None = 20.0,
-    ) -> None:
-        """Capture up to ``max_env_num`` arena-relative views at this step.
-
-        Args:
-            env: Owning environment.
-            env_ids: Rows to capture in per-environment mode, or all camera rows
-                when None. Tiled recording always captures all camera rows.
-            name: Camera name, configured at initialization.
-            resolution: Image width and height, configured at initialization.
-            eye: Arena-relative camera position.
-            target: Arena-relative look-at position.
-            up: Camera up direction.
-            intrinsics: Pinhole intrinsics.
-            max_env_num: Maximum number of environment views to capture.
-            save_path: Output directory, configured at initialization.
-            per_env: Whether to save separate environment videos.
-            fps: Video cadence, or None for the environment control frequency.
-        """
+    ):
         self._ensure_open()
-        # Interval events run before observation rendering. Render this group
-        # from the current physics state before reading its reusable buffers.
-        env.sim.render_camera_group([self.camera.group_id])
         self.camera.update(fetch_only=True)
         data = self.camera.get_data()
         rgb = data["color"]
 
-        num_frames = min(rgb.shape[0], max_env_num)
+        num_frames = max(rgb.shape[0], max_env_num)
         rgb = rgb[:num_frames]
-        if self._per_env:
-            selected_env_ids = list(range(num_frames))
-            frames = rgb[..., :3]
-            if env_ids is not None:
-                selected_env_ids = [
-                    int(env_id)
-                    for env_id in torch.as_tensor(env_ids).reshape(-1).cpu().tolist()
-                    if 0 <= int(env_id) < num_frames
-                ]
-                frames = frames[selected_env_ids]
-            for env_id, frame in zip(selected_env_ids, frames.detach().cpu().numpy()):
-                self._env_frames.setdefault(env_id, []).append(frame.copy())
-            return
         rgb = self._draw_frames_into_one_image(rgb)[..., :3].cpu().numpy()
         self._frames.append(rgb)
 
diff --git a/embodichain/lab/gym/utils/_component_composition.py b/embodichain/lab/gym/utils/_component_composition.py
index 9c2387d23..5ffa6ff81 100644
--- a/embodichain/lab/gym/utils/_component_composition.py
+++ b/embodichain/lab/gym/utils/_component_composition.py
@@ -59,29 +59,10 @@
         "rigid_object",
         "rigid_object_group",
         "articulation",
-        "fluid_object",
     }
 )
 
 
-def _resolve_physical_source_paths(simulation: dict, base_dir: Path) -> dict:
-    """Anchor file-backed fluid sources and explicitly relative visual meshes."""
-    result = deepcopy(dict(simulation))
-    for field in ("background", "rigid_object"):
-        for item in result.get(field, []):
-            for key in ("shape", "visual_shape", "particle_collision_shape"):
-                shape = item.get(key) or {}
-                path = shape.get("fpath")
-                if isinstance(path, str) and path.startswith(("./", "../")):
-                    shape["fpath"] = str((base_dir / path).resolve())
-    for fluid in result.get("fluid_object", []):
-        spawn = fluid.get("spawn", {})
-        path = spawn.get("file_path")
-        if isinstance(path, str) and not Path(path).is_absolute():
-            spawn["file_path"] = str((base_dir / path).resolve())
-    return result
-
-
 @dataclass(frozen=True, slots=True)
 class _ResolvedGymComponents:
     """One Gym config after its reusable components are expanded."""
@@ -240,9 +221,7 @@ def _resolve_environment_component(
         for key, value in component.items()
         if key not in {"environment_id", "simulation"}
     }
-    environment_values.update(
-        _resolve_physical_source_paths(dict(simulation), component_path.parent)
-    )
+    environment_values.update(deepcopy(dict(simulation)))
 
     deployment_physics_fields = sorted(
         {"physics", "physics_config"}.intersection(resolved)
@@ -349,7 +328,7 @@ def _resolve_scene_component(
         required=frozenset(),
         optional=_SCENE_SIMULATION_FIELDS,
     )
-    return _resolve_physical_source_paths(dict(simulation), component_path.parent)
+    return deepcopy(dict(simulation))
 
 
 def _physical_scene_uids(
diff --git a/embodichain/lab/gym/utils/gym_utils.py b/embodichain/lab/gym/utils/gym_utils.py
index 6f825d0a7..873979a92 100644
--- a/embodichain/lab/gym/utils/gym_utils.py
+++ b/embodichain/lab/gym/utils/gym_utils.py
@@ -490,16 +490,6 @@ def config_to_cfg(
     from embodichain.utils import configclass
     from embodichain.data import get_data_path
 
-    def _resolve_mesh_asset_paths(obj_dict: dict[str, Any]) -> None:
-        for key in ("shape", "visual_shape", "particle_collision_shape"):
-            shape = obj_dict.get(key)
-            if (
-                shape is not None
-                and shape.get("shape_type") == "Mesh"
-                and shape.get("fpath") is not None
-            ):
-                shape["fpath"] = get_data_path(shape["fpath"])
-
     @configclass
     class ComponentCfg:
         """Configuration for env events.
@@ -745,21 +735,12 @@ class ComponentCfg:
         ]
         env_cfg.light.indirect = config["light"].get("indirect", None)
 
-    # Fluids use one typed declaration path for inline and component scenes.
-    from embodichain.lab.sim.cfg import FluidObjectCfg
-    from embodichain.lab.gym.utils._component_composition import (
-        _resolve_physical_source_paths,
-    )
-
-    config = _resolve_physical_source_paths(config, base_dir)
-    env_cfg.fluid_object = [
-        FluidObjectCfg.from_dict(item) for item in config.get("fluid_object", [])
-    ]
-
     # parser background objects config
     if "background" in config:
         for obj_dict in config["background"]:
-            _resolve_mesh_asset_paths(obj_dict)
+            shape_type = obj_dict["shape"]["shape_type"]
+            if shape_type == "Mesh":
+                obj_dict["shape"]["fpath"] = get_data_path(obj_dict["shape"]["fpath"])
             # Set to static object if not specified.
             obj_dict["body_type"] = (
                 "static" if "body_type" not in obj_dict else obj_dict["body_type"]
@@ -770,7 +751,9 @@ class ComponentCfg:
     # parser scene objects config
     if "rigid_object" in config:
         for obj_dict in config["rigid_object"]:
-            _resolve_mesh_asset_paths(obj_dict)
+            shape_type = obj_dict["shape"]["shape_type"]
+            if shape_type == "Mesh":
+                obj_dict["shape"]["fpath"] = get_data_path(obj_dict["shape"]["fpath"])
             cfg = RigidObjectCfg.from_dict(obj_dict)
             env_cfg.rigid_object.append(cfg)
 
@@ -779,7 +762,11 @@ class ComponentCfg:
             if "folder_path" in obj_dict:
                 obj_dict["folder_path"] = get_data_path(obj_dict["folder_path"])
             for rigid_obj in obj_dict["rigid_objects"].values():
-                _resolve_mesh_asset_paths(rigid_obj)
+                shape_type = rigid_obj["shape"]["shape_type"]
+                if shape_type == "Mesh" and "fpath" in rigid_obj["shape"]:
+                    rigid_obj["shape"]["fpath"] = get_data_path(
+                        rigid_obj["shape"]["fpath"]
+                    )
             cfg = RigidObjectGroupCfg.from_dict(obj_dict)
             env_cfg.rigid_object_group.append(cfg)
 
@@ -896,12 +883,6 @@ class ComponentCfg:
                 )
                 obs_params_modified["params"]["entity_cfg"] = entity_cfg
 
-            if "entity_cfgs" in obs_params_modified["params"]:
-                obs_params_modified["params"]["entity_cfgs"] = [
-                    SceneEntityCfg(**item)
-                    for item in obs_params_modified["params"]["entity_cfgs"]
-                ]
-
             # Find the function from multiple modules using the utility function
             obs_func = find_function_from_modules(
                 obs_params["func"],
@@ -914,7 +895,6 @@ class ComponentCfg:
                 mode=obs_params_modified["mode"],
                 name=obs_params_modified["name"],
                 params=obs_params_modified["params"],
-                extra=deepcopy(obs_params_modified.get("extra", {})),
             )
 
             setattr(env_cfg.observations, obs_name, observation)
diff --git a/embodichain/lab/scripts/run_fluid_task_program.py b/embodichain/lab/scripts/run_fluid_task_program.py
deleted file mode 100644
index fc023a048..000000000
--- a/embodichain/lab/scripts/run_fluid_task_program.py
+++ /dev/null
@@ -1,267 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-
-"""Run a Task Program with live robot/cup contact and one-way SPH fluids."""
-
-from __future__ import annotations
-
-import argparse
-from collections.abc import Iterable, Iterator
-import json
-from pathlib import Path
-import time
-from typing import Any
-
-import numpy as np
-import torch
-
-from embodichain.lab.gym.envs.demo import execute_demo_episode
-from embodichain.lab.gym.envs.embodied_env import EmbodiedEnvCfg
-from embodichain.lab.gym.envs.managers.cfg import SceneEntityCfg
-from embodichain.lab.gym.envs.managers.observations import (
-    get_fluid_summary,
-    get_fluid_volume_in_region,
-)
-from embodichain.lab.gym.utils.gym_utils import config_to_cfg
-from embodichain.lab.gym.utils.registration import discover_task_packages, make
-from embodichain.lab.sim import SimulationManager
-from embodichain.utils.config_paths import resolve_config_path
-from embodichain.utils.utility import load_config
-
-__all__ = ["main"]
-
-_TASK = (
-    "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/"
-    "task.franka.fluid.yaml"
-)
-
-
-def _configure(
-    task: Path,
-    *,
-    num_envs: int,
-    renderer: str,
-    output: Path,
-    particle_spacing: float | None = None,
-    record_trajectory: bool = False,
-) -> tuple[str, EmbodiedEnvCfg]:
-    declaration = load_config(str(task))
-    cfg = config_to_cfg(declaration, source_path=task)
-    if cfg.task_program is None:
-        raise ValueError("The online fluid deployment must select a Task Program.")
-    solver = cfg.sim_cfg.physics_cfg.solver_cfg
-    if solver.get("solver_type") != "sph" or solver.get("rigid_solver_cfg") is None:
-        raise ValueError("Online fluid execution requires SPH with rigid_solver_cfg.")
-    if num_envs < 1:
-        raise ValueError("num_envs must be positive.")
-    cfg.num_envs = num_envs
-    cfg.sim_cfg.headless = True
-    cfg.sim_cfg.render_cfg.renderer = renderer
-    cfg.enable_sensor = False
-    cfg.record_trajectory = record_trajectory
-    if renderer == "no-render":
-        cfg.events.record_camera = None
-        for fluid in cfg.fluid_object:
-            fluid.render.mode = "none"
-    else:
-        cfg.events.record_camera.params["save_path"] = str(output / "videos")
-        cfg.events.record_camera.params["max_env_num"] = num_envs
-    if particle_spacing is not None:
-        if not np.isfinite(particle_spacing) or particle_spacing <= 0:
-            raise ValueError("particle_spacing must be finite and positive.")
-        fluid = cfg.fluid_object[0]
-        if fluid.spawn.kind == "points":
-            raise ValueError("particle-spacing requires a procedural volume source.")
-        scale = particle_spacing / fluid.particle_spacing
-        if fluid.render.render_radius is not None:
-            fluid.render.render_radius *= scale
-        for key in ("cell_size", "kernel_radius"):
-            if key in fluid.render.mesh_surface:
-                fluid.render.mesh_surface[key] *= scale
-        fluid.particle_spacing = particle_spacing
-        fluid.particle_radius = 0.3 * particle_spacing
-        solver["particle_support_radius"] = 2.25 * particle_spacing
-    return declaration["id"], cfg
-
-
-def _sample(env: Any) -> dict[str, Any]:
-    summary = get_fluid_summary(env, {}, SceneEntityCfg(uid="water"))
-    if not torch.isfinite(summary).all() or not torch.all(summary[:, -1] == 1):
-        raise RuntimeError("Fluid state became non-finite.")
-    received = get_fluid_volume_in_region(
-        env,
-        {},
-        [SceneEntityCfg(uid="water"), SceneEntityCfg(uid="receiver")],
-        lower=[-0.1, -0.1, 0.0045],
-        upper=[0.1, 0.1, 0.0675],
-        sphere_center=[0, 0, 0.088],
-        sphere_radius=0.093,
-    )
-    return {
-        "time_s": (env.elapsed_steps * env.step_dt).cpu().tolist(),
-        "received_ml": (received[:, 0] * 1e6).cpu().tolist(),
-        "summary": summary.cpu().tolist(),
-        "cup_pose": env.sim.get_rigid_object("cup").get_local_pose().cpu().tolist(),
-    }
-
-
-def _cup_qualification(poses: list[np.ndarray]) -> dict[str, Any]:
-    measured = np.stack(poses, axis=1)
-    lift = measured[..., 2].max(axis=1) - measured[:, 0, 2]
-    vertical = 1 - 2 * (measured[..., 3] ** 2 + measured[..., 4] ** 2)
-    tilt = np.rad2deg(np.arccos(np.clip(vertical, -1, 1))).max(axis=1)
-    return_error = np.linalg.norm(measured[:, -1, :3] - measured[:, 0, :3], axis=1)
-    return {
-        "lift_m": lift.tolist(),
-        "max_tilt_deg": tilt.tolist(),
-        "return_error_m": return_error.tolist(),
-        "accepted": ((lift >= 0.05) & (tilt >= 45) & (return_error <= 0.03)).tolist(),
-    }
-
-
-def _run(args: argparse.Namespace, output: Path) -> dict[str, Any]:
-    path = resolve_config_path(args.task).resolve()
-    task_id, cfg = _configure(
-        path,
-        num_envs=args.num_envs,
-        renderer=args.renderer,
-        output=output,
-        particle_spacing=args.particle_spacing,
-        record_trajectory=args.record_trajectory,
-    )
-    discover_task_packages()
-    env = make(task_id, cfg=cfg)
-    try:
-        env.reset(seed=0)
-        cup = env.sim.get_rigid_object("cup")
-        poses = [cup.get_local_pose().cpu().numpy().copy()]
-        samples = [_sample(env)]
-        started = time.perf_counter()
-        sample_stride = max(1, round(1 / env.step_dt))
-
-        def observe(actions: Iterable[Any], description: str) -> Iterator[Any]:
-            print(description, flush=True)
-            for action in actions:
-                yield action
-                poses.append(cup.get_local_pose().cpu().numpy().copy())
-                if int(env.elapsed_steps.max()) % sample_stride == 0:
-                    sample = _sample(env)
-                    samples.append(sample)
-                    print(json.dumps(sample), flush=True)
-
-        def stop() -> bool:
-            return (
-                args.seconds is not None
-                and float(env.elapsed_steps.max()) * env.step_dt >= args.seconds
-            )
-
-        result = execute_demo_episode(env, progress=observe, should_stop=stop)
-        if result.completed and args.settle_seconds:
-            target = env.robot.get_qpos()[:, env.active_joint_ids].clone()
-            for _ in range(round(args.settle_seconds / env.step_dt)):
-                env.step(target)
-                poses.append(cup.get_local_pose().cpu().numpy().copy())
-        samples.append(_sample(env))
-        qualification = _cup_qualification(poses)
-        videos = []
-        if args.renderer != "no-render":
-            env.event_manager.get_functor("record_camera").save_and_clear()
-            name = cfg.events.record_camera.params["name"]
-            videos = [
-                str(Path("videos") / f"episode_0_{name}_env{i}.mp4")
-                for i in range(env.num_envs)
-            ]
-        if args.record_trajectory:
-            env.save_trajectory(str(output / "online_trajectory.pt"))
-        report = {
-            "task": str(path),
-            "coupling": "online one-way rigid-to-fluid",
-            "num_envs": env.num_envs,
-            "renderer": args.renderer,
-            "videos": videos,
-            "video_fps": env.control_frequency if videos else None,
-            "particle_count_per_env": env.sim.get_fluid_object("water").num_particles,
-            "fluid_source": cfg.fluid_object[0].spawn.to_dict(),
-            "particle_spacing_m": cfg.fluid_object[0].particle_spacing,
-            "physics_dt": cfg.sim_cfg.physics_dt,
-            "control_dt": env.step_dt,
-            "task_program": result.to_metadata(),
-            "cup": qualification,
-            "wall_seconds": time.perf_counter() - started,
-            "samples": samples,
-        }
-        report["accepted"] = bool(
-            result.completed
-            and result.all_success
-            and all(qualification["accepted"])
-            and all(v >= 50 for v in samples[-1]["received_ml"])
-        )
-        (output / "fluid_gym_result.json").write_text(
-            json.dumps(report, indent=2) + "\n"
-        )
-        print(
-            json.dumps(
-                {
-                    key: report[key]
-                    for key in ("accepted", "coupling", "cup", "wall_seconds")
-                }
-            ),
-            flush=True,
-        )
-        if args.seconds is None and not report["accepted"]:
-            raise RuntimeError(
-                "Online pouring failed physical qualification; inspect fluid_gym_result.json."
-            )
-        return report
-    finally:
-        env.close(exit_process=False)
-        del env
-        SimulationManager.flush_cleanup_queue()
-
-
-def main() -> None:
-    """Execute the configured Task Program and fluid simulation in one World."""
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--task", default=_TASK)
-    parser.add_argument(
-        "--output", type=Path, default=Path("outputs/fluid-task-program-online")
-    )
-    parser.add_argument("--num-envs", type=int, default=2)
-    parser.add_argument(
-        "--renderer", choices=["fast-rt", "no-render"], default="fast-rt"
-    )
-    parser.add_argument("--seconds", type=float)
-    parser.add_argument("--particle-spacing", type=float)
-    parser.add_argument("--settle-seconds", type=float, default=2.0)
-    parser.add_argument(
-        "--record-trajectory",
-        action="store_true",
-        help="Optionally save measured online states for inspection.",
-    )
-    args = parser.parse_args()
-    if args.seconds is not None and (
-        not np.isfinite(args.seconds) or args.seconds <= 0
-    ):
-        parser.error("seconds must be finite and positive")
-    if not np.isfinite(args.settle_seconds) or args.settle_seconds < 0:
-        parser.error("settle-seconds must be finite and non-negative")
-    output = args.output.resolve()
-    output.mkdir(parents=True, exist_ok=True)
-    _run(args, output)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/embodichain/lab/sim/_runtime_controls.py b/embodichain/lab/sim/_runtime_controls.py
index b56f37c67..6c0b23954 100644
--- a/embodichain/lab/sim/_runtime_controls.py
+++ b/embodichain/lab/sim/_runtime_controls.py
@@ -122,59 +122,3 @@ def close(self) -> None:
         """Release runtime particle references retained after initialization."""
         self._particle_set = None
         self._initial_positions = None
-
-
-class _KinematicRigidTrajectoryControl(RuntimeControl):
-    """Sample world pose tracks on the native solver substep clock."""
-
-    def __init__(self, target: str, poses: np.ndarray, fps: float) -> None:
-        from scipy.spatial.transform import Rotation, Slerp
-
-        self.target = target
-        self.poses = poses.copy()
-        self.fps = fps
-        self._times = np.arange(len(poses)) / fps
-        self._rotations = Rotation.from_quat(poses[:, 3:])
-        self._slerp = Slerp(self._times, self._rotations) if len(poses) > 1 else None
-        self._actor = None
-        self.reset()
-
-    def initialize(self, context: Any) -> None:
-        self._actor = context.result.get_actor(self.target)
-        self.reset()
-
-    def exclusive_resource_claims(self) -> tuple[object, ...]:
-        return (("kinematic_rigid_trajectory", self.target),)
-
-    def reset(self) -> None:
-        self._elapsed = 0.0
-        self._previous = None
-
-    def __call__(
-        self, context: Any, substep_index: int, substep_count: int, substep_dt: float
-    ) -> None:
-        from scipy.spatial.transform import Rotation
-
-        del context, substep_index, substep_count
-        if self._actor is None:
-            raise RuntimeError("Kinematic rigid trajectory is not initialized.")
-        t = min(self._elapsed, self._times[-1])
-        position = np.array(
-            [np.interp(t, self._times, self.poses[:, axis]) for axis in range(3)]
-        )
-        rotation = self._rotations[0] if self._slerp is None else self._slerp(t)
-        pose = np.eye(4, dtype=np.float32)
-        pose[:3, :3] = rotation.as_matrix()
-        pose[:3, 3] = position
-        previous = pose if self._previous is None else self._previous
-        self._actor.set_world_pose(pose)
-        self._actor.set_linear_velocity((pose[:3, 3] - previous[:3, 3]) / substep_dt)
-        self._actor.set_angular_velocity(
-            Rotation.from_matrix(pose[:3, :3] @ previous[:3, :3].T).as_rotvec()
-            / substep_dt
-        )
-        self._previous = pose
-        self._elapsed += substep_dt
-
-    def close(self) -> None:
-        self._actor = None
diff --git a/embodichain/lab/sim/cfg/rigid_object.py b/embodichain/lab/sim/cfg/rigid_object.py
index c0436b415..facc35b76 100644
--- a/embodichain/lab/sim/cfg/rigid_object.py
+++ b/embodichain/lab/sim/cfg/rigid_object.py
@@ -46,19 +46,6 @@ class RigidObjectCfg(ObjectBaseCfg):
 
     # TODO: supoort basic primitive shapes, such as box, sphere, etc cfg and spawn method.
 
-    visual_shape: ShapeCfg | None = None
-    """Optional independent render geometry; shape remains collision geometry."""
-
-    particle_collision_shape: ShapeCfg | None = None
-    """Optional Newton SDF mesh for particles on this same rigid body.
-
-    Rigid contacts and mass use :attr:`shape`. This boundary follows the body's
-    live pose and twist and contributes no mass or rigid contact geometry.
-    """
-
-    render_enabled: bool = True
-    """Whether to create native render geometry for this rigid object."""
-
     attrs: RigidBodyPhysicsCfg = RigidBodyPhysicsCfg()
     """Rigid-body physics.
 
@@ -86,9 +73,6 @@ def resolve_asset_physics_mode(self) -> AssetPhysicsMode:
     def from_dict(cls, init_dict: Dict[str, Any]) -> RigidObjectCfg:
         """Parse a rigid object and normalize legacy mesh collision ownership."""
         data = dict(init_dict)
-        for key in ("visual_shape", "particle_collision_shape"):
-            if isinstance(data.get(key), Mapping):
-                data[key] = ShapeCfg.from_dict(data[key])
         attrs_value = data.get("attrs")
         if isinstance(attrs_value, Mapping) and "mesh_collision_props" in attrs_value:
             shape_value = data.get("shape")
diff --git a/embodichain/lab/sim/objects/backends/newton.py b/embodichain/lab/sim/objects/backends/newton.py
index e68b5c5c9..33b164404 100644
--- a/embodichain/lab/sim/objects/backends/newton.py
+++ b/embodichain/lab/sim/objects/backends/newton.py
@@ -204,8 +204,7 @@ def _configure_newton_mimic_compliance(
     backend = get_newton_backend(result.world)
     if (
         backend is None
-        or backend.solver_type not in {"mujoco_warp", "sph"}
-        or backend.mujoco_solver is None
+        or backend.solver_type != "mujoco_warp"
         or backend.cfg.requires_grad
         or (backend.model is not None and backend.model.requires_grad)
     ):
@@ -285,19 +284,13 @@ def _configure_newton_mimic_compliance(
             f"Newton model has no mimic constraint for joint pair {error.args[0]}."
         ) from error
 
-    solver = backend.mujoco_solver
+    solver = runtime.solver
     mapping = getattr(solver, "mjc_eq_to_newton_mimic", None)
     mjw_model = getattr(solver, "mjw_model", None)
     if mapping is None or mjw_model is None:
         raise RuntimeError("MuJoCo-Warp did not materialize Newton mimic rows.")
 
     mapping_values = np.asarray(mapping.numpy())
-    index_map = backend.get_mujoco_index_map(model.AttributeFrequency.CONSTRAINT_MIMIC)
-    if index_map is not None:
-        local_to_global = np.asarray(index_map.numpy())
-        valid = mapping_values >= 0
-        mapping_values = mapping_values.copy()
-        mapping_values[valid] = local_to_global[mapping_values[valid]]
     selected = np.isin(mapping_values, constraint_rows)
     if int(selected.sum()) != len(constraint_rows):
         raise RuntimeError(
@@ -306,8 +299,7 @@ def _configure_newton_mimic_compliance(
     eq_solref = np.asarray(mjw_model.eq_solref.numpy()).copy()
     mimic_solref = _default_mujoco_mimic_solref(
         float(backend.cfg.dt),
-        int(backend.cfg.num_substeps)
-        * int(getattr(backend.cfg.solver_cfg, "rigid_solver_substeps", 1)),
+        int(backend.cfg.num_substeps),
     )
     eq_solref[selected] = mimic_solref
     mjw_model.eq_solref.assign(eq_solref)
diff --git a/embodichain/lab/sim/objects/rigid_object.py b/embodichain/lab/sim/objects/rigid_object.py
index ad9acc056..64ea84d4b 100644
--- a/embodichain/lab/sim/objects/rigid_object.py
+++ b/embodichain/lab/sim/objects/rigid_object.py
@@ -75,7 +75,6 @@
     _restore_render_materials,
     _set_render_material,
     _wrap_first_render_material,
-    _apply_volume_material_properties,
 )
 from ._mesh_utils import (
     get_combined_triangles,
@@ -497,9 +496,7 @@ def _initialize_spawn_bound(self, result: Scene) -> None:
 
     def _apply_spawn_visual_uv(self) -> None:
         """Apply configured projective UV mapping to spawned render bodies."""
-        shape = getattr(self.cfg, "visual_shape", None) or getattr(
-            self.cfg, "shape", None
-        )
+        shape = self.cfg.shape
         if not isinstance(shape, MeshCfg) or not shape.compute_uv:
             return
 
@@ -938,78 +935,6 @@ def add_force_torque(
         else:
             logger.log_error("Cannot apply force or torque before body view is ready.")
 
-    def set_kinematic_state(
-        self,
-        pose_w: torch.Tensor,
-        lin_vel_w: torch.Tensor,
-        ang_vel_w: torch.Tensor,
-        env_ids: Sequence[int] | None = None,
-    ) -> None:
-        """Set a kinematic boundary's world pose and twist without stepping.
-
-        Args:
-            pose_w: World poses, ``(E, 7)`` xyz+xyzw or ``(E, 4, 4)``.
-            lin_vel_w: World linear velocity ``(E, 3)``, m/s.
-            ang_vel_w: World angular velocity ``(E, 3)``, rad/s.
-            env_ids: Selected replicas, or None for all.
-        """
-        if self.body_type != "kinematic":
-            raise ValueError("set_kinematic_state requires a kinematic rigid body.")
-        selection = torch.as_tensor(self._all_indices if env_ids is None else env_ids)
-        if selection.ndim != 1 or (
-            selection.numel() and selection.dtype not in (torch.int32, torch.int64)
-        ):
-            raise ValueError("env_ids must be a one-dimensional integer selection.")
-        ids = selection.tolist()
-        if len(set(ids)) != len(ids) or any(
-            i < 0 or i >= self.num_instances for i in ids
-        ):
-            raise ValueError("env_ids must be unique valid indices.")
-        if tuple(pose_w.shape) not in {(len(ids), 7), (len(ids), 4, 4)}:
-            raise ValueError("pose_w has an invalid shape.")
-        if any(tuple(v.shape) != (len(ids), 3) for v in (lin_vel_w, ang_vel_w)):
-            raise ValueError("Boundary velocities must have shape (E, 3).")
-        if not all(torch.isfinite(v).all() for v in (pose_w, lin_vel_w, ang_vel_w)):
-            raise ValueError("Kinematic state must be finite.")
-        if pose_w.ndim == 2:
-            if not torch.allclose(
-                pose_w[:, 3:].norm(dim=-1),
-                torch.ones(len(ids), device=pose_w.device, dtype=pose_w.dtype),
-                atol=1e-5,
-            ):
-                raise ValueError("pose_w requires unit xyzw quaternions.")
-        else:
-            rotations = pose_w[:, :3, :3]
-            identity = torch.eye(3, device=pose_w.device, dtype=pose_w.dtype).expand(
-                len(ids), -1, -1
-            )
-            if (
-                not torch.allclose(
-                    rotations.transpose(-1, -2) @ rotations, identity, atol=1e-5
-                )
-                or not torch.allclose(
-                    torch.linalg.det(rotations),
-                    torch.ones(len(ids), device=pose_w.device, dtype=pose_w.dtype),
-                    atol=1e-5,
-                )
-                or not torch.allclose(
-                    pose_w[:, 3], pose_w.new_tensor([0, 0, 0, 1]).expand(len(ids), -1)
-                )
-            ):
-                raise ValueError("pose_w requires rigid homogeneous transforms.")
-        if self._data is None or not self._data.body_view.is_ready:
-            raise RuntimeError("Kinematic state requires prepare().")
-        if pose_w.ndim == 2:
-            pose = torch.eye(4).repeat(len(ids), 1, 1)
-            pose[:, :3, :3] = matrix_from_quat(pose_w[:, 3:].cpu())
-            pose[:, :3, 3] = pose_w[:, :3].cpu()
-        else:
-            pose = pose_w.cpu()
-        for row, index in enumerate(ids):
-            self._entities[index].set_world_pose(pose[row].numpy())
-            self._entities[index].set_linear_velocity(lin_vel_w[row].cpu().numpy())
-            self._entities[index].set_angular_velocity(ang_vel_w[row].cpu().numpy())
-
     def set_velocity(
         self,
         lin_vel: torch.Tensor | None = None,
@@ -1581,14 +1506,6 @@ def _initialize_existing_visual_material(self) -> None:
             if render_body is None:
                 continue
             original_materials = _capture_render_materials(render_body)
-            shape = getattr(self.cfg, "visual_shape", None) or getattr(
-                self.cfg, "shape", None
-            )
-            material_cfg = getattr(shape, "visual_material", None)
-            if material_cfg is not None:
-                for native in original_materials:
-                    if native is not None:
-                        _apply_volume_material_properties(native, material_cfg)
             self._original_visual_material[env_idx] = original_materials
             wrapped = _wrap_first_render_material(original_materials)
             if wrapped is not None:
diff --git a/embodichain/lab/sim/shapes.py b/embodichain/lab/sim/shapes.py
index dba47693e..2c2e4612b 100755
--- a/embodichain/lab/sim/shapes.py
+++ b/embodichain/lab/sim/shapes.py
@@ -71,9 +71,6 @@ class MeshCollisionCfg:
     VisACD requires a DexSim build with CUDA/OptiX support.
     """
 
-    force_sdf: bool | None = None
-    """Optional Newton SDF strategy override; False uses an eagerly baked SDF."""
-
     sdf_resolution: int | None = None
     """Maximum SDF grid resolution; valid only for the ``sdf`` strategy."""
 
@@ -129,7 +126,6 @@ def __post_init__(self) -> None:
             )
 
         sdf_values = {
-            "force_sdf": self.force_sdf,
             "sdf_resolution": self.sdf_resolution,
             "is_hydroelastic": self.is_hydroelastic,
             "sdf_narrow_band_range": self.sdf_narrow_band_range,
@@ -191,9 +187,6 @@ def from_dict(cls, init_dict: Dict[str, Any]) -> MeshCollisionCfg:
             "force_sdf",
             "sdf_max_resolution",
         }
-        explicit_force_sdf = data.get("force_sdf") if "approximation" in data else None
-        if "approximation" in data:
-            legacy_fields.discard("force_sdf")
         has_legacy_fields = bool(legacy_fields.intersection(data))
         if has_legacy_fields:
             warnings.warn(
@@ -237,8 +230,6 @@ def from_dict(cls, init_dict: Dict[str, Any]) -> MeshCollisionCfg:
 
         if data.get("sdf_resolution") == 0:
             data.pop("sdf_resolution")
-        if explicit_force_sdf is not None:
-            data["force_sdf"] = explicit_force_sdf
         return cls(**data)
 
 
@@ -250,7 +241,6 @@ def _mesh_collision_cfg_init_with_legacy_max_hulls(
     approximation: MeshCollisionApproximation | None = None,
     max_hulls: int | None = None,
     acd_method: Literal["visacd", "coacd", "vhacd"] | None = None,
-    force_sdf: bool | None = None,
     sdf_resolution: int | None = None,
     is_hydroelastic: bool | None = None,
     sdf_narrow_band_range: tuple[float, float] | None = None,
@@ -293,7 +283,6 @@ def _mesh_collision_cfg_init_with_legacy_max_hulls(
         approximation="convex_hull" if approximation is None else approximation,
         max_hulls=max_hulls,
         acd_method=acd_method,
-        force_sdf=force_sdf,
         sdf_resolution=sdf_resolution,
         is_hydroelastic=is_hydroelastic,
         sdf_narrow_band_range=sdf_narrow_band_range,
diff --git a/embodichain/lab/sim/sim_manager.py b/embodichain/lab/sim/sim_manager.py
index 5fa1b3fed..36fcfad0f 100644
--- a/embodichain/lab/sim/sim_manager.py
+++ b/embodichain/lab/sim/sim_manager.py
@@ -608,7 +608,6 @@ def _initialize_simulation(
         self._markers: dict[str, _AxisMarkerGroup] = {}
         self._constraints: dict[str, RigidConstraint] = {}
         self._fluid_objects: dict[str, FluidObject] = {}
-        self._rigid_trajectory_controls: dict[str, tuple] = {}
         self._visual_materials: dict[str, VisualMaterial] = {}
         self._texture_cache: dict[str, torch.Tensor | list[torch.Tensor]] = {}
 
@@ -1327,61 +1326,6 @@ def _init_sim_resources(self) -> None:
 
         self._default_resources = SimResources()
 
-    def register_kinematic_rigid_trajectory(
-        self,
-        uid: str,
-        world_poses: torch.Tensor | np.ndarray,
-        *,
-        fps: float,
-    ) -> None:
-        """Drive declared kinematic rigid bodies at each Newton solver substep.
-
-        Args:
-            uid: Previously declared kinematic rigid-object UID.
-            world_poses: Finite unit-quaternion xyz+xyzw tracks, ``(E, frames, 7)``.
-            fps: Positive pose sample frequency in Hz. Positions interpolate
-                linearly, orientations use SLERP, and the last pose is held.
-
-        The host-side control disables CUDA graph replay. Register before
-        prepare; reset_objects_state rewinds only selected instance clocks.
-        """
-        from embodichain.lab.sim._runtime_controls import (
-            _KinematicRigidTrajectoryControl,
-        )
-
-        if not self.is_newton_backend or self._spawn_scene.builder.is_finalized:
-            raise RuntimeError("Register Newton rigid trajectories before prepare().")
-        rigid = self._rigid_objects.get(uid)
-        if rigid is None or rigid.body_type != "kinematic":
-            raise ValueError("uid must name a declared kinematic rigid object.")
-        if uid in self._rigid_trajectory_controls:
-            raise ValueError("A rigid trajectory already controls this object.")
-        poses = (
-            world_poses.detach().cpu().numpy()
-            if isinstance(world_poses, torch.Tensor)
-            else np.asarray(world_poses)
-        )
-        if (
-            poses.ndim != 3
-            or poses.shape[0] != self.num_envs
-            or poses.shape[1] == 0
-            or poses.shape[2] != 7
-        ):
-            raise ValueError("world_poses must have shape (E, frames, 7).")
-        if not np.isfinite(poses).all() or not np.allclose(
-            np.linalg.norm(poses[..., 3:], axis=-1), 1.0, atol=1e-5
-        ):
-            raise ValueError("world_poses must be finite with unit xyzw quaternions.")
-        if not np.isfinite(fps) or fps <= 0:
-            raise ValueError("fps must be finite and positive.")
-        controls = tuple(
-            _KinematicRigidTrajectoryControl(f"{arena}/{uid}", poses[i], fps)
-            for i, arena in enumerate(self._spawn_scene.arena_names)
-        )
-        for control in controls:
-            self._spawn_scene.builder.add_runtime_control(control)
-        self._rigid_trajectory_controls[uid] = controls
-
     def register_kinematic_joint_trajectory(
         self,
         uid: str,
@@ -4903,10 +4847,6 @@ def reset_objects_state(
         for uid, rigid_obj_group in self._rigid_object_groups.items():
             if uid not in excluded_uids:
                 rigid_obj_group.reset(env_ids)
-        for uid, controls in self._rigid_trajectory_controls.items():
-            if uid not in excluded_uids:
-                for index in (range(self.num_envs) if env_ids is None else env_ids):
-                    controls[int(index)].reset()
         for uid, fluid_obj in self._fluid_objects.items():
             if uid not in excluded_uids:
                 fluid_obj.reset(env_ids)
@@ -5107,7 +5047,6 @@ def _deferred_destroy(self) -> None:
             finally:
                 self._spawn_scene = None
 
-        self._rigid_trajectory_controls.clear()
         self.clean_materials()
 
         if self._env:
diff --git a/embodichain/lab/sim/spawn/descriptors.py b/embodichain/lab/sim/spawn/descriptors.py
index 4a1e3705e..bbcbe2af0 100644
--- a/embodichain/lab/sim/spawn/descriptors.py
+++ b/embodichain/lab/sim/spawn/descriptors.py
@@ -303,14 +303,8 @@ def rigid_desc_from_cfg(
         )
     )
     geometry, approximation, max_hulls, acd_method = _compile_geometry(cfg)
-    render_shape = cfg.visual_shape or cfg.shape
-    render_geometry = geometry
-    if cfg.visual_shape is not None:
-        render_cfg = cfg.copy()
-        render_cfg.shape = cfg.visual_shape
-        render_geometry = _compile_geometry(render_cfg)[0]
     material_ref, material_entry = _compile_visual_material(
-        uid, render_shape.visual_material
+        uid, cfg.shape.visual_material
     )
     collision = CollisionDesc.from_geometry(
         geometry,
@@ -331,58 +325,19 @@ def rigid_desc_from_cfg(
             cfg.shape.collision if isinstance(cfg.shape, MeshCfg) else None
         ),
     )
-    collision.render_source_index = (
-        0 if cfg.visual_shape is None and cfg.render_enabled else None
-    )
-
-    collisions = [collision]
-    if cfg.particle_collision_shape is not None:
-        boundary_shape = cfg.particle_collision_shape
-        if newton_solver_type is None:
-            raise ValueError("particle_collision_shape requires Newton physics.")
-        if (
-            not isinstance(boundary_shape, MeshCfg)
-            or boundary_shape.collision is None
-            or boundary_shape.collision.approximation != "sdf"
-        ):
-            raise ValueError(
-                "particle_collision_shape requires a MeshCfg with sdf collision."
-            )
-        boundary_cfg = cfg.copy()
-        boundary_cfg.shape = boundary_shape
-        boundary_geometry = _compile_geometry(boundary_cfg)[0]
-        boundary = CollisionDesc.from_geometry(
-            boundary_geometry, approximation=CollisionApproximation.SDF
-        )
-        boundary.enable_collision = False
-        boundary.newton = replace(
-            _compile_newton_collision(
-                physics,
-                newton_solver_type=newton_solver_type,
-                author_shape_defaults=True,
-                mesh_collision=boundary_shape.collision,
-            ),
-            has_particle_collision=True,
-            contributes_to_mass=False,
-        )
-        collision.newton = replace(collision.newton, has_particle_collision=False)
-        collisions.append(boundary)
+    collision.render_source_index = 0
 
     descriptor = ObjectDesc(
         name=uid,
         pose=_pose_from_cfg(cfg),
-        renders=(
-            [
-                RenderDesc.from_geometry(
-                    render_geometry,
-                    load_option=_compile_load_option(render_shape),
-                    material_ref=material_ref,
-                )
-            ]
-            if cfg.render_enabled
-            else []
-        ),
-        collisions=collisions,
+        renders=[
+            RenderDesc.from_geometry(
+                geometry,
+                load_option=_compile_load_option(cfg.shape),
+                material_ref=material_ref,
+            )
+        ],
+        collisions=[collision],
         physics=_compile_rigid_physics(physics, cfg.body_type),
         per_env=per_env,
         body_scale=_vector3(cfg.body_scale, field_name="body_scale"),
@@ -1594,9 +1549,7 @@ def _compile_newton_collision(
             values["gap"] = gap
     values.update(physics.newton_collision_props)
     if mesh_collision is not None and mesh_collision.approximation == "sdf":
-        values["force_sdf"] = (
-            True if mesh_collision.force_sdf is None else mesh_collision.force_sdf
-        )
+        values["force_sdf"] = True
         for field_name in (
             "is_hydroelastic",
             "sdf_narrow_band_range",
@@ -1737,7 +1690,6 @@ def _compile_visual_material(
         metallic_map=cfg.metallic_texture,
         ao_map=cfg.ao_texture,
         ior=float(cfg.ior),
-        transmission=cfg.transmission,
     )
     return key, (key, desc)
 
diff --git a/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip.yaml b/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip.yaml
deleted file mode 100644
index 90bacd060..000000000
--- a/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip.yaml
+++ /dev/null
@@ -1,88 +0,0 @@
-embodiment_id: franka_panda_firm_grip
-simulation:
-  class_type: FrankaPanda
-  robot_type: panda
-  uid: FrankaPanda
-  joint_drive_props:
-    drive_type: force
-    stiffness:
-      fr3_joint[1-7]: 10000.0
-      fr3_finger_joint[1-2]: 10000.0
-    damping:
-      fr3_joint[1-7]: 1000.0
-      fr3_finger_joint[1-2]: 100.0
-    max_effort:
-      fr3_joint[1-7]: 100000.0
-      fr3_finger_joint[1-2]: 80.0
-  link_attrs:
-    # Override the existing finger selector with Default-backend contact properties.
-    newton_gripper_contacts:
-      link_names_expr: [fr3_leftfinger, fr3_rightfinger]
-      attrs:
-        collision_props:
-          backend: default
-          torsional_patch_radius: 0.009
-          min_torsional_patch_radius: 0.008
-        material_props:
-          static_friction: 1.0
-          dynamic_friction: 1.0
-sensor:
-- sensor_type: Camera
-  uid: cam_high
-  width: 640
-  height: 480
-  intrinsics:
-  - 488.1665
-  - 488.1665
-  - 320.0
-  - 240.0
-  extrinsics:
-    eye:
-    - -0.5
-    - -0.9
-    - 0.88
-    target:
-    - -0.5
-    - 0.1
-    - 0.2
-    up:
-    - 0.0
-    - 0.0
-    - 1.0
-skill_profile:
-  contract_id: single_arm_parallel_gripper
-  profile_id: franka_panda_firm_grip
-  resources:
-  - resource_id: primary_manipulator
-    endpoints:
-    - endpoint_id: motion
-      control_part: arm
-      capabilities:
-      - kinematics.batch_inverse
-      - motion.cartesian_pose
-      - kinematics.forward
-    - endpoint_id: grasp
-      control_part: eef
-      capabilities:
-      - interaction.grasp
-      command_preset: parallel_jaw_commands
-  command_presets:
-  - preset_id: parallel_jaw_commands
-    control_part: eef
-    commands:
-      open:
-      - 0.04
-      - 0.04
-      grasp:
-      - 0.0
-      - 0.0
-  runtime_services:
-    grasp_pose_generators:
-      eef:
-        kind: antipodal_parallel_jaw
-        sample_count: 1000
-        opening_margin: 0.002
-        point_sample_density: 0.012
-        filter_ground_collision: false
-        model:
-          model_id: franka_panda_hand
diff --git a/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip_newton.yaml b/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip_newton.yaml
deleted file mode 100644
index 216e95d37..000000000
--- a/embodichain_tasks/configs/components/embodiments/franka_panda_firm_grip_newton.yaml
+++ /dev/null
@@ -1,91 +0,0 @@
-embodiment_id: franka_panda_firm_grip_newton
-simulation:
-  class_type: FrankaPanda
-  robot_type: panda
-  uid: FrankaPanda
-  joint_drive_props:
-    drive_type: force
-    stiffness:
-      fr3_joint[1-7]: 10000.0
-      fr3_finger_joint[1-2]: 8000.0
-    damping:
-      fr3_joint[1-7]: 1000.0
-      fr3_finger_joint[1-2]: 100.0
-    max_effort:
-      fr3_joint[1-7]: 100000.0
-      fr3_finger_joint[1-2]: 80.0
-  link_attrs:
-    # Configure the existing finger selector for MuJoCo torsional and rolling contacts.
-    newton_gripper_contacts:
-      link_names_expr: [fr3_leftfinger, fr3_rightfinger]
-      attrs:
-        collision_props:
-          backend: newton
-          condim: 6
-        material_props:
-          backend: newton
-          dynamic_friction: 1.0
-          ke: 400000.0
-          kd: 1265.0
-          torsional_friction: 0.1
-          rolling_friction: 0.03
-sensor:
-- sensor_type: Camera
-  uid: cam_high
-  width: 640
-  height: 480
-  intrinsics:
-  - 488.1665
-  - 488.1665
-  - 320.0
-  - 240.0
-  extrinsics:
-    eye:
-    - -0.5
-    - -0.9
-    - 0.88
-    target:
-    - -0.5
-    - 0.1
-    - 0.2
-    up:
-    - 0.0
-    - 0.0
-    - 1.0
-skill_profile:
-  contract_id: single_arm_parallel_gripper
-  profile_id: franka_panda_firm_grip_newton
-  resources:
-  - resource_id: primary_manipulator
-    endpoints:
-    - endpoint_id: motion
-      control_part: arm
-      capabilities:
-      - kinematics.batch_inverse
-      - motion.cartesian_pose
-      - kinematics.forward
-    - endpoint_id: grasp
-      control_part: eef
-      capabilities:
-      - interaction.grasp
-      command_preset: parallel_jaw_commands
-  command_presets:
-  - preset_id: parallel_jaw_commands
-    control_part: eef
-    commands:
-      open:
-      - 0.04
-      - 0.04
-      grasp:
-      - 0.0
-      - 0.0
-  runtime_services:
-    grasp_pose_generators:
-      eef:
-        kind: antipodal_parallel_jaw
-        sample_count: 1000
-        opening_margin: 0.002
-        point_sample_density: 0.012
-        filter_ground_collision: false
-        model:
-          model_id: franka_panda_hand
diff --git a/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_gentle.yaml b/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_gentle.yaml
deleted file mode 100644
index 56835899e..000000000
--- a/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_gentle.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-policy_id: trajectory_open_loop_gentle_v1
-preset_id: trajectory
-
-requires:
-  embodiment_contract: single_arm_parallel_gripper
-
-motion:
-  strategy: ik_interp
-  sample_count: 360
-  dynamic_collision_mode: "off"
-
-tracking:
-  kind: timed
-  settle_duration: 0.4
-
-recovery:
-  max_replans: 0
-  max_action_retries: 0
-  goal_rotation_threshold: 3.141592653589793
-
-workflow_recovery:
-  max_recovery_attempts: 0
-
-runner:
-  minimum_cycle_time: 0.0
-  hold_on_completion: true
-  hold_during_effect_verification: false
-
-effect_assurance: projected
diff --git a/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_pour_cup.yaml b/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_pour_cup.yaml
deleted file mode 100644
index 2ee9b3f6f..000000000
--- a/embodichain_tasks/configs/components/execution_policies/trajectory_open_loop_pour_cup.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-policy_id: trajectory_open_loop_pour_cup_v1
-preset_id: trajectory
-
-requires:
-  embodiment_contract: single_arm_parallel_gripper
-
-motion:
-  strategy: ik_interp
-  sample_count: 240
-  dynamic_collision_mode: "off"
-
-tracking:
-  kind: timed
-  settle_duration: 0.4
-
-recovery:
-  max_replans: 0
-  max_action_retries: 0
-  goal_rotation_threshold: 3.141592653589793
-
-workflow_recovery:
-  max_recovery_attempts: 0
-
-runner:
-  minimum_cycle_time: 0.0
-  hold_on_completion: true
-  hold_during_effect_verification: false
-
-effect_assurance: projected
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
deleted file mode 100644
index 1b666e694..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/README.md
+++ /dev/null
@@ -1,93 +0,0 @@
-# Franka 在线倒水
-
-Sim demo 和带水 Gym 部署均在同一个 World 中在线推进：MuJoCo-Warp 求解机械臂驱动、
-重力和杯把抓持接触，DFSPH 读取当前刚体位姿和速度。当前是单向耦合，水没有反作用力。
-初始填充由参数采样，不需要预录关节/杯子轨迹或粒子文件。
-
-## 运行
-
-Gym 使用普通 `EmbodiedEnv`，通过 Task Program 的 `Pick → MoveHeldObject → Pour → Place`
-生成动作目标，和其它任务一样由 `env.step(action)` 实时执行：
-
-```bash
-python -m embodichain.lab.scripts.run_fluid_task_program \
-  --num-envs 2 --renderer fast-rt --output outputs/fluid-task-program-online
-```
-
-也可使用普通任务入口：
-
-```bash
-embodichain run-env \
-  --gym-config embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml \
-  --headless --renderer fast-rt
-```
-
-独立 Sim demo 直接创建 `SimulationManager`，从当前场景规划 Atomic Action 关节目标，
-每帧设置驱动目标并推进真实动力学：
-
-```bash
-python scripts/tutorials/sim/franka_dfsph_pour_demo.py --headless --renderer fast-rt
-```
-
-Gym 的 `--particle-spacing 0.003 --renderer no-render` 可用于较快的物理检查；
-`--seconds 2` 只验证起始片段，不能作为完整倒水验收。Sim 对应参数是 `--spacing`。
-两种入口默认均为 1.5 mm；粗分辨率会改变水量和边界精度。
-
-`env.fluid.yaml` 拥有求解器、场景、初始水体、观测和录制；`task.franka.fluid.yaml`
-选择 Franka 真实接触配置、Task Program、语义绑定和执行策略。
-带水程序先验证实际抬杯，再将杯子移动到相对接水碗定义的倾倒位置，最后倾倒并放回。
-`env.newton.yaml` 与 `env.yaml` 仍提供独立的空杯任务。
-
-## 求解与碰撞几何
-
-直接复用 DexSim 的 `SPHSolverCfg`，通过 `rigid_solver_cfg` 选择现有
-`MJWarpSolverCfg`，没有另建 EmbodiChain SPH 配置类。外层物理步为 1/240 s，
-含 6 个 SPH 子步；每个 SPH 子步含 7 个 MuJoCo 微步，控制周期为 0.025 s。
-两种求解器共享模型、状态和仿真时钟，微步不会重复增加时间。
-
-杯子是一个动态刚体，质量 0.30 kg：`shape` 的凸分解网格提供杯身/杯把的刚体接触，
-`particle_collision_shape` 的杯内 SDF 提供流体边界。后者挂在同一个刚体上，
-不增加质量、不参与刚体接触，也不通过位姿跟随器或固定关节伪造抓持。
-机器人使用已有 Franka firm-grip 配置，保留关节驱动和指腹的抗扭/滚动摩擦。
-
-当前要求兼容的 DexSim Newton CUDA 实现，包含 SPH 与 MuJoCo 的在线组合、
-world 隔离及选择性重置支持。实测运行时使用隔离 JIT 构建；发布 wheel/AOT 尚需单独验收。
-场景准备后粒子数量和物理拓扑固定。
-
-## 初始填充
-
-```yaml
-spawn:
-  kind: revolved_volume
-  reference_uid: cup
-  profile: [[-0.044, 0.031], [0.05, 0.0385]]
-  fill_level: 0.91
-  clearance: 0.003
-```
-
-截面绕杯子局部 Z 轴旋转。`fill_level` 是腔体高度比例；壁面间隙不再裁剪液面。
-1.5 mm 网格生成 76,607 粒，名义体积约 258.55 mL。点集只在声明时采样一次并保存在内存，
-reset 恢复选中环境的初始状态，不额外推进其它行的物理时间。机械臂接近期间
-容器和液体自然稳定。需要在移动后的容器中重新填充时使用
-`reset_fluid_in_container` 事件。NPZ 仍可用于外部点集导入，默认路径不读取或生成 NPZ。
-
-## 录制与验收
-
-当前原生液面位于 OptiX 场景，使用 FastRT；Hybrid 的 Vulkan 主表面无法显示液体，
-因此流体门禁会拒绝 Hybrid，包括复用已初始化 Hybrid 的进程。纯物理检查使用 NoRender。
-
-Gym 的 `record_camera` 事件使用现有 `record_camera_data`，配置 `per_env: true`、
-`fps: null`，每个环境以控制频率 40 fps、960 × 720 分辨率录制杯口与接水碗的独立视角，输出
-`videos/episode_0_fluid_gym_env0.mp4` 等文件。Sim 使用相同视角的 record camera，
-输出 `fluid_sim_env0.mp4` 等文件。离屏读取前显式发布最新渲染状态。
-
-Gym runner 在结束时保存最后一段视频，并写入 `fluid_gym_result.json`，分别记录
-Task Program 结果、实测抬升/倾角/放回误差、接水量及墙钟耗时。
-执行策略的 `projected` 完成状态不能单独证明物理成功；完整验收还要求实际杯子
-抬升至少 5 cm、倾角至少 45°、放回误差不超过 3 cm，且碗内最终接水量至少 50 mL。
-`--record-trajectory` 仅用于可选的实测状态诊断，不作为下一阶段输入。
-Sim 报告为 `validation.json`。旧回放证据归档在 `PourCupDemoData`，不能用来证明在线效果。
-
-任务网格由标准资产缓存中的 `PourCupAssets` 提供，托管于
-[Hugging Face task_assets](https://huggingface.co/datasets/DexForceAI/embodichain_data/tree/main/task_assets)。
-本 PR 不重新提交网格、预录轨迹、粒子快照或视频文件。
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
deleted file mode 100644
index fcda972f1..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.fluid.yaml
+++ /dev/null
@@ -1,356 +0,0 @@
-environment_id: pour_cup_fluid
-physics: newton
-render_cfg:
-  renderer: fast-rt
-num_envs: 2
-arena_space: 2.5
-max_episode_steps: 2000
-max_episodes: 1
-physics_config:
-  physics_dt: 0.004166666666666667
-  num_substeps: 6
-  collision_cfg: null
-  solver_cfg:
-    solver_type: sph
-    sph_method: dfsph
-    particle_support_radius: 0.003375
-    max_neighbors: 192
-    viscosity: 0.0006
-    surface_tension_coefficient: 0.002
-    surface_normal_threshold: 0.08
-    pressure_relaxation: 0.2
-    divergence_max_iterations: 10
-    density_max_iterations: 20
-    max_velocity: 2.0
-    volume_map_boundary_enabled: true
-    volume_map_resolution: 192
-    volume_map_quadrature_samples: 6
-    volume_map_volume_scale: 0.8
-    sdf_boundary_enabled: false
-    rigid_solver_cfg:
-      solver_type: mujoco_warp
-      solver: newton
-      integrator: implicitfast
-      iterations: 50
-      ls_iterations: 100
-      cone: elliptic
-      impratio: 1000.0
-      use_mujoco_contacts: true
-      enable_multiccd: true
-      rne_mode: disabled
-    rigid_solver_substeps: 7
-simulation:
-  light:
-    direct:
-    - uid: main_light
-      light_type: sun
-      color:
-      - 1.0
-      - 1.0
-      - 1.0
-      intensity: 4.0
-      direction:
-      - -0.3
-      - 0.2
-      - -1.0
-    - uid: fill_light
-      light_type: rect
-      color:
-      - 1.0
-      - 1.0
-      - 1.0
-      intensity: 6.0
-      init_pos:
-      - -0.5
-      - -0.4
-      - 1.2
-      direction:
-      - 0.0
-      - 0.0
-      - -1.0
-      rect_width: 0.7
-      rect_height: 0.7
-  background:
-  - uid: cup_platform
-    body_type: static
-    shape:
-      shape_type: Cube
-      size:
-      - 0.4
-      - 0.5
-      - 0.5
-    init_pos:
-    - -0.9
-    - 0.0
-    - 0.25
-    attrs:
-      collision_props:
-        backend: newton
-        condim: 6
-      material_props:
-        backend: newton
-        dynamic_friction: 1.0
-        ke: 400000.0
-        kd: 1265.0
-        torsional_friction: 0.1
-        rolling_friction: 0.03
-    particle_collision_shape:
-      shape_type: Mesh
-      fpath: PourCupAssets/fluid_table.obj
-      collision:
-        approximation: sdf
-        force_sdf: true
-        sdf_resolution: 192
-        sdf_narrow_band_range:
-        - -0.025
-        - 0.025
-        sdf_padding: 0.012
-        sdf_texture_format: uint16
-      visual_material: &id001
-        uid: fluid_table
-        base_color:
-        - 0.23
-        - 0.29
-        - 0.34
-        - 1.0
-        roughness: 0.6
-        metallic: 0.5
-        ior: 1.0
-    visual_shape:
-      shape_type: Mesh
-      fpath: PourCupAssets/fluid_table.obj
-      visual_material: *id001
-  - uid: fluid_floor
-    body_type: static
-    shape:
-      shape_type: Mesh
-      fpath: PourCupAssets/fluid_floor.obj
-      collision:
-        approximation: sdf
-        force_sdf: false
-        sdf_resolution: 192
-        sdf_narrow_band_range:
-        - -0.025
-        - 0.025
-        sdf_padding: 0.012
-        sdf_texture_format: uint16
-    init_pos:
-    - -0.5
-    - 0
-    - -0.055
-    attrs:
-      collision_props:
-        backend: newton
-        has_particle_collision: true
-      material_props:
-        backend: newton
-        dynamic_friction: 0.2
-    render_enabled: false
-  - uid: receiver
-    body_type: static
-    shape:
-      shape_type: Mesh
-      fpath: PourCupAssets/receiver_wall.obj
-      collision:
-        approximation: sdf
-        force_sdf: false
-        sdf_resolution: 192
-        sdf_narrow_band_range:
-        - -0.025
-        - 0.025
-        sdf_padding: 0.012
-        sdf_texture_format: uint16
-      visual_material:
-        uid: fluid_receiver
-        base_color:
-        - 0.82
-        - 0.85
-        - 0.88
-        - 1.0
-        roughness: 0.22
-        metallic: 0.5
-        ior: 1.0
-    init_pos:
-    - -0.755
-    - -0.15
-    - 0.5
-    attrs:
-      collision_props:
-        backend: newton
-        has_particle_collision: true
-      material_props:
-        backend: newton
-        dynamic_friction: 0.2
-  fluid_object:
-  - uid: water
-    density: 1000.0
-    particle_spacing: 0.0015
-    particle_radius: 0.00045
-    spawn:
-      kind: revolved_volume
-      profile:
-      - - -0.044
-        - 0.031
-      - - 0.05
-        - 0.0385
-      fill_level: 0.91
-      clearance: 0.003
-      reference_uid: cup
-    render:
-      mode: mesh_surface
-      render_radius: 0.00045
-      clip_below_z: 0.49
-      material:
-        base_color:
-        - 0.9452768
-        - 0.982207
-        - 1.0
-        - 1.0
-        roughness: 0.025
-        metallic: 0.0
-        ior: 1.333
-        transmission: 0.96
-        volume_thickness: 0.04
-        volume_attenuation_distance: 0.6
-        volume_attenuation_color:
-        - 0.8
-        - 0.92
-        - 1.0
-      mesh_surface:
-        gpu_enabled: true
-        cell_size: 0.0008100000000000001
-        max_res: 512
-        kernel_radius: 0.00225
-        iso_density: 0.57
-        density_smooth_iters: 2
-        aabb_trim_fraction: 0.0
-        min_component_tris: 32
-        laplacian_iters: 4
-        laplacian_alpha: 0.11
-        update_period: 1
-  rigid_object:
-  - uid: cup
-    shape:
-      shape_type: Mesh
-      fpath: PourCupAssets/cup.glb
-      compute_uv: true
-      collision:
-        approximation: convex_decomposition
-        max_hulls: 32
-    body_type: dynamic
-    init_pos:
-    - -0.75
-    - 0.0
-    - 0.552
-    body_scale:
-    - 1.0
-    - 1.0
-    - 1.0
-    attrs:
-      mass_props:
-        mass: 0.3
-      collision_props:
-        backend: newton
-        contact_offset: 0.0005
-        rest_offset: 0.0001
-        condim: 6
-        has_particle_collision: true
-      material_props:
-        backend: newton
-        dynamic_friction: 1.2
-        ke: 400000.0
-        kd: 1265.0
-        torsional_friction: 0.1
-        rolling_friction: 0.03
-    particle_collision_shape:
-      shape_type: Mesh
-      fpath: PourCupAssets/mug_wall.obj
-      collision:
-        approximation: sdf
-        force_sdf: true
-        sdf_resolution: 192
-        sdf_narrow_band_range:
-        - -0.025
-        - 0.025
-        sdf_padding: 0.012
-        sdf_texture_format: uint16
-env:
-  sim_steps_per_control: 6
-  events:
-    record_camera:
-      func: record_camera_data
-      mode: interval
-      interval_step: 1
-      is_global: true
-      params:
-        name: fluid_gym
-        resolution:
-        - 960
-        - 720
-        intrinsics:
-        - 1000.0
-        - 1000.0
-        - 480.0
-        - 360.0
-        eye:
-        - -1.12
-        - -0.73
-        - 0.96
-        target:
-        - -0.76
-        - -0.07
-        - 0.61
-        up:
-        - 0.0
-        - 0.0
-        - 1.0
-        max_env_num: 16
-        per_env: true
-        fps: null
-        save_path: outputs/fluid-task-program/videos
-    refill_water:
-      func: reset_fluid_in_container
-      mode: reset
-      params:
-        entity_cfgs:
-        - uid: water
-        - uid: cup
-  control_parts:
-  - arm
-  - eef
-  observations:
-    water_summary:
-      func: get_fluid_summary
-      mode: add
-      name: fluid/summary
-      params:
-        entity_cfg:
-          uid: water
-      extra:
-        shape:
-        - 5
-    received_volume:
-      func: get_fluid_volume_in_region
-      mode: add
-      name: fluid/received_volume
-      params:
-        entity_cfgs:
-        - uid: water
-        - uid: receiver
-        lower:
-        - -0.1
-        - -0.1
-        - 0.0045000000000000005
-        upper:
-        - 0.1
-        - 0.1
-        - 0.0675
-        sphere_center:
-        - 0
-        - 0
-        - 0.088
-        sphere_radius: 0.093
-      extra:
-        shape:
-        - 1
-  dataset: {}
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
deleted file mode 100644
index 2a1e9fa22..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.newton.yaml
+++ /dev/null
@@ -1,116 +0,0 @@
-environment_id: pour_cup
-physics: newton
-max_episodes: 1
-max_episode_steps: 1400
-num_envs: 1
-arena_space: 2.5
-
-physics_config:
-  physics_dt: 0.005
-  gravity: [0.0, 0.0, -9.81]
-  # Each 5 ms physics step contains fifty 0.1 ms MuJoCo solver steps.
-  num_substeps: 50
-  collision_cfg: null
-  solver_cfg:
-    solver_type: mujoco_warp
-    solver: newton
-    integrator: implicitfast
-    iterations: 50
-    ls_iterations: 100
-    cone: elliptic
-    impratio: 1000.0
-    use_mujoco_contacts: true
-    enable_multiccd: true
-
-simulation:
-  light:
-    direct:
-      - uid: main_light
-        light_type: sun
-        color: [1.0, 1.0, 1.0]
-        intensity: 4.0
-        direction: [-0.3, 0.2, -1.0]
-      - uid: fill_light
-        light_type: rect
-        color: [1.0, 1.0, 1.0]
-        intensity: 6.0
-        init_pos: [-0.5, -0.4, 1.2]
-        direction: [0.0, 0.0, -1.0]
-        rect_width: 0.7
-        rect_height: 0.7
-  background:
-    - uid: cup_platform
-      shape:
-        shape_type: Cube
-        size: [0.4, 0.5, 0.5]
-      body_type: static
-      init_pos: [-0.9, 0.0, 0.25]
-      attrs:
-        collision_props:
-          backend: newton
-          condim: 6
-        material_props:
-          backend: newton
-          dynamic_friction: 1.0
-          ke: 400000.0
-          kd: 1265.0
-          torsional_friction: 0.1
-          rolling_friction: 0.03
-  rigid_object:
-    - uid: cup
-      shape:
-        shape_type: Mesh
-        fpath: PourCupAssets/cup.glb
-        compute_uv: true
-        collision:
-          approximation: convex_decomposition
-          max_hulls: 32
-      body_type: dynamic
-      init_pos: [-0.75, 0.0, 0.552]
-      body_scale: [1.0, 1.0, 1.0]
-      attrs:
-        mass_props:
-          mass: 0.30
-        collision_props:
-          backend: newton
-          contact_offset: 0.0005
-          rest_offset: 0.0001
-          condim: 6
-        material_props:
-          backend: newton
-          dynamic_friction: 1.2
-          ke: 400000.0
-          kd: 1265.0
-          torsional_friction: 0.1
-          rolling_friction: 0.03
-  rigid_object_group: []
-  articulation: []
-
-env:
-  sim_steps_per_control: 10
-  control_parts: [arm, eef]
-  events:
-    settle_cup:
-      func: wait_for_dynamic_objects_to_settle
-      mode: reset
-      params:
-        entity_cfgs:
-          - uid: cup
-        min_steps: 10
-        max_steps: 120
-        check_interval_steps: 2
-        required_stable_checks: 3
-        timeout_behavior: raise
-    record_demo:
-      func: record_camera_data
-      mode: interval
-      interval_step: 1
-      params:
-        name: franka_newton_smooth_pour_mug
-        resolution: [1280, 960]
-        intrinsics: [1000.0, 1000.0, 640.0, 480.0]
-        eye: [-1.4, -1.5, 1.15]
-        target: [-0.4, 0.0, 0.45]
-        up: [0.0, 0.0, 1.0]
-        save_path: outputs/franka_pour_cup/videos
-  dataset: {}
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
deleted file mode 100644
index 001b92002..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/env.yaml
+++ /dev/null
@@ -1,90 +0,0 @@
-environment_id: pour_cup
-physics: default
-max_episodes: 1
-max_episode_steps: 1200
-num_envs: 1
-arena_space: 2.5
-
-physics_config:
-  physics_dt: 0.005
-  gravity: [0.0, 0.0, -9.81]
-  enable_ccd: true
-
-simulation:
-  light:
-    direct:
-      - uid: main_light
-        light_type: sun
-        color: [1.0, 1.0, 1.0]
-        intensity: 4.0
-        direction: [-0.3, 0.2, -1.0]
-      - uid: fill_light
-        light_type: rect
-        color: [1.0, 1.0, 1.0]
-        intensity: 6.0
-        init_pos: [-0.5, -0.4, 1.2]
-        direction: [0.0, 0.0, -1.0]
-        rect_width: 0.7
-        rect_height: 0.7
-  background: []
-  rigid_object:
-    - uid: cup
-      shape:
-        shape_type: Mesh
-        fpath: PourCupAssets/cup.glb
-        compute_uv: true
-        collision:
-          approximation: convex_decomposition
-          max_hulls: 32
-      body_type: dynamic
-      init_pos: [-0.485, -0.08, 0.052]
-      body_scale: [1.0, 1.0, 1.0]
-      attrs:
-        mass_props:
-          mass: 0.30
-        rigid_props:
-          linear_damping: 0.2
-          angular_damping: 0.2
-          min_position_iters: 64
-          min_velocity_iters: 16
-          max_depenetration_velocity: 1.0
-        collision_props:
-          contact_offset: 0.0005
-          rest_offset: 0.0001
-          torsional_patch_radius: 0.009
-          min_torsional_patch_radius: 0.008
-        material_props:
-          dynamic_friction: 1.2
-          static_friction: 1.2
-          restitution: 0.0
-  rigid_object_group: []
-  articulation: []
-
-env:
-  sim_steps_per_control: 10
-  control_parts: [arm, eef]
-  events:
-    settle_cup:
-      func: wait_for_dynamic_objects_to_settle
-      mode: reset
-      params:
-        entity_cfgs:
-          - uid: cup
-        min_steps: 10
-        max_steps: 120
-        check_interval_steps: 2
-        required_stable_checks: 3
-        timeout_behavior: raise
-    record_demo:
-      func: record_camera_data
-      mode: interval
-      interval_step: 1
-      params:
-        name: franka_side_pour_mug
-        resolution: [1280, 960]
-        intrinsics: [1000.0, 1000.0, 640.0, 480.0]
-        eye: [-1.1, -1.25, 0.85]
-        target: [-0.22, -0.02, 0.32]
-        up: [0.0, 0.0, 1.0]
-        save_path: outputs/franka_pour_cup/videos
-  dataset: {}
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml
deleted file mode 100644
index 2c159d783..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml
+++ /dev/null
@@ -1,26 +0,0 @@
-id: PourCup-Franka-Fluid-v1
-seed: 0
-enable_sensor: false
-environment:
-  component: env.fluid.yaml
-task_program:
-  program: task_program/program.fluid.yaml
-  integration: task_program/integration.fluid.yaml
-  execution_policy: ../../../../components/execution_policies/trajectory_open_loop_gentle.yaml
-embodiment:
-  component: ../../../../components/embodiments/franka_panda_firm_grip_newton.yaml
-  overrides:
-    init_rot:
-    - 0.0
-    - 0.0
-    - 180.0
-    init_qpos:
-    - 0.0023885604459792376
-    - -0.09648692607879639
-    - -0.0022385704796761274
-    - -2.388152599334717
-    - 0.00024301925441250205
-    - 3.862462282180786
-    - 0.7850000262260437
-    - 0.04
-    - 0.04
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml
deleted file mode 100644
index 675b375c2..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.newton.yaml
+++ /dev/null
@@ -1,26 +0,0 @@
-id: PourCup-Franka-Newton-v1
-seed: 0
-enable_sensor: false
-environment:
-  component: env.newton.yaml
-task_program:
-  program: task_program/program.newton.yaml
-  integration: task_program/integration.newton.yaml
-  execution_policy: ../../../../components/execution_policies/trajectory_open_loop_gentle.yaml
-embodiment:
-  component: ../../../../components/embodiments/franka_panda_firm_grip_newton.yaml
-  overrides:
-    init_rot:
-    - 0.0
-    - 0.0
-    - 180.0
-    init_qpos:
-    - 0.0023885604459792376
-    - -0.09648692607879639
-    - -0.0022385704796761274
-    - -2.388152599334717
-    - 0.00024301925441250205
-    - 3.862462282180786
-    - 0.7850000262260437
-    - 0.04
-    - 0.04
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml
deleted file mode 100644
index c4d180c8f..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.yaml
+++ /dev/null
@@ -1,16 +0,0 @@
-id: PourCup-Franka-v1
-seed: 0
-enable_sensor: false
-environment:
-  component: env.yaml
-task_program:
-  program: task_program/program.yaml
-  integration: task_program/integration.yaml
-  execution_policy: ../../../../components/execution_policies/trajectory_open_loop_pour_cup.yaml
-embodiment:
-  component: ../../../../components/embodiments/franka_panda_firm_grip.yaml
-  overrides:
-    init_rot:
-    - 0.0
-    - 0.0
-    - 180.0
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.fluid.yaml
deleted file mode 100644
index 9b247f142..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.fluid.yaml
+++ /dev/null
@@ -1,97 +0,0 @@
-integration_id: pour_cup_fluid_franka_v1
-program_id: pick_aim_pour_return_cup
-requires:
-  scene_contract: pour_cup_fluid_scene_v1
-  embodiment_contract: single_arm_parallel_gripper
-scene_binding:
-  contract_id: pour_cup_fluid_scene_v1
-  registry_id: task_program_pour_cup_fluid
-  rigid_objects:
-  - entity_id: cup
-    simulation_uid: cup
-    dynamics: dynamic
-    semantic_type: cup
-    affordances:
-    - entity_id: cup_grasp
-      kind: antipodal_grasp
-      internal_axis:
-      - 1.0
-      - 0.0
-      - 0.0
-  - entity_id: receiver
-    simulation_uid: receiver
-    dynamics: static
-    semantic_type: bowl
-profile:
-  defaults:
-    pick_up:
-      primary: primary_manipulator
-    pour:
-      primary: primary_manipulator
-    place:
-      primary: primary_manipulator
-    move_held_object:
-      primary: primary_manipulator
-  action_options:
-    pick:
-      kind: pick_up
-      hand_interp_steps: 20
-      grasp_settle_steps: 20
-      lift_height: 0.12
-      pre_grasp_distance: 0.08
-      approach_direction:
-      - -1.0
-      - 0.0
-      - 0.0
-      fixed_object_to_eef:
-      - 0.0
-      - 0.0
-      - -1.0
-      - 0.065
-      - 0.0
-      - 1.0
-      - 0.0
-      - 0.0
-      - 1.0
-      - 0.0
-      - 0.0
-      - 0.0
-      - 0.0
-      - 0.0
-      - 0.0
-      - 1.0
-    simulation.pour:
-      kind: pour
-      rotate_angle: 1.3962634015954636
-    place:
-      kind: place
-      hand_interp_steps: 20
-      release_settle_steps: 20
-      preserve_current_object_orientation: false
-    simulation.move_held_object:
-      kind: move_held_object
-  effect_monitors: {}
-runtime_services:
-  registered_semantic_lowerers:
-  - kind: move_held_object
-    target_id: receiver_pour_pose
-    reference_entity_id: receiver
-    relative_pose:
-    - 1.0
-    - 0.0
-    - 0.0
-    - 0.005
-    - 0.0
-    - 1.0
-    - 0.0
-    - 0.09
-    - 0.0
-    - 0.0
-    - 1.0
-    - 0.17
-    - 0.0
-    - 0.0
-    - 0.0
-    - 1.0
-  - kind: pour
-    object_id: cup
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.newton.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.newton.yaml
deleted file mode 100644
index 43db3b170..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.newton.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-integration_id: pour_cup_franka_newton_v1
-program_id: pick_pour_return_cup
-
-requires:
-  scene_contract: pour_cup_scene_v1
-  embodiment_contract: single_arm_parallel_gripper
-
-scene_binding:
-  contract_id: pour_cup_scene_v1
-  registry_id: task_program_pour_cup
-  rigid_objects:
-    - entity_id: cup
-      simulation_uid: cup
-      dynamics: dynamic
-      semantic_type: cup
-      affordances:
-        - entity_id: cup_grasp
-          kind: antipodal_grasp
-          internal_axis: [1.0, 0.0, 0.0]
-
-profile:
-  defaults:
-    pick_up:
-      primary: primary_manipulator
-    pour:
-      primary: primary_manipulator
-    place:
-      primary: primary_manipulator
-  action_options:
-    pick:
-      kind: pick_up
-      hand_interp_steps: 20
-      grasp_settle_steps: 20
-      lift_height: 0.12
-      pre_grasp_distance: 0.08
-      approach_direction: [-1.0, 0.0, 0.0]
-      fixed_object_to_eef:
-        - 0.0
-        - 0.0
-        - -1.0
-        - 0.065
-        - 0.0
-        - 1.0
-        - 0.0
-        - 0.0
-        - 1.0
-        - 0.0
-        - 0.0
-        - 0.0
-        - 0.0
-        - 0.0
-        - 0.0
-        - 1.0
-    simulation.pour:
-      kind: pour
-      rotate_angle: 1.3962634015954636
-    place:
-      kind: place
-      hand_interp_steps: 20
-      release_settle_steps: 20
-      preserve_current_object_orientation: false
-  effect_monitors: {}
-
-runtime_services:
-  registered_semantic_lowerers:
-    - kind: pour
-      object_id: cup
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.yaml
deleted file mode 100644
index 50308ae4f..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/integration.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-integration_id: pour_cup_franka_v1
-program_id: pick_pour_return_cup
-
-requires:
-  scene_contract: pour_cup_scene_v1
-  embodiment_contract: single_arm_parallel_gripper
-
-scene_binding:
-  contract_id: pour_cup_scene_v1
-  registry_id: task_program_pour_cup
-  rigid_objects:
-    - entity_id: cup
-      simulation_uid: cup
-      dynamics: dynamic
-      semantic_type: cup
-      affordances:
-        - entity_id: cup_grasp
-          kind: antipodal_grasp
-          internal_axis: [1.0, 0.0, 0.0]
-
-profile:
-  defaults:
-    pick_up:
-      primary: primary_manipulator
-    pour:
-      primary: primary_manipulator
-    place:
-      primary: primary_manipulator
-  action_options:
-    pick:
-      kind: pick_up
-      hand_interp_steps: 20
-      grasp_settle_steps: 20
-      lift_height: 0.20
-      pre_grasp_distance: 0.12
-      approach_direction: [-1.0, 0.0, 0.0]
-      fixed_object_to_eef:
-        - 0.0
-        - 0.0
-        - -1.0
-        - 0.065
-        - 0.0
-        - 1.0
-        - 0.0
-        - 0.0
-        - 1.0
-        - 0.0
-        - 0.0
-        - 0.0
-        - 0.0
-        - 0.0
-        - 0.0
-        - 1.0
-    simulation.pour:
-      kind: pour
-      rotate_angle: 1.3962634015954636
-    place:
-      kind: place
-      hand_interp_steps: 20
-      release_settle_steps: 20
-      preserve_current_object_orientation: false
-  effect_monitors: {}
-
-runtime_services:
-  registered_semantic_lowerers:
-    - kind: pour
-      object_id: cup
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.fluid.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.fluid.yaml
deleted file mode 100644
index 2aa890ee6..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.fluid.yaml
+++ /dev/null
@@ -1,96 +0,0 @@
-program_id: pick_aim_pour_return_cup
-targets:
-  cup_return_pose:
-    kind: cyclic_pose
-    values:
-    - position:
-      - -0.75
-      - 0.0
-      - 0.552
-      quaternion_xyzw:
-      - 0.0
-      - 0.0
-      - 0.0
-      - 1.0
-  cup_lift_pose:
-    kind: cyclic_pose
-    values:
-    - position:
-      - -0.75
-      - 0.0
-      - 0.672
-      quaternion_xyzw:
-      - 0.0
-      - 0.0
-      - 0.0
-      - 1.0
-  cup_pour_pose:
-    kind: cyclic_pose
-    values:
-    - position:
-      - -0.75
-      - -0.06
-      - 0.67
-      quaternion_xyzw:
-      - 0.0
-      - 0.0
-      - 0.0
-      - 1.0
-program:
-  kind: sequence
-  items:
-  - kind: segment
-    name: pick_cup
-    steps:
-      kind: invoke
-      call:
-        kind: pick
-        object: cup
-        grasp: cup_grasp
-    validators:
-    - kind: object_near_target
-      object: cup
-      target: cup_lift_pose
-      position_tolerance: 0.03
-  - kind: segment
-    name: aim_cup_above_bowl
-    steps:
-      kind: invoke
-      call:
-        kind: registered
-        call_id: simulation.move_held_object
-        arguments:
-          target: receiver_pour_pose
-    validators:
-    - kind: object_near_target
-      object: cup
-      target: cup_pour_pose
-      position_tolerance: 0.03
-  - kind: segment
-    name: pour_water
-    steps:
-      kind: invoke
-      call:
-        kind: registered
-        call_id: simulation.pour
-        arguments:
-          object: cup
-  - kind: segment
-    name: return_cup
-    steps:
-      kind: invoke
-      call:
-        kind: place
-        object: cup
-        at:
-          kind: target_ref
-          target: cup_return_pose
-    post:
-    - kind: wait_stable
-      entity: cup
-    validators:
-    - kind: object_near_target
-      object: cup
-      target: cup_return_pose
-      position_tolerance: 0.03
-instruction: Pour water from the cup into the bowl and return the cup to the table.
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.newton.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.newton.yaml
deleted file mode 100644
index 1f77d32e4..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.newton.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-program_id: pick_pour_return_cup
-targets:
-  cup_return_pose:
-    kind: cyclic_pose
-    values:
-      - position: [-0.75, 0.0, 0.552]
-        quaternion_xyzw: [0.0, 0.0, 0.0, 1.0]
-
-program:
-  kind: segment
-  name: pick_pour_return_cup
-  steps:
-    kind: sequence
-    items:
-      - kind: invoke
-        call:
-          kind: pick
-          object: cup
-          grasp: cup_grasp
-      - kind: invoke
-        call:
-          kind: registered
-          call_id: simulation.pour
-          arguments:
-            object: cup
-      - kind: invoke
-        call:
-          kind: place
-          object: cup
-          at:
-            kind: target_ref
-            target: cup_return_pose
-  post:
-    - kind: wait_stable
-      entity: cup
-  validators:
-    - kind: object_near_target
-      object: cup
-      target: cup_return_pose
-      position_tolerance: 0.03
diff --git a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.yaml b/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.yaml
deleted file mode 100644
index 18bf4cb97..000000000
--- a/embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task_program/program.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-program_id: pick_pour_return_cup
-targets:
-  cup_return_pose:
-    kind: cyclic_pose
-    values:
-      - position: [-0.485, -0.08, 0.052]
-        quaternion_xyzw: [0.0, 0.0, 0.0, 1.0]
-
-program:
-  kind: segment
-  name: pick_pour_return_cup
-  steps:
-    kind: sequence
-    items:
-      - kind: invoke
-        call:
-          kind: pick
-          object: cup
-          grasp: cup_grasp
-      - kind: invoke
-        call:
-          kind: registered
-          call_id: simulation.pour
-          arguments:
-            object: cup
-      - kind: invoke
-        call:
-          kind: place
-          object: cup
-          at:
-            kind: target_ref
-            target: cup_return_pose
-  post:
-    - kind: wait_stable
-      entity: cup
-  validators:
-    - kind: object_near_target
-      object: cup
-      target: cup_return_pose
-      position_tolerance: 0.03
diff --git a/scripts/tutorials/sim/create_fluid.py b/scripts/tutorials/sim/create_fluid.py
new file mode 100644
index 000000000..e73c1a11c
--- /dev/null
+++ b/scripts/tutorials/sim/create_fluid.py
@@ -0,0 +1,153 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Drop a procedural water block into a box using DexSim 0.5.1rc1 SPH.
+
+Run ``python scripts/tutorials/sim/create_fluid.py`` for native particles, or
+add ``--headless --renderer no-render`` for physics only. The scene uses five
+primitive walls and needs no task assets, robot, Gym or Task Program.
+"""
+
+from __future__ import annotations
+
+import argparse
+
+from embodichain.cli.sim import add_sim_args_to_parser
+
+__all__ = ["build_parser", "main"]
+
+
+def build_parser() -> argparse.ArgumentParser:
+    """Return simulation options without loading or initializing the engine."""
+    parser = argparse.ArgumentParser(description=__doc__)
+    add_sim_args_to_parser(parser)
+    parser.set_defaults(device="cuda", physics="newton", renderer="fast-rt")
+    parser.add_argument("--steps", type=int, default=480)
+    return parser
+
+
+def main(args: argparse.Namespace | None = None) -> None:
+    """Create, step, inspect and reset a single-world fluid.
+
+    Args:
+        args: Parsed options; None reads the command line.
+    """
+    parser = build_parser()
+    if args is None:
+        args = parser.parse_args()
+    if args.physics != "newton" or not str(args.device).startswith("cuda"):
+        parser.error("Fluids require --physics newton and a CUDA device.")
+    if args.num_envs != 1:
+        parser.error("This DexSim 0.5.1rc1 example requires --num-envs 1.")
+    if args.renderer not in {"fast-rt", "rt", "no-render"}:
+        parser.error("Choose --renderer fast-rt, rt or no-render for fluids.")
+    if args.steps <= 0:
+        parser.error("--steps must be positive.")
+
+    import torch
+    import trimesh
+
+    from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
+    from embodichain.lab.sim.cfg import (
+        FluidObjectCfg,
+        FluidRenderCfg,
+        FluidSpawnCfg,
+        NewtonPhysicsCfg,
+        RenderCfg,
+        RigidObjectCfg,
+    )
+    from embodichain.lab.sim.shapes import MeshCfg, MeshCollisionCfg
+    from embodichain.lab.visualization import visualization_cfg_from_args
+
+    spacing = 0.01
+    sim = SimulationManager(
+        SimulationManagerCfg(
+            device=args.device,
+            headless=args.headless,
+            gpu_id=args.gpu_id,
+            physics_dt=1 / 240,
+            render_cfg=RenderCfg(renderer=args.renderer),
+            physics_cfg=NewtonPhysicsCfg(
+                num_substeps=4,
+                collision_cfg=None,
+                solver_cfg={
+                    "solver_type": "sph",
+                    "sph_method": "dfsph",
+                    "particle_diameter": spacing,
+                    "particle_support_radius": 2.25 * spacing,
+                },
+            ),
+            visualization=visualization_cfg_from_args(args),
+        )
+    )
+    fluid = None
+    try:
+        # SPH queries texture SDFs; render boxes alone are not fluid boundaries.
+        for uid, position, size in (
+            ("floor", (0, 0, 0), (0.32, 0.32, 0.02)),
+            ("left", (-0.16, 0, 0.15), (0.02, 0.32, 0.3)),
+            ("right", (0.16, 0, 0.15), (0.02, 0.32, 0.3)),
+            ("front", (0, -0.16, 0.15), (0.32, 0.02, 0.3)),
+            ("back", (0, 0.16, 0.15), (0.32, 0.02, 0.3)),
+        ):
+            mesh = trimesh.creation.box(extents=size)
+            sim.add_rigid_object(
+                RigidObjectCfg(
+                    uid=uid,
+                    body_type="kinematic",
+                    init_pos=position,
+                    shape=MeshCfg(
+                        vertices=mesh.vertices,
+                        triangles=mesh.faces,
+                        collision=MeshCollisionCfg(approximation="sdf"),
+                    ),
+                )
+            )
+        fluid = sim.add_fluid_object(
+            FluidObjectCfg(
+                uid="water",
+                particle_spacing=spacing,
+                init_pos=(0, 0, 0.16),
+                spawn=FluidSpawnCfg(kind="box", size=(0.12, 0.12, 0.12)),
+                render=FluidRenderCfg(
+                    mode="none" if args.renderer == "no-render" else "particles"
+                ),
+            )
+        )
+        sim.prepare()
+        initial = fluid.data.default_particle_state_w
+        print(f"Created {fluid.num_particles} fluid particles.")
+        if not args.headless:
+            sim.open_window()
+        for _ in range(args.steps):
+            sim.update(step=1)
+        state = fluid.data.particle_state_w
+        if not bool(torch.isfinite(state).all()):
+            raise RuntimeError("Fluid state is not finite.")
+        print(
+            f"Mean particle height after stepping: {state[..., 2].mean().item():.3f} m"
+        )
+        fluid.reset()
+        torch.testing.assert_close(fluid.data.particle_state_w, initial)
+        print("Restored the initial fluid state without advancing physics.")
+    finally:
+        fluid = None
+        sim.destroy(exit_process=False)
+        SimulationManager.flush_cleanup_queue()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/tutorials/sim/franka_dfsph_pour_demo.py b/scripts/tutorials/sim/franka_dfsph_pour_demo.py
deleted file mode 100644
index fccc5733d..000000000
--- a/scripts/tutorials/sim/franka_dfsph_pour_demo.py
+++ /dev/null
@@ -1,309 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-"""Drive a live Franka grasp/pour/place in SimulationManager with one-way SPH.
-
-Atomic Actions plan controller targets from the current scene. MuJoCo solves
-joint drives, gravity and grasp contact; DFSPH reads the live cup boundary.
-The Gym counterpart uses a configured Task Program for the same physical scene.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-from pathlib import Path
-import time
-
-import imageio.v2 as imageio
-import numpy as np
-import torch
-
-from embodichain.lab.scripts.run_fluid_task_program import (
-    _configure,
-    _cup_qualification,
-)
-from embodichain.lab.sim import SimulationManager
-from embodichain.lab.sim.atomic_actions import (
-    AxisAlignAffordance,
-    ControlPartCommandProfile,
-    GraspGoal,
-    HeldObjectPoseGoal,
-    MoveHeldObjectOptions,
-    MotionPolicy,
-    ObjectSemantics,
-    PickUpOptions,
-    PlaceGoal,
-    PlaceOptions,
-    PourGoal,
-    PourOptions,
-    create_simulation_atomic_action_engine,
-)
-from embodichain.lab.sim.motion.motion_generator import MotionGenerator, MotionGenCfg
-from embodichain.lab.sim.motion.planners import ToppraPlannerCfg
-from embodichain.lab.sim.sensors import CameraCfg
-
-__all__ = ["main"]
-
-ROOT = Path(__file__).resolve().parents[3]
-TASK = (
-    ROOT
-    / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
-)
-
-
-def main() -> None:
-    """Plan fresh joint commands and solve the robot, cup and fluid online."""
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--output", type=Path, default=ROOT / "outputs/fluid-sim-online"
-    )
-    parser.add_argument("--spacing", type=float, default=0.0015)
-    parser.add_argument("--seconds", type=float)
-    parser.add_argument("--num-envs", type=int, default=1)
-    parser.add_argument(
-        "--renderer", choices=["fast-rt", "no-render"], default="fast-rt"
-    )
-    parser.add_argument("--headless", action="store_true")
-    args = parser.parse_args()
-    if args.seconds is not None and (
-        not np.isfinite(args.seconds) or args.seconds <= 0
-    ):
-        parser.error("seconds must be finite and positive")
-    output = args.output.resolve()
-    output.mkdir(parents=True, exist_ok=True)
-    _, cfg = _configure(
-        TASK,
-        num_envs=args.num_envs,
-        renderer=args.renderer,
-        output=output,
-        particle_spacing=args.spacing,
-    )
-    cfg.sim_cfg.num_envs = cfg.num_envs
-    sim = SimulationManager(cfg.sim_cfg)
-    writers = []
-    robot = cup = camera = engine = None
-    try:
-        robot = sim.add_robot(cfg.robot)
-        for object_cfg in [*cfg.background, *cfg.rigid_object]:
-            sim.add_rigid_object(object_cfg)
-        for fluid_cfg in cfg.fluid_object:
-            sim.add_fluid_object(fluid_cfg)
-        for light_cfg in cfg.light.direct:
-            sim.add_light(light_cfg)
-        sim.prepare()
-        robot.build_pk_serial_chain()
-        sim.reset_objects_state()
-        cup = sim.get_rigid_object("cup")
-        # Live gravity/contact settling before observing the planning scene.
-        sim.update(step=24, render_final_step=False)
-        initial_pose = cup.get_local_pose(to_matrix=True).clone()
-        fill_pose = cup.get_local_pose().clone()
-        fill_pose[:, :3] += sim.arena_offsets
-        sim.get_fluid_object("water").reset_to_pose(fill_pose)
-        object_to_eef = torch.tensor(
-            [[0, 0, -1, 0.065], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1]],
-            dtype=torch.float32,
-            device=sim.device,
-        )
-        semantics = ObjectSemantics(
-            entity_id="cup",
-            label="cup",
-            geometry={},
-            affordance=AxisAlignAffordance(
-                mesh_vertices=cup.get_vertices(env_ids=[0], scale=True)[0],
-                mesh_triangles=cup.get_triangles(env_ids=[0])[0],
-                internal_axis=torch.tensor([1.0, 0, 0], device=sim.device),
-            ),
-        )
-        motion = MotionGenerator(
-            MotionGenCfg(
-                planner_cfg=ToppraPlannerCfg(
-                    robot_uid=robot.uid, sim_instance_id=sim.instance_id
-                )
-            )
-        )
-        engine = create_simulation_atomic_action_engine(
-            motion,
-            (cup,),
-            control_profiles={
-                "eef": ControlPartCommandProfile.joint_positions(
-                    open=torch.tensor([0.04, 0.04], device=sim.device),
-                    grasp=torch.zeros(2, device=sim.device),
-                )
-            },
-        )
-        bindings = {"primary": {"motion": "arm", "grasp": "eef"}}
-        policy = MotionPolicy(
-            strategy="ik_interp", sample_count=360, dynamic_collision_mode="off"
-        )
-        receiver_pose = sim.get_rigid_object("receiver").get_local_pose(to_matrix=True)
-        receiver_to_cup = torch.eye(4, device=sim.device)
-        receiver_to_cup[:3, 3] = torch.tensor([0.005, 0.09, 0.17], device=sim.device)
-        pour_pose = receiver_pose @ receiver_to_cup
-        invocations = (
-            engine.make_invocation(
-                "pick_up",
-                GraspGoal(semantics),
-                control_parts=bindings,
-                motion_policy=policy,
-                skill_options=PickUpOptions(
-                    fixed_object_to_eef=object_to_eef,
-                    approach_direction=torch.tensor([-1.0, 0, 0], device=sim.device),
-                    pre_grasp_distance=0.08,
-                    lift_height=0.12,
-                    hand_interp_steps=20,
-                    grasp_settle_steps=20,
-                ),
-            ),
-            engine.make_invocation(
-                "move_held_object",
-                HeldObjectPoseGoal(pour_pose),
-                control_parts=bindings,
-                motion_policy=policy,
-                skill_options=MoveHeldObjectOptions(),
-            ),
-            engine.make_invocation(
-                "pour",
-                PourGoal(),
-                control_parts=bindings,
-                motion_policy=policy,
-                skill_options=PourOptions(rotate_angle=np.deg2rad(80)),
-            ),
-            engine.make_invocation(
-                "place",
-                PlaceGoal(initial_pose @ object_to_eef),
-                control_parts=bindings,
-                motion_policy=policy,
-                skill_options=PlaceOptions(
-                    hand_interp_steps=20, release_settle_steps=20
-                ),
-            ),
-        )
-        control_dt = cfg.sim_steps_per_control * sim.sim_config.physics_dt
-        plan = engine.compile(
-            invocations, engine.initial_context(control_dt=control_dt)
-        )
-        if not bool(plan.plan_success.all()):
-            raise RuntimeError("Atomic Action planning failed.")
-        if args.renderer != "no-render":
-            params = cfg.events.record_camera.params
-            camera = sim.add_sensor(
-                CameraCfg(
-                    uid="fluid_sim_record",
-                    width=params["resolution"][0],
-                    height=params["resolution"][1],
-                    intrinsics=params["intrinsics"],
-                    visualization_role="record",
-                    extrinsics=CameraCfg.ExtrinsicsCfg(
-                        eye=params["eye"], target=params["target"], up=params["up"]
-                    ),
-                )
-            )
-            writers = [
-                imageio.get_writer(
-                    output / f"fluid_sim_env{i}.mp4",
-                    fps=1 / control_dt,
-                    codec="libx264",
-                    quality=8,
-                    macro_block_size=1,
-                )
-                for i in range(sim.num_envs)
-            ]
-            if not args.headless:
-                sim.open_window()
-        positions = plan.trajectory.positions
-        poses = [cup.get_local_pose().cpu().numpy().copy()]
-        samples = []
-        started = time.perf_counter()
-        count = positions.shape[1] + round(2 / control_dt)
-        if args.seconds is not None:
-            count = min(count, int(np.ceil(args.seconds / control_dt)))
-        for i in range(count):
-            # Set targets only. Every measured joint/cup pose comes from physics.
-            robot.set_qpos(positions[:, min(i, positions.shape[1] - 1)], target=True)
-            sim.update(step=cfg.sim_steps_per_control, render_final_step=False)
-            poses.append(cup.get_local_pose().cpu().numpy().copy())
-            if camera is not None:
-                with sim.render_frame():
-                    sim.sync_render_state()
-                    camera.update()
-                    rgb = camera.get_data()["color"][..., :3].cpu().numpy()
-                for env_id, writer in enumerate(writers):
-                    writer.append_data(rgb[env_id])
-            if i % round(1 / control_dt) == 0 or i == count - 1:
-                fluid = sim.get_fluid_object("water")
-                state = fluid.data.particle_state_w
-                if not bool(torch.isfinite(state).all()):
-                    raise RuntimeError("Non-finite online fluid state.")
-                local = state[..., :3] - sim.arena_offsets[:, None, :]
-                receiver_pose = sim.get_rigid_object("receiver").get_local_pose(
-                    to_matrix=True
-                )
-                receiver = torch.bmm(
-                    local - receiver_pose[:, None, :3, 3], receiver_pose[:, :3, :3]
-                )
-                received = (
-                    (receiver[..., 2] > 0.0045)
-                    & (receiver[..., 2] < 0.0675)
-                    & (
-                        torch.linalg.vector_norm(
-                            receiver - torch.tensor([0, 0, 0.088], device=sim.device),
-                            dim=-1,
-                        )
-                        < 0.093
-                    )
-                )
-                sample = {
-                    "time_s": (i + 1) * control_dt,
-                    "cup_pose": poses[-1].tolist(),
-                    "received_ml": (received.sum(dim=1) * args.spacing**3 * 1e6)
-                    .cpu()
-                    .tolist(),
-                }
-                samples.append(sample)
-                print(json.dumps(sample), flush=True)
-        report = {
-            "coupling": "online one-way rigid-to-fluid",
-            "controller": "fresh Atomic Action joint targets",
-            "cup": _cup_qualification(poses),
-            "num_envs": sim.num_envs,
-            "particle_count_per_env": sim.get_fluid_object("water").num_particles,
-            "control_dt": control_dt,
-            "simulated_seconds": count * control_dt,
-            "wall_seconds": time.perf_counter() - started,
-            "samples": samples,
-        }
-        report["accepted"] = bool(
-            all(report["cup"]["accepted"])
-            and all(v >= 50 for v in samples[-1]["received_ml"])
-        )
-        (output / "validation.json").write_text(json.dumps(report, indent=2) + "\n")
-        print(json.dumps(report["cup"]), flush=True)
-        if args.seconds is None and not report["accepted"]:
-            raise RuntimeError(
-                "Online Sim pouring failed physical qualification; inspect validation.json."
-            )
-    finally:
-        for writer in writers:
-            writer.close()
-        robot = cup = camera = engine = None
-        sim.destroy(exit_process=False)
-        del sim
-        SimulationManager.flush_cleanup_queue()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/tests/data/assets/test_task_assets.py b/tests/data/assets/test_task_assets.py
deleted file mode 100644
index 65bdfce98..000000000
--- a/tests/data/assets/test_task_assets.py
+++ /dev/null
@@ -1,102 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-
-"""Task-asset descriptors, cache selection and public registry contracts."""
-
-from __future__ import annotations
-
-from argparse import Namespace
-from pathlib import Path
-
-import open3d as o3d
-import pytest
-
-from embodichain.data import download
-from embodichain.data.assets import task_assets
-from embodichain.data.dataset import EmbodiChainDataset, get_data_class
-
-pytestmark = pytest.mark.no_sim
-
-_HUB = "https://huggingface.co/datasets/DexForceAI/embodichain_data/resolve/main/"
-_ASSETS = (
-    ("PourCupAssets", "f97857b639189be4daf717a873ab84cf"),
-    ("PourCupDemoData", "8eb96cb756ac08efe403417f50ad2bf2"),
-)
-
-
-@pytest.mark.parametrize("name,md5", _ASSETS)
-@pytest.mark.parametrize(
-    "prefix", [_HUB, "https://hf-mirror.com/assets/", "https://example.test/"]
-)
-@pytest.mark.parametrize("custom_root", [False, True])
-def test_task_archive_descriptor_uses_configured_source_and_locked_cache(
-    name: str,
-    md5: str,
-    prefix: str,
-    custom_root: bool,
-    tmp_path: Path,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    calls = []
-
-    class DownloadIntercepted(Exception):
-        """Stop before the Open3D base performs network I/O."""
-
-    def initialize(
-        self: EmbodiChainDataset,
-        asset: str,
-        descriptor: o3d.data.DataDescriptor,
-        root: str,
-    ) -> None:
-        calls.append((asset, descriptor, root))
-        raise DownloadIntercepted
-
-    default = str(tmp_path / "default")
-    override = str(tmp_path / "custom") if custom_root else None
-    monkeypatch.setattr(task_assets, "EMBODICHAIN_DEFAULT_DATA_ROOT", default)
-    monkeypatch.setattr(task_assets, "EMBODICHAIN_DOWNLOAD_PREFIX", prefix)
-    monkeypatch.setattr(EmbodiChainDataset, "__init__", initialize)
-    with pytest.raises(DownloadIntercepted):
-        getattr(task_assets, name)(data_root=override)
-    assert len(calls) == 1
-    asset, descriptor, root = calls[0]
-    assert asset == name
-    assert root == (override or default)
-    relative = f"task_assets/{name}.zip"
-    assert descriptor.urls == [f"{prefix}{relative}"] + (
-        [] if prefix == _HUB else [f"{_HUB}{relative}"]
-    )
-    assert descriptor.md5 == md5
-
-
-@pytest.mark.parametrize("name,_", _ASSETS)
-def test_task_assets_are_shared_by_config_and_cli_registry(
-    name: str, _: str, monkeypatch: pytest.MonkeyPatch, capsys
-) -> None:
-    registry = download.get_registry()
-    cls = get_data_class(name)
-    assert dict(registry["task"])[name] is cls
-    assert all(not key.startswith("_") for key, _ in registry["task"])
-    download.cmd_list(Namespace(category="task"))
-    assert name in capsys.readouterr().out
-    calls = []
-    monkeypatch.setattr(
-        download,
-        "download_asset",
-        lambda key, value: calls.append((key, value)) or True,
-    )
-    download.cmd_download(Namespace(all=False, category=None, name=name.lower()))
-    assert calls == [(name.lower(), cls)]
diff --git a/tests/data/test_download_prefix.py b/tests/data/test_download_prefix.py
index 2eda5ea25..11422e10a 100644
--- a/tests/data/test_download_prefix.py
+++ b/tests/data/test_download_prefix.py
@@ -36,7 +36,7 @@
 _COLLECT_URLS = """
 import json
 from embodichain.data import dataset
-from embodichain.data.assets import CobotMagicArm, UnitreeGo2Locomotion, PourCupAssets, PourCupDemoData
+from embodichain.data.assets import CobotMagicArm, UnitreeGo2Locomotion
 
 urls = {}
 
@@ -48,7 +48,7 @@ def capture(self, prefix, descriptor, path):
     raise Captured  # stop before anything is downloaded
 
 dataset.EmbodiChainDataset.__init__ = capture
-for cls in (CobotMagicArm, UnitreeGo2Locomotion, PourCupAssets, PourCupDemoData):
+for cls in (CobotMagicArm, UnitreeGo2Locomotion):
     try:
         cls()
     except Captured:
@@ -107,11 +107,6 @@ def test_download_prefix_gets_a_trailing_slash(monkeypatch, reload_constants) ->
 def test_assets_download_from_the_configured_prefix(prefix: str) -> None:
     urls = _consumer_urls(prefix)
     assert urls["CobotMagicArm"] == [f"{MIRROR}robot_assets/CobotMagicArmV4.zip"]
-    for name in ("PourCupAssets", "PourCupDemoData"):
-        assert urls[name] == [
-            f"{MIRROR}task_assets/{name}.zip",
-            f"{HF_PREFIX}task_assets/{name}.zip",
-        ]
     # Locomotion tries the configured source first and keeps the Hub as fallback.
     assert urls["UnitreeGo2Locomotion"] == [
         f"{MIRROR}robot_assets/UnitreeGo2Locomotion.zip",
@@ -120,10 +115,8 @@ def test_assets_download_from_the_configured_prefix(prefix: str) -> None:
 
 
 @pytest.mark.no_sim
-def test_assets_do_not_repeat_the_hub_url() -> None:
+def test_locomotion_does_not_repeat_the_hub_url() -> None:
     urls = _consumer_urls(HF_PREFIX)
     assert urls["UnitreeGo2Locomotion"] == [
         f"{HF_PREFIX}robot_assets/UnitreeGo2Locomotion.zip"
     ]
-    for name in ("PourCupAssets", "PourCupDemoData"):
-        assert urls[name] == [f"{HF_PREFIX}task_assets/{name}.zip"]
diff --git a/tests/gym/envs/managers/test_record.py b/tests/gym/envs/managers/test_record.py
index 64c619c25..6b0f2fad2 100644
--- a/tests/gym/envs/managers/test_record.py
+++ b/tests/gym/envs/managers/test_record.py
@@ -16,13 +16,6 @@
 
 from __future__ import annotations
 
-from unittest.mock import Mock
-import numpy as np
-import pytest
-import torch
-
-import embodichain.lab.gym.envs.managers.record as record_module
-
 from embodichain.lab.gym.envs.managers import FunctorCfg
 from embodichain.lab.gym.envs.managers.record import record_camera_data
 from embodichain.lab.sim.sensors import CameraCfg
@@ -31,15 +24,6 @@
 class _CameraHandle:
     group_id = 7
 
-    def __init__(self) -> None:
-        self.rgb = torch.zeros(2, 4, 6, 4, dtype=torch.uint8)
-
-    def update(self, **kwargs) -> None:
-        pass
-
-    def get_data(self) -> dict:
-        return {"color": self.rgb}
-
 
 class _Simulation:
     def __init__(self) -> None:
@@ -50,13 +34,8 @@ def add_sensor(self, sensor_cfg: CameraCfg) -> _CameraHandle:
         self.sensor_cfg = sensor_cfg
         return self.camera
 
-    def render_camera_group(self, group_ids: list[int]) -> None:
-        assert group_ids == [self.camera.group_id]
-
 
 class _Environment:
-    control_frequency = 24.0
-
     def __init__(self) -> None:
         self.sim = _Simulation()
         self.camera_group_ids: list[int] = []
@@ -73,131 +52,3 @@ def test_record_camera_is_marked_for_visualization() -> None:
 
     assert env.sim.sensor_cfg is not None
     assert env.sim.sensor_cfg.visualization_role == "record"
-
-
-def _make_recorder(**params):
-    env = _Environment()
-    params = {"name": "views", **params}
-    cfg = FunctorCfg(func=record_camera_data, params=params)
-    return env, record_camera_data(cfg, env)
-
-
-def test_per_environment_capture_copies_views_and_saves_at_control_frequency(
-    monkeypatch,
-):
-    env, recorder = _make_recorder(per_env=True, fps=None)
-    # Distinct pixels identify the arena view in each saved video.
-    env.sim.camera.rgb[0, ..., :3] = 11
-    env.sim.camera.rgb[1, ..., :3] = 71
-    recorder(env, None, name="views", per_env=True, fps=None)
-    env.sim.camera.rgb.fill_(0)  # Camera buffers are reused after acquisition.
-    save_video = Mock()
-    monkeypatch.setattr(record_module, "images_to_video", save_video)
-
-    recorder.save_and_clear()
-    recorder.finalize()
-    assert save_video.call_count == 2
-    for env_id, (expected, call) in enumerate(zip((11, 71), save_video.call_args_list)):
-        assert call.args[2] == f"episode_0_views_env{env_id}"
-        np.testing.assert_array_equal(call.args[0][0], np.full((4, 6, 3), expected))
-        assert call.kwargs["fps"] == 24.0
-
-
-def test_per_environment_reset_and_discard_preserve_unselected_recordings(monkeypatch):
-    env, recorder = _make_recorder(per_env=True)
-    save_video = Mock()
-    monkeypatch.setattr(record_module, "images_to_video", save_video)
-    recorder(env, None, name="views")
-    recorder.save_and_clear(torch.tensor([0]))
-    recorder(env, None, name="views")
-    recorder.discard_and_clear(torch.tensor([0]))
-    recorder.save_and_clear(torch.tensor([1]))
-    assert [
-        (call.args[2], len(call.args[0])) for call in save_video.call_args_list
-    ] == [("episode_0_views_env0", 1), ("episode_0_views_env1", 2)]
-    recorder(env, None, name="views")
-    recorder.save_and_clear(torch.tensor([0]))
-    assert save_video.call_args.args[2] == "episode_1_views_env0"
-    recorder.finalize()
-    assert not recorder._env_frames[1]
-
-
-@pytest.mark.parametrize(
-    ("env_ids", "max_env_num", "expected_env_ids"),
-    [
-        (None, 3, [0, 1, 2]),
-        ([2, 0], 3, [2, 0]),
-        ([2, 0], 2, [0]),
-        ([], 3, []),
-        ([2], 2, []),
-    ],
-)
-def test_per_environment_capture_honors_event_rows_and_recording_limit(
-    env_ids, max_env_num, expected_env_ids, monkeypatch
-):
-    env, recorder = _make_recorder(per_env=True)
-    # Pixel values identify original arena IDs even for reordered selections.
-    env.sim.camera.rgb = torch.stack(
-        [torch.full((4, 6, 4), env_id, dtype=torch.uint8) for env_id in range(3)]
-    )
-    selection = None if env_ids is None else torch.tensor(env_ids, dtype=torch.long)
-    recorder(env, selection, name="views", max_env_num=max_env_num)
-    save_video = Mock()
-    monkeypatch.setattr(record_module, "images_to_video", save_video)
-
-    recorder.save_and_clear()
-
-    assert [call.args[2] for call in save_video.call_args_list] == [
-        f"episode_0_views_env{env_id}" for env_id in expected_env_ids
-    ]
-    for env_id, call in zip(expected_env_ids, save_video.call_args_list):
-        np.testing.assert_array_equal(call.args[0][0], np.full((4, 6, 3), env_id))
-
-
-def test_per_environment_capture_preserves_frames_for_rows_not_due(monkeypatch):
-    env, recorder = _make_recorder(per_env=True)
-    save_video = Mock()
-    monkeypatch.setattr(record_module, "images_to_video", save_video)
-    recorder(env, None, name="views")
-    # After a partial reset, per-row event intervals can be out of phase.
-    recorder(env, torch.tensor([1]), name="views")
-
-    recorder.save_and_clear()
-
-    assert [
-        (call.args[2], len(call.args[0])) for call in save_video.call_args_list
-    ] == [("episode_0_views_env0", 1), ("episode_0_views_env1", 2)]
-
-
-def test_failed_per_environment_save_keeps_frames_for_retry(monkeypatch):
-    env, recorder = _make_recorder(per_env=True)
-    recorder(env, None, name="views")
-    save_video = Mock(side_effect=OSError("disk full"))
-    monkeypatch.setattr(record_module, "images_to_video", save_video)
-    with pytest.raises(OSError, match="disk full"):
-        recorder.save_and_clear(torch.tensor([1]))
-    save_video.side_effect = None
-    recorder.save_and_clear(torch.tensor([1]))
-    assert save_video.call_args.args[2] == "episode_0_views_env1"
-    assert len(save_video.call_args.args[0]) == 1
-
-
-def test_camera_recording_honors_explicit_environment_limit(monkeypatch):
-    env, recorder = _make_recorder(per_env=True)
-    env.sim.camera.rgb = torch.zeros(17, 4, 6, 4, dtype=torch.uint8)
-    recorder(env, None, name="views", max_env_num=17)
-    save_video = Mock()
-    monkeypatch.setattr(record_module, "images_to_video", save_video)
-    recorder.save_and_clear()
-    assert save_video.call_count == 17
-    env, recorder = _make_recorder()
-    recorder(env, None, name="views", max_env_num=1)
-    assert recorder._frames[0].shape == (4, 6, 3)
-
-
-@pytest.mark.parametrize("fps", [0, -1, float("nan"), float("inf")])
-def test_invalid_recording_fps_fails_before_camera_creation(fps):
-    env = _Environment()
-    with pytest.raises(ValueError, match="fps"):
-        record_camera_data(FunctorCfg(params={"fps": fps}), env)
-    assert env.sim.sensor_cfg is None
diff --git a/tests/gym/envs/test_fluid_integration.py b/tests/gym/envs/test_fluid_integration.py
deleted file mode 100644
index d491abb59..000000000
--- a/tests/gym/envs/test_fluid_integration.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-
-"""Fluid Gym configuration, measurement and replay acceptance contracts."""
-
-from __future__ import annotations
-
-from pathlib import Path
-from types import SimpleNamespace
-import numpy as np
-import pytest
-import torch
-from embodichain.lab.gym.utils.gym_utils import config_to_cfg
-from embodichain.utils.utility import load_config
-from embodichain.lab.gym.envs.managers.cfg import SceneEntityCfg
-from embodichain.lab.gym.envs.managers.observations import (
-    get_fluid_summary,
-    get_fluid_volume_in_region,
-)
-from embodichain.lab.sim.cfg import FluidObjectCfg
-
-pytestmark = pytest.mark.no_sim
-ROOT = Path(__file__).resolve().parents[3]
-TASK = (
-    ROOT
-    / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
-)
-
-
-def _replay_cfg():
-    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
-    cfg.task_program = None
-    cup = cfg.rigid_object.pop(0)
-    cup.body_type = "kinematic"
-    cfg.background.append(cup)
-    cfg.sim_cfg.physics_cfg.num_substeps = 1
-    cfg.sim_cfg.physics_cfg.solver_cfg.pop("rigid_solver_cfg", None)
-    return cfg
-
-
-@pytest.fixture(autouse=True)
-def task_mesh_cache(tmp_path, monkeypatch):
-    """Resolve task meshes locally so configuration tests never download them."""
-    from types import SimpleNamespace
-    from embodichain.data import dataset
-
-    directory = tmp_path / "PourCupAssets"
-    directory.mkdir()
-    for name in (
-        "cup.glb",
-        "mug_wall.obj",
-        "receiver_wall.obj",
-        "fluid_table.obj",
-        "fluid_floor.obj",
-    ):
-        (directory / name).touch()
-    original = dataset.get_data_class
-
-    def resolve(name, extra_modules=None):
-        if name == "PourCupAssets":
-            return lambda: SimpleNamespace(extract_dir=str(directory))
-        return original(name, extra_modules)
-
-    monkeypatch.setattr(dataset, "get_data_class", resolve)
-    return directory
-
-
-def test_fluid_deployment_resolves_sources_and_observation_entities():
-    cfg = config_to_cfg(load_config(TASK), source_path=TASK)
-    assert cfg.sim_cfg.render_cfg.renderer == "fast-rt"
-    assert cfg.events.record_camera.params["per_env"] is True
-    assert cfg.events.record_camera.params["fps"] is None
-    assert cfg.events.record_camera.interval_step == 1
-    assert len(cfg.fluid_object) == 1
-    fluid = cfg.fluid_object[0]
-    assert isinstance(fluid, FluidObjectCfg)
-    assert fluid.spawn.kind == "revolved_volume"
-    assert fluid.spawn.file_path is None
-    assert fluid.spawn.sample(fluid.particle_spacing).shape == (76607, 3)
-    assert cfg.observations.received_volume.extra["shape"] == [1]
-    assert all(
-        isinstance(item, SceneEntityCfg)
-        for item in cfg.observations.received_volume.params["entity_cfgs"]
-    )
-    cup = next(item for item in cfg.rigid_object if item.uid == "cup")
-    assert Path(cup.particle_collision_shape.fpath).is_absolute()
-    assert cup.body_type == "dynamic"
-    assert cfg.task_program is not None
-
-
-def test_fluid_component_rejects_duplicate_inline_ownership():
-    config = load_config(TASK)
-    config["fluid_object"] = []
-    with pytest.raises(ValueError, match="owns|duplicate|must not"):
-        config_to_cfg(config, source_path=TASK)
-
-
-def test_region_volume_uses_reference_pose_and_keeps_state_unchanged():
-    state = torch.tensor(
-        [
-            [[1.0, 2.0, 3.0, 0.0, 0.0, 0.0], [1.5, 2.0, 3.0, 0.0, 0.0, 0.0]],
-            [[10.0, 2.0, 3.0, 0.0, 0.0, 0.0], [10.5, 2.0, 3.0, 0.0, 0.0, 0.0]],
-        ]
-    )
-    poses = torch.tensor(
-        [[1.0, 2.0, 3.0, 0.0, 0.0, 0.0, 1.0], [1.0, 2.0, 3.0, 0.0, 0.0, 0.0, 1.0]]
-    )
-    fluid = SimpleNamespace(
-        data=SimpleNamespace(particle_state_w=state, particle_pos_w=state[..., :3]),
-        cfg=SimpleNamespace(particle_spacing=0.1),
-        particle_mass=0.001,
-        num_particles=2,
-        nominal_volume=0.002,
-    )
-    rigid = SimpleNamespace(get_local_pose=lambda: poses)
-    env = SimpleNamespace(
-        num_envs=2,
-        device=torch.device("cpu"),
-        sim=SimpleNamespace(
-            get_fluid_object=lambda _: fluid,
-            get_rigid_object=lambda _: rigid,
-            arena_offsets=torch.tensor([[0.0, 0, 0], [9.0, 0, 0]]),
-        ),
-    )
-    before = state.clone()
-    volume = get_fluid_volume_in_region(
-        env,
-        {},
-        [SceneEntityCfg(uid="water"), SceneEntityCfg(uid="receiver")],
-        [-0.1] * 3,
-        [0.1] * 3,
-    )
-    torch.testing.assert_close(volume, torch.full((2, 1), 0.001))
-    torch.testing.assert_close(
-        get_fluid_summary(env, {}, SceneEntityCfg(uid="water"))[:, 4], torch.ones(2)
-    )
-    torch.testing.assert_close(state, before)
-
-
-def test_fluid_replay_rejects_unqualified_recording_before_world_creation():
-    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
-    from tensordict import TensorDict
-
-    cfg = _replay_cfg()
-    source = {
-        "meta": {"num_envs": 1, "num_steps": 2, "lengths": [2]},
-        "states": TensorDict({}, batch_size=[1, 2]),
-        "actions": torch.zeros(1, 2, 9),
-    }
-    with pytest.raises(ValueError, match="Task Program"):
-        FluidReplayEnv(cfg, trajectory=source)
-
-
-def _qualified_source(root_position=(0.0, 0.0, 0.0)):
-    from tensordict import TensorDict
-
-    root = torch.tensor([*root_position, 0.0, 0.0, 0.0, 1.0]).repeat(1, 2, 1)
-    cup = torch.tensor([-0.75, 0.0, 0.552, 0.0, 0.0, 0.0, 1.0]).repeat(1, 2, 1)
-    return {
-        "meta": {
-            "num_envs": 1,
-            "num_steps": 2,
-            "lengths": [2],
-            "dt": 0.05,
-            "rigid_object_uids": ["cup"],
-            "fluid_source": {
-                "kind": "task_program",
-                "accepted": True,
-                "program_hash": "test-program",
-            },
-        },
-        "states": TensorDict(
-            {
-                "robot": TensorDict(
-                    {"qpos": torch.zeros(1, 2, 9), "root_pose": root}, batch_size=[1, 2]
-                ),
-                "rigid_objects": TensorDict(
-                    {"cup": TensorDict({"pose": cup}, batch_size=[1, 2])},
-                    batch_size=[1, 2],
-                ),
-            },
-            batch_size=[1, 2],
-        ),
-        "actions": torch.zeros(1, 2, 9),
-    }
-
-
-def test_replay_adopts_source_root_before_scene_creation(monkeypatch):
-    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
-    from embodichain.lab.gym.envs.embodied_env import EmbodiedEnv
-
-    cfg = _replay_cfg()
-    cfg.dataset = {}  # Empty mapping is a supported Python configuration.
-    captured = []
-
-    def initialize(self, cfg, **kwargs):
-        captured.append(cfg)
-        self.robot = SimpleNamespace(dof=9)
-        self.sim = SimpleNamespace(physics=SimpleNamespace(solver_type="sph"))
-
-    monkeypatch.setattr(EmbodiedEnv, "__init__", initialize)
-    env = FluidReplayEnv(cfg, trajectory=_qualified_source((1.0, 2.0, 3.0)))
-    assert len(env._replay_time) == cfg.num_envs == 2
-    np.testing.assert_array_equal(captured[0].robot.init_local_pose[:3, 3], [1, 2, 3])
-
-
-@pytest.mark.parametrize("kind", ["trajectory", "actions", "dataset"])
-def test_replay_rejects_misleading_policy_recording(kind):
-    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
-
-    cfg = _replay_cfg()
-    if kind == "trajectory":
-        cfg.record_trajectory = True
-    elif kind == "actions":
-        cfg.actions = SimpleNamespace(control="unused")
-    else:
-        cfg.dataset = SimpleNamespace(recorder="unused")
-    with pytest.raises(ValueError, match="recording"):
-        FluidReplayEnv(cfg, trajectory=_qualified_source())
-
-
-def test_replay_rejects_moving_root_before_scene_creation():
-    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
-
-    cfg = _replay_cfg()
-    source = _qualified_source()
-    source["states"]["robot", "root_pose"][0, 1, 0] = 0.2
-    with pytest.raises(ValueError, match="fixed-root"):
-        FluidReplayEnv(cfg, trajectory=source)
-
-
-def test_replay_exhaustion_requires_reset():
-    from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
-
-    env = object.__new__(FluidReplayEnv)
-    env._replay_time = np.array([2.0, 2.1])
-    env.replay_duration = 2.0
-    with pytest.raises(RuntimeError, match="reset"):
-        env.step(None)
-
-
-def test_refill_transforms_selected_container_pose_into_world_frame():
-    from embodichain.lab.gym.envs.managers.events import reset_fluid_in_container
-
-    recorded = []
-    fluid = SimpleNamespace(
-        cfg=FluidObjectCfg(uid="water", init_pos=(0, 0, 0.1)),
-        reset_to_pose=lambda pose, ids: recorded.append((pose, ids)),
-    )
-    poses = torch.eye(4).repeat(2, 1, 1)
-    poses[:, :3, 3] = torch.tensor([1.0, 2.0, 3.0])
-    env = SimpleNamespace(
-        device=torch.device("cpu"),
-        sim=SimpleNamespace(
-            get_fluid_object=lambda _: fluid,
-            get_rigid_object=lambda _: SimpleNamespace(
-                get_local_pose=lambda **_: poses
-            ),
-            arena_offsets=torch.tensor([[0.0, 0, 0], [9.0, 0, 0]]),
-        ),
-    )
-    reset_fluid_in_container(
-        env, [1], [SceneEntityCfg(uid="water"), SceneEntityCfg(uid="cup")]
-    )
-    pose, ids = recorded[0]
-    torch.testing.assert_close(pose[:, :3], torch.tensor([[10.0, 2.0, 3.1]]))
-    assert ids.tolist() == [1]
diff --git a/tests/gym/envs/test_fluid_replay_runtime.py b/tests/gym/envs/test_fluid_replay_runtime.py
deleted file mode 100644
index 838e1628a..000000000
--- a/tests/gym/envs/test_fluid_replay_runtime.py
+++ /dev/null
@@ -1,178 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-
-"""Live Gym reset and exhaustion with a small synthetic source recording."""
-
-from __future__ import annotations
-
-from pathlib import Path
-from unittest.mock import patch
-import subprocess
-import sys
-import numpy as np
-import pytest
-import torch
-from tensordict import TensorDict
-from embodichain.lab.gym.envs.fluid_replay import FluidReplayEnv
-from embodichain.lab.gym.utils.gym_utils import config_to_cfg
-from embodichain.lab.sim import SimulationManager
-from embodichain.lab.sim.cfg import FluidSpawnCfg, FluidRenderCfg, RenderCfg
-from embodichain.utils.utility import load_config
-
-pytestmark = [pytest.mark.gpu, pytest.mark.subprocess_sim]
-
-
-class _FluidReplayRuntime:
-    def setup_method(self):
-        from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
-
-        if not getattr(ensure_solver_available("sph"), "supports_world_reset", False):
-            pytest.skip("requires world-isolated SPH build")
-        task = (
-            Path(__file__).resolve().parents[3]
-            / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
-        )
-        cfg = config_to_cfg(load_config(task), source_path=task)
-        cfg.task_program = None
-        cup_cfg = cfg.rigid_object.pop(0)
-        cup_cfg.body_type = "kinematic"
-        cfg.background.append(cup_cfg)
-        cfg.events.settle_cup = None
-        cfg.sim_cfg.physics_cfg.num_substeps = 1
-        cfg.sim_cfg.physics_cfg.solver_cfg.pop("rigid_solver_cfg", None)
-        cfg.sim_cfg.render_cfg = RenderCfg(renderer="no-render")
-        cfg.sim_cfg.headless = True
-        cfg.enable_sensor = False
-        cfg.events.record_camera = None
-        cfg.sim_steps_per_control = 1
-        cfg.sim_cfg.physics_dt = 0.001
-        solver = cfg.sim_cfg.physics_cfg.solver_cfg
-        solver.update(
-            volume_map_boundary_enabled=False,
-            sdf_boundary_enabled=False,
-            particle_support_radius=0.0225,
-        )
-        for obj in cfg.background:
-            for shape in (obj.shape, obj.particle_collision_shape):
-                if shape is not None and getattr(shape, "collision", None) is not None:
-                    shape.collision.sdf_resolution = 16
-        fluid = cfg.fluid_object[0]
-        fluid.particle_spacing = 0.01
-        fluid.particle_radius = 0.003
-        fluid.spawn = FluidSpawnCfg(
-            kind="revolved_volume",
-            profile=((-0.01, 0.01), (0.01, 0.01)),
-            fill_level=0.8,
-            reference_uid="cup",
-        )
-        fluid.render = FluidRenderCfg(mode="none")
-        root = torch.tensor([0, 0, 0, 0, 0, 0, 1.0]).repeat(1, 2, 1)
-        cup = torch.tensor([-0.75, 0, 0.552, 0, 0, 0, 1.0]).repeat(1, 2, 1)
-        qpos = torch.tensor(cfg.robot.init_qpos).reshape(1, 1, -1).repeat(1, 2, 1)
-        # Synthetic provenance exercises the replay contract, not source acceptance.
-        source = {
-            "meta": {
-                "num_envs": 1,
-                "num_steps": 2,
-                "lengths": [2],
-                "dt": 0.004,
-                "rigid_object_uids": ["cup"],
-                "fluid_source": {
-                    "kind": "task_program",
-                    "accepted": True,
-                    "program_hash": "synthetic-test",
-                },
-            },
-            "states": TensorDict(
-                {
-                    "robot": {"qpos": qpos, "root_pose": root},
-                    "rigid_objects": {"cup": {"pose": cup}},
-                },
-                batch_size=[1, 2],
-            ),
-            "actions": torch.zeros(1, 2, qpos.shape[-1]),
-        }
-        self.env = FluidReplayEnv(cfg, trajectory=source, speed=1.0, hold_seconds=0.0)
-
-    def teardown_method(self):
-        if hasattr(self, "env"):
-            self.env.close(exit_process=False)
-            del self.env
-        SimulationManager.flush_cleanup_queue()
-
-    def test_partial_reset_preserves_other_world_then_finishes_independently(self):
-        env = self.env
-        with patch.object(
-            FluidSpawnCfg,
-            "sample",
-            side_effect=AssertionError("reset must reuse memory"),
-        ):
-            env.reset(seed=0)
-        action = torch.zeros(
-            (env.num_envs, len(env.active_joint_ids)), device=env.device
-        )
-        env.step(action)
-        env.step(action)
-        before = env.sim.get_fluid_object("water").data.particle_state_w.clone()
-        with patch.object(
-            FluidSpawnCfg,
-            "sample",
-            side_effect=AssertionError("reset must reuse memory"),
-        ):
-            env.reset(
-                options={
-                    "reset_ids": torch.tensor([0], device=env.device),
-                    "save_data": False,
-                }
-            )
-        torch.testing.assert_close(
-            env.sim.get_fluid_object("water").data.particle_state_w[1],
-            before[1],
-            rtol=0,
-            atol=0,
-        )
-        np.testing.assert_allclose(env._replay_time, [0, 0.002])
-        env.step(action)
-        _, _, _, truncated, info = env.step(action)
-        assert info["fluid_replay_finished"].tolist() == [False, True]
-        assert truncated.tolist() == [False, True]
-        env.step(action)
-        env.step(action)
-        with pytest.raises(RuntimeError, match="exhausted"):
-            env.step(action)
-
-
-def test_fluid_replay_reset_and_exhaustion_in_fresh_process():
-    from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
-
-    if not getattr(ensure_solver_available("sph"), "supports_world_reset", False):
-        pytest.skip("requires world-isolated SPH build")
-    result = subprocess.run(
-        [sys.executable, str(Path(__file__).resolve())],
-        capture_output=True,
-        text=True,
-        timeout=180,
-    )
-    assert result.returncode == 0, result.stdout + result.stderr
-
-
-if __name__ == "__main__":
-    worker = _FluidReplayRuntime()
-    try:
-        worker.setup_method()
-        worker.test_partial_reset_preserves_other_world_then_finishes_independently()
-    finally:
-        worker.teardown_method()
diff --git a/tests/gym/envs/test_official_task_layout.py b/tests/gym/envs/test_official_task_layout.py
index 3fd6a43c1..b655d9b53 100644
--- a/tests/gym/envs/test_official_task_layout.py
+++ b/tests/gym/envs/test_official_task_layout.py
@@ -58,7 +58,6 @@
     "blocks_ranking_size",
     "match_object_container",
     "place_object_drawer",
-    "pour_cup",
     "pour_water",
     "scoop_ice",
     "stack_blocks_two",
diff --git a/tests/gym/utils/test_gym_utils.py b/tests/gym/utils/test_gym_utils.py
index 9de58250f..68c5e1ab8 100644
--- a/tests/gym/utils/test_gym_utils.py
+++ b/tests/gym/utils/test_gym_utils.py
@@ -2554,40 +2554,3 @@ def test_launcher_can_explicitly_disable_configured_headless() -> None:
     args = parser.parse_args(["--gym_config", "task.yaml", "--no-headless"])
     merged = merge_args_with_gym_config(args, {"physics": "default", "headless": True})
     assert merged["headless"] is False
-
-
-@pytest.mark.parametrize(
-    "collection", ["background", "rigid_object", "rigid_object_group"]
-)
-def test_mesh_asset_paths_resolve_collision_and_visual_geometry(
-    collection: str, tmp_path: Path, monkeypatch: pytest.MonkeyPatch
-) -> None:
-    """Separate render meshes use the same asset resolver as collision meshes."""
-    paths = {
-        "TaskMeshes/collision.obj": str(tmp_path / "collision.obj"),
-        "TaskMeshes/visual.glb": str(tmp_path / "visual.glb"),
-    }
-    monkeypatch.setattr("embodichain.data.get_data_path", paths.__getitem__)
-    obj = {
-        "uid": "cup",
-        "shape": {"shape_type": "Mesh", "fpath": "TaskMeshes/collision.obj"},
-        "visual_shape": {"shape_type": "Mesh", "fpath": "TaskMeshes/visual.glb"},
-    }
-    entries = [obj]
-    if collection == "rigid_object_group":
-        entries = [{"uid": "cups", "rigid_objects": {"cup": obj}}]
-    cfg = config_to_cfg(
-        {
-            "id": "EmbodiedEnv-v1",
-            "physics": "default",
-            "env": {},
-            "robot": {"uid": "robot"},
-            collection: entries,
-        },
-        manager_modules=DEFAULT_MANAGER_MODULES,
-    )
-    parsed = getattr(cfg, collection)[0]
-    if collection == "rigid_object_group":
-        parsed = parsed.rigid_objects["cup"]
-    assert parsed.shape.fpath == paths["TaskMeshes/collision.obj"]
-    assert parsed.visual_shape.fpath == paths["TaskMeshes/visual.glb"]
diff --git a/tests/lab/scripts/test_run_fluid_task_program.py b/tests/lab/scripts/test_run_fluid_task_program.py
deleted file mode 100644
index 5fa46f360..000000000
--- a/tests/lab/scripts/test_run_fluid_task_program.py
+++ /dev/null
@@ -1,162 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-
-"""Online fluid deployment and measured physical qualification."""
-
-from __future__ import annotations
-
-from pathlib import Path
-import numpy as np
-import pytest
-
-from embodichain.lab.scripts.run_fluid_task_program import (
-    _configure,
-    _cup_qualification,
-)
-
-pytestmark = pytest.mark.no_sim
-ROOT = Path(__file__).resolve().parents[3]
-TASK = (
-    ROOT
-    / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
-)
-
-
-@pytest.fixture(autouse=True)
-def task_mesh_cache(tmp_path, monkeypatch):
-    """Resolve task meshes locally so configuration tests never download them."""
-    from types import SimpleNamespace
-    from embodichain.data import dataset
-
-    directory = tmp_path / "PourCupAssets"
-    directory.mkdir()
-    for name in (
-        "cup.glb",
-        "mug_wall.obj",
-        "receiver_wall.obj",
-        "fluid_table.obj",
-        "fluid_floor.obj",
-    ):
-        (directory / name).touch()
-    original = dataset.get_data_class
-
-    def resolve(name, extra_modules=None):
-        if name == "PourCupAssets":
-            return lambda: SimpleNamespace(extract_dir=str(directory))
-        return original(name, extra_modules)
-
-    monkeypatch.setattr(dataset, "get_data_class", resolve)
-    return directory
-
-
-def test_online_task_uses_live_grasp_and_same_body_particle_boundary(tmp_path):
-    task_id, cfg = _configure(TASK, num_envs=2, renderer="fast-rt", output=tmp_path)
-    assert task_id == "PourCup-Franka-Fluid-v1"
-    assert cfg.task_program is not None
-    cup = next(c for c in cfg.rigid_object if c.uid == "cup")
-    assert cup.body_type == "dynamic"
-    assert Path(cup.particle_collision_shape.fpath).is_absolute()
-    assert cup.particle_collision_shape.collision.approximation == "sdf"
-    assert cup.shape.collision.approximation == "convex_decomposition"
-    solver = cfg.sim_cfg.physics_cfg.solver_cfg
-    assert solver["rigid_solver_cfg"]["solver_type"] == "mujoco_warp"
-    assert solver["rigid_solver_substeps"] == 7
-    assert cfg.events.record_camera.params["per_env"] is True
-    assert cfg.events.record_camera.params["max_env_num"] == 2
-    assert cfg.record_trajectory is False
-    assert cfg.sim_steps_per_control * cfg.sim_cfg.physics_dt == pytest.approx(0.025)
-    assert not hasattr(cfg.events, "settle_cup")
-
-
-def test_no_render_coarse_override_resamples_and_keeps_physical_scale(tmp_path):
-    _, cfg = _configure(
-        TASK, num_envs=1, renderer="no-render", output=tmp_path, particle_spacing=0.003
-    )
-    fluid = cfg.fluid_object[0]
-    assert fluid.render.mode == "none"
-    assert fluid.particle_radius == pytest.approx(0.0009)
-    assert fluid.render.mesh_surface["kernel_radius"] == pytest.approx(0.0045)
-    assert cfg.sim_cfg.physics_cfg.solver_cfg[
-        "particle_support_radius"
-    ] == pytest.approx(0.00675)
-    assert cfg.events.record_camera is None
-
-
-def test_physical_qualification_does_not_accept_motion_commands_alone():
-    initial = np.array([[0, 0, 0.5, 0, 0, 0, 1], [1, 0, 0.5, 0, 0, 0, 1]], dtype=float)
-    moving = initial.copy()
-    moving[0, 2] += 0.12
-    moving[0, 3] = np.sin(np.deg2rad(35))
-    moving[0, 6] = np.cos(np.deg2rad(35))
-    result = _cup_qualification([initial, moving, initial])
-    assert result["accepted"] == [True, False]
-    assert result["max_tilt_deg"] == pytest.approx([70, 0])
-    assert result["lift_m"] == pytest.approx([0.12, 0])
-
-
-@pytest.mark.parametrize("renderer", ["fast-rt", "no-render"])
-def test_online_runner_commits_last_recording_before_close(
-    renderer, tmp_path, monkeypatch
-):
-    import argparse
-    from types import SimpleNamespace
-    from unittest.mock import Mock
-    import torch
-    from embodichain.lab.scripts import run_fluid_task_program as runner
-
-    order = []
-    recorder = SimpleNamespace(
-        save_and_clear=Mock(side_effect=lambda: order.append("save"))
-    )
-    env = SimpleNamespace(
-        num_envs=2,
-        step_dt=0.05,
-        control_frequency=20,
-        elapsed_steps=torch.zeros(2),
-        reset=Mock(),
-        event_manager=SimpleNamespace(get_functor=lambda name: recorder),
-        sim=SimpleNamespace(
-            get_rigid_object=lambda uid: SimpleNamespace(
-                get_local_pose=lambda: torch.tensor([[0.0, 0, 0, 0, 0, 0, 1]]).repeat(
-                    2, 1
-                )
-            ),
-            get_fluid_object=lambda uid: SimpleNamespace(num_particles=1),
-        ),
-        close=Mock(side_effect=lambda **kwargs: order.append("close")),
-    )
-    result = SimpleNamespace(
-        completed=False, all_success=False, to_metadata=lambda: {"completed": False}
-    )
-    monkeypatch.setattr(runner, "make", lambda *args, **kwargs: env)
-    monkeypatch.setattr(runner, "discover_task_packages", lambda: None)
-    monkeypatch.setattr(runner, "execute_demo_episode", lambda *args, **kwargs: result)
-    monkeypatch.setattr(runner, "_sample", lambda env: {"received_ml": [0.0, 0.0]})
-    monkeypatch.setattr(runner.SimulationManager, "flush_cleanup_queue", lambda: None)
-    args = argparse.Namespace(
-        task=str(TASK),
-        num_envs=2,
-        renderer=renderer,
-        particle_spacing=None,
-        record_trajectory=False,
-        seconds=0.05,
-        settle_seconds=0,
-    )
-    report = runner._run(args, tmp_path)
-    assert report["accepted"] is False
-    assert order == (["save", "close"] if renderer == "fast-rt" else ["close"])
-    assert len(report["videos"]) == (2 if renderer == "fast-rt" else 0)
-    assert (tmp_path / "fluid_gym_result.json").is_file()
diff --git a/tests/sim/objects/test_fluid_batch_runtime.py b/tests/sim/objects/test_fluid_batch_runtime.py
deleted file mode 100644
index 99c6ea2d2..000000000
--- a/tests/sim/objects/test_fluid_batch_runtime.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-
-"""Batch fluid declarations and row-local reset through the public manager."""
-
-from __future__ import annotations
-
-from pathlib import Path
-import subprocess
-import sys
-import pytest
-import torch
-from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
-from embodichain.lab.sim.cfg import (
-    FluidObjectCfg,
-    FluidSpawnCfg,
-    FluidRenderCfg,
-    NewtonPhysicsCfg,
-    RenderCfg,
-)
-
-pytestmark = [pytest.mark.gpu, pytest.mark.subprocess_sim]
-
-
-def _run_fluid_partial_reset():
-    from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
-
-    if not getattr(ensure_solver_available("sph"), "supports_world_reset", False):
-        pytest.skip("requires world-isolated SPH build")
-    sim = SimulationManager(
-        SimulationManagerCfg(
-            headless=True,
-            device="cuda",
-            num_envs=2,
-            arena_space=1,
-            physics_dt=0.001,
-            render_cfg=RenderCfg(renderer="no-render"),
-            physics_cfg=NewtonPhysicsCfg(
-                num_substeps=1,
-                collision_cfg=None,
-                solver_cfg={
-                    "solver_type": "sph",
-                    "sph_method": "dfsph",
-                    "volume_map_boundary_enabled": False,
-                },
-            ),
-        )
-    )
-    fluid = None
-    try:
-        fluid = sim.add_fluid_object(
-            FluidObjectCfg(
-                uid="water",
-                particle_spacing=0.01,
-                init_pos=(0.0, 0.0, 1.0),
-                spawn=FluidSpawnCfg(kind="box", size=(0.04,) * 3),
-                render=FluidRenderCfg(mode="none"),
-            )
-        )
-        sim.prepare()
-        initial = fluid.data.default_particle_state_w
-        offsets = torch.as_tensor(
-            sim.arena_offsets, device=sim.device, dtype=torch.float32
-        )
-        torch.testing.assert_close(
-            initial[:, :, :3].mean(1),
-            offsets + torch.tensor([0.0, 0.0, 1.0], device=sim.device),
-        )
-        changed = initial[[1]].clone()
-        changed[..., 3] = 1
-        fluid.write_particle_state_to_sim(changed, [1])
-        sim.update(step=3, render_final_step=False)
-        before = fluid.data.particle_state_w
-        density = sim.get_fluid_diagnostics("water")["particle_density"]
-        fluid.reset([1])
-        torch.testing.assert_close(
-            fluid.data.particle_state_w[0], before[0], rtol=0, atol=0
-        )
-        torch.testing.assert_close(
-            fluid.data.particle_state_w[1], initial[1], rtol=0, atol=0
-        )
-        torch.testing.assert_close(
-            sim.get_fluid_diagnostics("water")["particle_density"][0],
-            density[0],
-            rtol=0,
-            atol=0,
-        )
-        assert not sim.get_fluid_diagnostics("water")["particle_density"][1].any()
-        sim.update(step=1, render_final_step=False)
-        assert torch.isfinite(fluid.data.particle_state_w).all()
-    finally:
-        fluid = None
-        sim.destroy(exit_process=False)
-        SimulationManager.flush_cleanup_queue()
-
-
-def test_fluid_partial_reset_preserves_other_world():
-    from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
-
-    if not getattr(ensure_solver_available("sph"), "supports_world_reset", False):
-        pytest.skip("requires world-isolated SPH build")
-    result = subprocess.run(
-        [sys.executable, str(Path(__file__).resolve())],
-        capture_output=True,
-        text=True,
-        timeout=120,
-    )
-    assert result.returncode == 0, result.stdout + result.stderr
-
-
-if __name__ == "__main__":
-    _run_fluid_partial_reset()
diff --git a/tests/sim/objects/test_fluid_object.py b/tests/sim/objects/test_fluid_object.py
index d5b34c43b..9cd19b2bc 100644
--- a/tests/sim/objects/test_fluid_object.py
+++ b/tests/sim/objects/test_fluid_object.py
@@ -149,56 +149,3 @@ def test_fluid_write_respects_render_frame_cache_scope(inside_frame):
     SimulationManager._ensure_render_state(sim)
     assert len(calls) == (1 if inside_frame else 2)
     assert sim._render_state_published is (True if inside_frame else None)
-
-
-def test_rigid_trajectory_samples_solver_substeps_and_rewinds():
-    from embodichain.lab.sim._runtime_controls import _KinematicRigidTrajectoryControl
-
-    poses = np.array([[0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 1]], dtype=np.float32)
-    samples, velocities = [], []
-    actor = SimpleNamespace(
-        set_world_pose=lambda value: samples.append(value.copy()),
-        set_linear_velocity=lambda value: velocities.append(value.copy()),
-        set_angular_velocity=lambda value: None,
-    )
-    control = _KinematicRigidTrajectoryControl("arena_0/cup", poses, fps=1.0)
-    control.initialize(
-        SimpleNamespace(result=SimpleNamespace(get_actor=lambda _: actor))
-    )
-    for index in range(3):
-        control(None, index, 3, 0.1)
-    np.testing.assert_allclose([p[0, 3] for p in samples], [0, 0.1, 0.2])
-    np.testing.assert_allclose(velocities[1], [1, 0, 0])
-    control.reset()
-    control(None, 0, 1, 0.1)
-    np.testing.assert_array_equal(samples[-1], samples[0])
-    control.close()
-    assert control._actor is None
-
-
-def test_rigid_trajectory_rotation_hold_across_outer_steps():
-    from scipy.spatial.transform import Rotation
-    from embodichain.lab.sim._runtime_controls import _KinematicRigidTrajectoryControl
-
-    axis = np.array([1.0, 2.0, 3.0])
-    axis /= np.linalg.norm(axis)
-    end = Rotation.from_rotvec(0.6 * axis).as_quat()
-    poses = np.array([[0, 0, 0, 0, 0, 0, 1], [1, 2, 3, *end]])
-    captured = []
-    actor = SimpleNamespace(
-        set_world_pose=lambda value: captured.append({"pose": value.copy()}),
-        set_linear_velocity=lambda value: captured[-1].update(linear=value.copy()),
-        set_angular_velocity=lambda value: captured[-1].update(angular=value.copy()),
-    )
-    control = _KinematicRigidTrajectoryControl("arena_0/cup", poses, fps=1)
-    control.initialize(
-        SimpleNamespace(result=SimpleNamespace(get_actor=lambda _: actor))
-    )
-    for outer in range(3):
-        for substep in range(2):
-            control(None, substep, 2, 0.25)
-    np.testing.assert_allclose(captured[3]["pose"][:3, 3], [0.75, 1.5, 2.25])
-    np.testing.assert_allclose(captured[3]["angular"], axis * 0.6, atol=1e-6)
-    np.testing.assert_allclose(captured[4]["pose"], captured[5]["pose"])
-    np.testing.assert_allclose(captured[5]["linear"], 0, atol=1e-6)
-    np.testing.assert_allclose(captured[5]["angular"], 0, atol=1e-6)
diff --git a/tests/sim/objects/test_fluid_online_runtime.py b/tests/sim/objects/test_fluid_online_runtime.py
deleted file mode 100644
index 30f1b3868..000000000
--- a/tests/sim/objects/test_fluid_online_runtime.py
+++ /dev/null
@@ -1,285 +0,0 @@
-# ----------------------------------------------------------------------------
-# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ----------------------------------------------------------------------------
-
-"""Online rigid boundaries, mass ownership and selected fluid resets."""
-
-from __future__ import annotations
-
-import pytest
-from pathlib import Path
-import subprocess
-import sys
-import torch
-import trimesh
-
-from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
-from embodichain.lab.sim.cfg import (
-    FluidObjectCfg,
-    FluidSpawnCfg,
-    FluidRenderCfg,
-    NewtonPhysicsCfg,
-    RenderCfg,
-    RigidObjectCfg,
-)
-from embodichain.lab.sim.shapes import CubeCfg, MeshCfg, MeshCollisionCfg
-
-pytestmark = [pytest.mark.gpu, pytest.mark.subprocess_sim]
-
-
-class _FluidOnlineRuntime:
-    def test_live_dynamic_boundary_and_selected_reset(self):
-        from dexsim.engine.newton_physics.backend_registry import get_newton_backend
-
-        mesh = trimesh.creation.box(extents=[0.1] * 3)
-        sim = SimulationManager(
-            SimulationManagerCfg(
-                headless=True,
-                device="cuda",
-                num_envs=2,
-                arena_space=0,
-                physics_dt=0.001,
-                render_cfg=RenderCfg(renderer="no-render"),
-                physics_cfg=NewtonPhysicsCfg(
-                    num_substeps=1,
-                    collision_cfg=None,
-                    solver_cfg={
-                        "solver_type": "sph",
-                        "sph_method": "dfsph",
-                        "rigid_solver_cfg": {"rne_mode": "disabled"},
-                        "rigid_solver_substeps": 2,
-                        "particle_support_radius": 0.0225,
-                        "volume_map_resolution": 16,
-                        "volume_map_quadrature_samples": 2,
-                    },
-                ),
-            )
-        )
-        body = fluid = None
-        try:
-            body = sim.add_rigid_object(
-                RigidObjectCfg.from_dict(
-                    {
-                        "uid": "boundary",
-                        "shape": CubeCfg(size=[0.1] * 3).to_dict(),
-                        "particle_collision_shape": MeshCfg(
-                            vertices=mesh.vertices.tolist(),
-                            triangles=mesh.faces.tolist(),
-                            collision=MeshCollisionCfg(
-                                approximation="sdf",
-                                sdf_resolution=32,
-                                sdf_narrow_band_range=(-0.05, 0.05),
-                                sdf_padding=0.02,
-                            ),
-                        ).to_dict(),
-                        "body_type": "dynamic",
-                        "init_pos": [0, 0, 1],
-                        "attrs": {"mass_props": {"mass": 0.3}},
-                    }
-                )
-            )
-            fluid = sim.add_fluid_object(
-                FluidObjectCfg(
-                    uid="water",
-                    particle_spacing=0.01,
-                    particle_radius=0.003,
-                    spawn=FluidSpawnCfg(
-                        kind="points",
-                        positions=[
-                            [0.055, -0.005, 0.995],
-                            [0.055, 0.005, 0.995],
-                            [0.055, -0.005, 1.005],
-                            [0.055, 0.005, 1.005],
-                        ],
-                    ),
-                    render=FluidRenderCfg(mode="none"),
-                )
-            )
-            sim.prepare()
-            backend = get_newton_backend(sim._world)
-            assert backend.solver.sph_solver.sdf_shape_inventory.shape_count == 2
-            torch.testing.assert_close(
-                torch.as_tensor(backend.model.body_mass.numpy()),
-                torch.tensor([0.3, 0.3]),
-            )
-            torch.testing.assert_close(
-                torch.as_tensor(backend.model.body_inertia.numpy())[
-                    :, [0, 1, 2], [0, 1, 2]
-                ],
-                torch.full((2, 3), 0.0005),
-                rtol=0.001,
-                atol=1e-7,
-            )
-            body.set_velocity(
-                lin_vel=torch.tensor([[0.5, 0, 0], [0.5, 0, 0]], device=sim.device)
-            )
-            sim.update(step=200, render_final_step=False)
-            assert backend.sim_time == pytest.approx(0.2)
-            assert sim.physics.cuda_graph_status == "captured"
-            pose = body.get_local_pose()
-            assert bool((pose[:, 0] > 0.095).all())
-            assert bool((fluid.data.particle_pos_w[..., 0] > 0.12).all())
-            # Water has no reaction force on the prescribed initial rigid momentum.
-            torch.testing.assert_close(
-                pose[:, 0],
-                torch.full((2,), 0.1, device=sim.device),
-                rtol=0.01,
-                atol=0.001,
-            )
-            particles = fluid.data.particle_state_w.clone()
-            fluid.reset([0])
-            torch.testing.assert_close(
-                fluid.data.particle_state_w[1], particles[1], rtol=0, atol=0
-            )
-            torch.testing.assert_close(body.get_local_pose(), pose, rtol=0, atol=0)
-            sim.reset_objects_state(env_ids=[0])
-            torch.testing.assert_close(
-                body.get_local_pose()[1], pose[1], rtol=0, atol=0
-            )
-            sim.update(step=1, render_final_step=False)
-            assert bool(torch.isfinite(fluid.data.particle_state_w).all())
-        finally:
-            body = fluid = None
-            sim.destroy(exit_process=False)
-            SimulationManager.flush_cleanup_queue()
-
-    def test_mimic_follower_uses_inner_rigid_solver(self):
-        from embodichain_robots import FrankaPandaCfg
-
-        sim = SimulationManager(
-            SimulationManagerCfg(
-                headless=True,
-                device="cuda",
-                num_envs=1,
-                physics_dt=0.001,
-                render_cfg=RenderCfg(renderer="no-render"),
-                physics_cfg=NewtonPhysicsCfg(
-                    num_substeps=1,
-                    collision_cfg=None,
-                    solver_cfg={
-                        "solver_type": "sph",
-                        "rigid_solver_cfg": {"rne_mode": "disabled"},
-                        "rigid_solver_substeps": 2,
-                        "volume_map_boundary_enabled": False,
-                        "sdf_boundary_enabled": False,
-                    },
-                ),
-            )
-        )
-        robot = fluid = None
-        try:
-            robot = sim.add_robot(FrankaPandaCfg.from_dict({}))
-            fluid = sim.add_fluid_object(
-                FluidObjectCfg(
-                    uid="water",
-                    particle_spacing=0.01,
-                    init_pos=(2, 0, 2),
-                    spawn=FluidSpawnCfg(kind="box", size=(0.02,) * 3),
-                    render=FluidRenderCfg(mode="none"),
-                )
-            )
-            sim.prepare()
-            assert robot._newton_mimic_compliance_configured
-            joint_ids = robot.get_joint_ids("eef", remove_mimic=True)
-            robot.set_qpos(
-                torch.zeros((1, len(joint_ids)), device=sim.device), joint_ids=joint_ids
-            )
-            sim.update(step=500, render_final_step=False)
-            fingers = robot.get_qpos(name="eef")
-            assert bool((fingers.abs() < 0.004).all()), fingers
-            assert float((fingers[:, 0] - fingers[:, 1]).abs().max()) < 0.002
-        finally:
-            robot = fluid = None
-            sim.destroy(exit_process=False)
-            SimulationManager.flush_cleanup_queue()
-
-    def test_normal_gym_partial_reset_does_not_advance_peer(self):
-        from embodichain.lab.gym.utils.gym_utils import config_to_cfg
-        from embodichain.lab.gym.utils.registration import discover_task_packages, make
-        from embodichain.utils.utility import load_config
-
-        task = (
-            Path(__file__).resolve().parents[3]
-            / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"
-        )
-        discover_task_packages()
-        declaration = load_config(task)
-        cfg = config_to_cfg(declaration, source_path=task)
-        cfg.num_envs = 2
-        cfg.enable_sensor = False
-        cfg.sim_cfg.render_cfg = RenderCfg(renderer="no-render")
-        cfg.sim_cfg.headless = True
-        cfg.sim_steps_per_control = 1
-        cfg.events.record_camera = None
-        cfg.sim_cfg.physics_cfg.solver_cfg.update(
-            volume_map_boundary_enabled=False, sdf_boundary_enabled=False
-        )
-        for obj in cfg.background + cfg.rigid_object:
-            for shape in (obj.shape, obj.particle_collision_shape):
-                collision = getattr(shape, "collision", None)
-                if collision is not None and collision.approximation == "sdf":
-                    collision.sdf_resolution = 16
-        fluid_cfg = cfg.fluid_object[0]
-        fluid_cfg.particle_spacing = 0.01
-        fluid_cfg.particle_radius = 0.003
-        fluid_cfg.spawn = FluidSpawnCfg(
-            kind="box", size=(0.02,) * 3, reference_uid="cup"
-        )
-        fluid_cfg.render = FluidRenderCfg(mode="none")
-        env = make(declaration["id"], cfg=cfg)
-        try:
-            env.reset(seed=0)
-            action = env.robot.get_qpos()[:, env.active_joint_ids].clone()
-            for _ in range(3):
-                env.step(action)
-            fluid = env.sim.get_fluid_object("water")
-            before_water = fluid.data.particle_state_w[1].clone()
-            before_robot = env.robot.get_qpos()[1].clone()
-            before_cup = env.sim.get_rigid_object("cup").get_local_pose()[1].clone()
-            env.reset(options={"reset_ids": [0], "save_data": False})
-            torch.testing.assert_close(
-                fluid.data.particle_state_w[1], before_water, rtol=0, atol=0
-            )
-            torch.testing.assert_close(
-                env.robot.get_qpos()[1], before_robot, rtol=0, atol=0
-            )
-            torch.testing.assert_close(
-                env.sim.get_rigid_object("cup").get_local_pose()[1],
-                before_cup,
-                rtol=0,
-                atol=0,
-            )
-            assert env.elapsed_steps.tolist() == [0, 3]
-        finally:
-            env.close(exit_process=False)
-            del env
-            SimulationManager.flush_cleanup_queue()
-
-
-def test_online_fluid_boundaries_and_mimic_reset_in_fresh_process():
-    result = subprocess.run(
-        [sys.executable, str(Path(__file__).resolve())],
-        capture_output=True,
-        text=True,
-        timeout=180,
-    )
-    assert result.returncode == 0, result.stdout + result.stderr
-
-
-if __name__ == "__main__":
-    worker = _FluidOnlineRuntime()
-    worker.test_live_dynamic_boundary_and_selected_reset()
-    worker.test_mimic_follower_uses_inner_rigid_solver()
-    worker.test_normal_gym_partial_reset_does_not_advance_peer()
diff --git a/tests/sim/objects/test_fluid_runtime.py b/tests/sim/objects/test_fluid_runtime.py
index ad60002b9..37848ad96 100644
--- a/tests/sim/objects/test_fluid_runtime.py
+++ b/tests/sim/objects/test_fluid_runtime.py
@@ -63,11 +63,6 @@ def test_prepare_write_reset_and_repeat(self):
                     uid="boundary", body_type="kinematic", shape=CubeCfg(size=[0.1] * 3)
                 )
             )
-            sim.register_kinematic_rigid_trajectory(
-                "boundary",
-                torch.tensor([[[0.0, 0, 0, 0, 0, 0, 1], [1.0, 0, 0, 0, 0, 0, 1]]]),
-                fps=10,
-            )
             fluid = sim.add_fluid_object(
                 FluidObjectCfg(
                     uid="water",
@@ -85,7 +80,6 @@ def test_prepare_write_reset_and_repeat(self):
             assert fluid.num_particles == 64
             original = fluid.data.default_particle_state_w
             sim.update(step=3, render_final_step=False)
-            assert rigid.get_local_pose()[0, 0].item() == pytest.approx(0.025, abs=1e-6)
             first = fluid.data.particle_state_w
             assert torch.isfinite(first).all()
             assert first[..., 2].mean() < original[..., 2].mean()
@@ -95,9 +89,6 @@ def test_prepare_write_reset_and_repeat(self):
             torch.testing.assert_close(
                 fluid.data.particle_state_w, first, rtol=1e-5, atol=1e-6
             )
-            assert rigid.get_local_pose()[0, 0].item() == pytest.approx(0.025, abs=1e-6)
-            sim.update(step=105, render_final_step=False)
-            assert rigid.get_local_pose()[0, 0].item() == pytest.approx(1.0, abs=1e-6)
             with pytest.raises(NotImplementedError, match="before prepare"):
                 sim.add_fluid_object(FluidObjectCfg(uid="late"))
         finally:
diff --git a/tests/sim/spawn/test_descriptors.py b/tests/sim/spawn/test_descriptors.py
index f535ad149..11b57a2a0 100644
--- a/tests/sim/spawn/test_descriptors.py
+++ b/tests/sim/spawn/test_descriptors.py
@@ -2340,51 +2340,3 @@ def test_rigid_descriptor_rejects_orientation_without_principal_moments() -> Non
     )
     with pytest.raises(ValueError, match="requires explicit principal inertia"):
         rigid_desc_from_cfg(cfg)
-
-
-@pytest.mark.parametrize("solver", ["sph", "mujoco_warp"])
-def test_particle_boundary_shares_body_without_replacing_rigid_contact(solver):
-    from embodichain.lab.sim.cfg import RigidObjectCfg
-    from embodichain.lab.sim.shapes import MeshCollisionCfg
-    from embodichain.lab.sim.spawn.descriptors import rigid_desc_from_cfg
-
-    cfg = RigidObjectCfg(
-        uid="cup",
-        shape=MeshCfg(
-            fpath="/assets/cup.glb",
-            collision=MeshCollisionCfg(
-                approximation="convex_decomposition", max_hulls=32
-            ),
-        ),
-        particle_collision_shape=MeshCfg(
-            fpath="/assets/cavity.obj",
-            collision=MeshCollisionCfg(approximation="sdf", sdf_resolution=192),
-        ),
-    )
-    desc, _ = rigid_desc_from_cfg(cfg, newton_solver_type=solver)
-    assert len(desc.collisions) == 2
-    contact, boundary = desc.collisions
-    assert contact.approximation == CollisionApproximation.CONVEX_DECOMPOSITION
-    assert contact.newton.has_particle_collision is False
-    assert boundary.approximation == CollisionApproximation.SDF
-    assert boundary.enable_collision is False
-    assert boundary.newton.has_particle_collision is True
-    assert boundary.newton.contributes_to_mass is False
-    assert boundary.newton.sdf_max_resolution == 192
-    assert desc.renders[0].file_path == "/assets/cup.glb"
-    assert cfg.shape.collision.approximation == "convex_decomposition"
-
-
-def test_particle_boundary_requires_newton_and_sdf():
-    from embodichain.lab.sim.cfg import RigidObjectCfg
-    from embodichain.lab.sim.spawn.descriptors import rigid_desc_from_cfg
-
-    cfg = RigidObjectCfg(
-        uid="cup",
-        shape=MeshCfg(fpath="/assets/cup.glb"),
-        particle_collision_shape=MeshCfg(fpath="/assets/cavity.obj"),
-    )
-    with pytest.raises(ValueError, match="Newton"):
-        rigid_desc_from_cfg(cfg)
-    with pytest.raises(ValueError, match="sdf"):
-        rigid_desc_from_cfg(cfg, newton_solver_type="sph")
diff --git a/tests/sim/test_fluid_cfg.py b/tests/sim/test_fluid_cfg.py
index 8ccd409a6..3f98047c1 100644
--- a/tests/sim/test_fluid_cfg.py
+++ b/tests/sim/test_fluid_cfg.py
@@ -24,9 +24,7 @@
     FluidObjectCfg,
     FluidSpawnCfg,
     NewtonPhysicsCfg,
-    RigidObjectCfg,
 )
-from embodichain.lab.sim.spawn.descriptors import rigid_desc_from_cfg
 from embodichain.compute.geometry.particles import sample_volume_particles
 
 pytestmark = pytest.mark.no_sim
@@ -110,41 +108,6 @@ def test_fill_mesh_must_be_closed(tmp_path):
         sample_volume_particles("mesh_volume", 0.01, mesh_path=str(path))
 
 
-def test_rigid_visual_mesh_does_not_replace_sdf_collision():
-    cfg = RigidObjectCfg.from_dict(
-        {
-            "uid": "cup",
-            "body_type": "kinematic",
-            "shape": {
-                "shape_type": "Mesh",
-                "vertices": [[0, 0, 0], [1, 0, 0], [0, 1, 0]],
-                "triangles": [[0, 1, 2]],
-                "collision": {
-                    "approximation": "sdf",
-                    "force_sdf": False,
-                    "sdf_resolution": 192,
-                },
-            },
-            "visual_shape": {"shape_type": "Mesh", "fpath": "cup.glb"},
-        }
-    )
-    desc, _ = rigid_desc_from_cfg(cfg, newton_solver_type="sph")
-    assert desc.renders[0].file_path == "cup.glb"
-    assert desc.collisions[0].newton.force_sdf is False
-    assert desc.collisions[0].render_source_index is None
-
-
-def test_rigid_material_preserves_transmission():
-    from embodichain.lab.sim.shapes import CubeCfg
-    from embodichain.lab.sim.material import VisualMaterialCfg
-
-    cfg = RigidObjectCfg(
-        uid="glass", shape=CubeCfg(visual_material=VisualMaterialCfg(transmission=0.8))
-    )
-    _, materials = rigid_desc_from_cfg(cfg, newton_solver_type="sph")
-    assert next(iter(materials.values())).transmission == 0.8
-
-
 @pytest.mark.parametrize("backend,num_envs", [("default", 1), ("newton", 2)])
 def test_manager_rejects_unsupported_fluid_worlds_before_declaration(backend, num_envs):
     from types import SimpleNamespace
diff --git a/tests/sim/test_sim_manager.py b/tests/sim/test_sim_manager.py
index 14d4bf71e..6f3b29c98 100644
--- a/tests/sim/test_sim_manager.py
+++ b/tests/sim/test_sim_manager.py
@@ -467,7 +467,6 @@ def _make_runtime_control_sim_manager(
     sim._articulations = {}
     sim._rigid_objects = {"table": object()}
     sim._fluid_objects = {}
-    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {
         "cloth": SimpleNamespace(
             cfg=SurfaceDeformableObjectCfg(uid="cloth", particle_flags=[0, 1, 0])
@@ -550,7 +549,6 @@ def test_deferred_destroy_prepares_backend_before_releasing_world(
     sim._constraints = {}
     sim._rigid_object_groups = {}
     sim._fluid_objects = {}
-    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._articulations = {}
     sim._robots = {}
@@ -1294,7 +1292,6 @@ def capture(self, **_kwargs: object) -> None:
     sim._rigid_objects = {}
     sim._rigid_object_groups = {}
     sim._fluid_objects = {}
-    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._articulations = {}
     sim._robots = {}
@@ -2230,7 +2227,6 @@ def test_remove_asset_marks_visualization_topology_dirty() -> None:
     sim._rigid_objects = {"cube": rigid_object}
     sim._rigid_object_groups = {}
     sim._fluid_objects = {}
-    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._articulations = {}
     sim._robots = {}
@@ -2496,7 +2492,6 @@ def test_reset_objects_state_includes_deformable_assets() -> None:
     sim._lights = {}
     sim._sensors = {}
     sim._fluid_objects = {}
-    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {
         "soft": MagicMock(),
         "cloth": MagicMock(),
@@ -2530,7 +2525,6 @@ def test_newton_reset_clears_all_articulations_in_selected_worlds_together() ->
     sim._rigid_objects = {}
     sim._rigid_object_groups = {}
     sim._fluid_objects = {}
-    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._lights = {}
     sim._sensors = {}
@@ -2553,7 +2547,6 @@ def test_newton_reset_rejects_excluding_one_articulation_from_selected_worlds()
     sim._rigid_objects = {}
     sim._rigid_object_groups = {}
     sim._fluid_objects = {}
-    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._lights = {}
     sim._sensors = {}
@@ -2583,7 +2576,6 @@ def test_newton_non_mujoco_reset_allows_excluding_an_articulation() -> None:
     sim._rigid_objects = {}
     sim._rigid_object_groups = {}
     sim._fluid_objects = {}
-    sim._rigid_trajectory_controls = {}
     sim._deformable_objects = {}
     sim._lights = {}
     sim._sensors = {}
@@ -2813,7 +2805,7 @@ def test_process_no_render_is_detected_after_world_cleanup(monkeypatch) -> None:
     assert SimulationManager._get_process_renderer() == "no-render"
 
 
-def test_reset_fluid_and_rigid_controls_selects_rows_and_honors_exclusions():
+def test_reset_fluid_selects_rows_and_honors_exclusions():
     sim = object.__new__(SimulationManager)
     sim.physics = SimpleNamespace(name="default")
     sim.num_envs = 2
@@ -2827,15 +2819,8 @@ def test_reset_fluid_and_rigid_controls_selects_rows_and_honors_exclusions():
         "_deformable_objects",
     ):
         setattr(sim, name, {})
-    first, second, excluded = MagicMock(), MagicMock(), MagicMock()
-    fluid = MagicMock()
-    sim._rigid_trajectory_controls = {
-        "cup": (first, second),
-        "other": (excluded, excluded),
-    }
-    sim._fluid_objects = {"water": fluid}
+    fluid, excluded = MagicMock(), MagicMock()
+    sim._fluid_objects = {"water": fluid, "other": excluded}
     sim.reset_objects_state(env_ids=[1], excluded_uids=["other"])
-    first.reset.assert_not_called()
-    second.reset.assert_called_once_with()
     excluded.reset.assert_not_called()
     fluid.reset.assert_called_once_with([1])
diff --git a/tests/test_task_program_package_data.py b/tests/test_task_program_package_data.py
index 653dc97b4..57a790e60 100644
--- a/tests/test_task_program_package_data.py
+++ b/tests/test_task_program_package_data.py
@@ -98,8 +98,6 @@
 }
 _RESOURCE_PATHS = frozenset(
     {
-        Path("tasks/manipulation/tableware/pour_cup/task.franka.fluid.yaml"),
-        Path("tasks/manipulation/tableware/pour_cup/env.fluid.yaml"),
         *_PROGRAMS,
         *_DEPLOYMENTS,
         Path("tasks/manipulation/repeated_pick_place/catalog.yaml"),
@@ -241,10 +239,6 @@ def test_setup_stages_all_official_task_programs(
         for relative_path in _RESOURCE_PATHS
     }
     assert expected_outputs <= staged_config_package.relative_outputs
-    assert not (
-        staged_config_package.build_lib
-        / "embodichain_tasks/configs/tasks/manipulation/tableware/pour_cup/assets"
-    ).exists()
 
 
 def test_staged_task_program_resources_decode_through_installed_paths(

From 697afb4c814ede9cc14d7e6c5d9d129a1a071f41 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Thu, 22 Oct 2026 21:21:49 +0800
Subject: [PATCH 14/19] fix(fluid): address material, solver and lifecycle
 review findings

---
 .../topics/simulation-system/fluids.md        |   5 +
 .../topics/simulation-system/rendering.md     |   6 +
 embodichain/lab/sim/cfg/fluid.py              |   4 +
 embodichain/lab/sim/material.py               |  17 ++-
 .../lab/sim/objects/deformable/base.py        |   7 +-
 embodichain/lab/sim/objects/rigid_object.py   |   6 +-
 embodichain/lab/sim/objects/visual_object.py  |   3 +-
 embodichain/lab/sim/sim_manager.py            |   9 +-
 embodichain/lab/sim/spawn/descriptors.py      |   1 +
 .../test_asset_material_initialization.py     |  57 +++++++
 tests/sim/objects/test_fluid_runtime.py       | 141 +++++++++++-------
 tests/sim/objects/test_visual_object.py       |  39 +++++
 tests/sim/spawn/test_descriptors.py           |  46 ++++++
 tests/sim/test_fluid_cfg.py                   |  75 ++++++++++
 14 files changed, 348 insertions(+), 68 deletions(-)

diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
index 877914348..22127568e 100644
--- a/agent_context/topics/simulation-system/fluids.md
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -6,6 +6,11 @@ remain DexSim `SPHSolverCfg`, accepted directly or through
 `SimulationManager.add_fluid_object()` declares through SpawnScene and binds
 `objects/fluid.py` in place at `prepare()`.
 
+The fluid declaration owns density and spacing. Default native SPH values for
+`rest_density` and `particle_diameter` are placeholders, equally for mappings
+and native config objects; non-default solver values must match the fluid.
+DexSim derives the effective solver values from the fluid descriptors.
+
 ## Runtime requirements
 
 The foundation supports the published `dexsim_engine==0.5.1rc1` artifact
diff --git a/agent_context/topics/simulation-system/rendering.md b/agent_context/topics/simulation-system/rendering.md
index 8b90fca3c..96a664afa 100644
--- a/agent_context/topics/simulation-system/rendering.md
+++ b/agent_context/topics/simulation-system/rendering.md
@@ -147,6 +147,12 @@ Configuration tests do not prove GPU/NGX/NRD runtime support, which initializes
 on a rendered frame. Read exact renderer and algorithm defaults in the config
 source.
 
+`VisualMaterialCfg` optical settings apply to direct material creation and
+shape-owned materials. Spawn carries transmission in its material descriptor;
+rigid, visual and deformable facades apply explicit volume parameters to every
+native material when binding. The captured defaults retain these properties
+through material reset; absent settings preserve imported native values.
+
 ## Startup summaries
 
 `sim/_startup_summary.py` collects read-only rows. Manager engine output follows
diff --git a/embodichain/lab/sim/cfg/fluid.py b/embodichain/lab/sim/cfg/fluid.py
index 1ed11df5c..5d3d873cd 100644
--- a/embodichain/lab/sim/cfg/fluid.py
+++ b/embodichain/lab/sim/cfg/fluid.py
@@ -150,6 +150,10 @@ class FluidObjectCfg(ObjectBaseCfg):
     Density and spacing determine mass and must agree across fluid objects.
     Solver viscosity, surface tension and iteration controls belong exclusively
     to DexSim's SPHSolverCfg supplied to NewtonPhysicsCfg.solver_cfg.
+
+    This config owns density and spacing. Native SPH defaults for those fields
+    are placeholders in both mapping and native solver configs; non-default
+    solver values must match the fluid declaration.
     """
 
     density: float = 1000.0
diff --git a/embodichain/lab/sim/material.py b/embodichain/lab/sim/material.py
index cc2e41746..75a33b86b 100644
--- a/embodichain/lab/sim/material.py
+++ b/embodichain/lab/sim/material.py
@@ -22,7 +22,7 @@
 import numpy as np
 
 from dataclasses import dataclass
-from typing import Dict, Union
+from typing import Any, Dict, Union
 from functools import cached_property
 
 from dexsim.engine import MaterialInst, Material
@@ -103,7 +103,7 @@ def from_dict(cls, cfg_dict: dict) -> VisualMaterialCfg:
 def _apply_volume_material_properties(
     native: MaterialInst, cfg: VisualMaterialCfg
 ) -> None:
-    """Apply optical volume parameters not carried by Spawn MaterialDesc."""
+    """Apply explicit optical parameters to an existing native material."""
     for name in (
         "transmission",
         "volume_thickness",
@@ -491,12 +491,19 @@ class ReuseSegmentState:
     working_inst: VisualMaterialInst
 
 
-def _capture_render_materials(render_body) -> list[MaterialInst | None]:
-    """Capture the material attached to every render-body mesh segment."""
-    return [
+def _capture_render_materials(
+    render_body: Any, *, cfg: VisualMaterialCfg | None = None
+) -> list[MaterialInst | None]:
+    """Capture every segment material and apply configured optical properties."""
+    materials = [
         render_body.get_material(mesh_id)
         for mesh_id in range(render_body.get_mesh_count())
     ]
+    if cfg is not None:
+        for material in materials:
+            if material is not None:
+                _apply_volume_material_properties(material, cfg)
+    return materials
 
 
 def _wrap_first_render_material(
diff --git a/embodichain/lab/sim/objects/deformable/base.py b/embodichain/lab/sim/objects/deformable/base.py
index 231baf321..61b0bf366 100644
--- a/embodichain/lab/sim/objects/deformable/base.py
+++ b/embodichain/lab/sim/objects/deformable/base.py
@@ -332,11 +332,16 @@ def _initialize_existing_visual_material(self) -> None:
         """Capture and wrap materials parsed from the source asset."""
         self._original_visual_material = [[] for _ in self._entities]
         self._original_visual_material_inst = [None] * len(self._entities)
+        cfg = getattr(self, "cfg", None)
+        shape = getattr(cfg, "visual_shape", None) or getattr(cfg, "shape", None)
+        material_cfg = getattr(shape, "visual_material", None)
         for env_idx, entity in enumerate(self._entities):
             render_body = entity.get_render_body()
             if render_body is None:
                 continue
-            original_materials = _capture_render_materials(render_body)
+            original_materials = _capture_render_materials(
+                render_body, cfg=material_cfg
+            )
             self._original_visual_material[env_idx] = original_materials
             wrapped = _wrap_first_render_material(original_materials)
             if wrapped is not None:
diff --git a/embodichain/lab/sim/objects/rigid_object.py b/embodichain/lab/sim/objects/rigid_object.py
index 64ea84d4b..cf6fbac93 100644
--- a/embodichain/lab/sim/objects/rigid_object.py
+++ b/embodichain/lab/sim/objects/rigid_object.py
@@ -1501,11 +1501,15 @@ def _initialize_existing_visual_material(self) -> None:
         """
         self._original_visual_material = [[] for _ in self._entities]
         self._original_visual_material_inst = [None] * len(self._entities)
+        shape = getattr(getattr(self, "cfg", None), "shape", None)
+        material_cfg = getattr(shape, "visual_material", None)
         for env_idx, entity in enumerate(self._entities):
             render_body = entity.get_render_body()
             if render_body is None:
                 continue
-            original_materials = _capture_render_materials(render_body)
+            original_materials = _capture_render_materials(
+                render_body, cfg=material_cfg
+            )
             self._original_visual_material[env_idx] = original_materials
             wrapped = _wrap_first_render_material(original_materials)
             if wrapped is not None:
diff --git a/embodichain/lab/sim/objects/visual_object.py b/embodichain/lab/sim/objects/visual_object.py
index 162afe025..d3eaa1a28 100644
--- a/embodichain/lab/sim/objects/visual_object.py
+++ b/embodichain/lab/sim/objects/visual_object.py
@@ -139,11 +139,12 @@ def bind_spawn(self, result: Scene) -> None:
         self._pose_view = _SceneVisualObjectView(result, self._entities, self.device)
         poses = self._pose_view.fetch_pose(self._pose_view.rows)
         self._apply_visual_uv()
+        material_cfg = getattr(self.cfg.shape, "visual_material", None)
         materials = [
             (
                 []
                 if e.get_render_body() is None
-                else _capture_render_materials(e.get_render_body())
+                else _capture_render_materials(e.get_render_body(), cfg=material_cfg)
             )
             for e in self._entities
         ]
diff --git a/embodichain/lab/sim/sim_manager.py b/embodichain/lab/sim/sim_manager.py
index 56cfe38d9..ad3fcf97b 100644
--- a/embodichain/lab/sim/sim_manager.py
+++ b/embodichain/lab/sim/sim_manager.py
@@ -2883,6 +2883,9 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
             ):
                 raise ValueError("All fluids must share density and particle_spacing.")
         configured = self.sim_config.physics_cfg.solver_cfg
+        from dexsim.engine.newton_physics import SPHSolverCfg
+
+        defaults = SPHSolverCfg()
         for key, value in (
             ("rest_density", cfg.density),
             ("particle_diameter", cfg.particle_spacing),
@@ -2892,8 +2895,10 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
                 if isinstance(configured, Mapping)
                 else getattr(configured, key, None)
             )
-            if existing is not None and not np.isclose(
-                existing, value, rtol=1e-7, atol=0
+            if (
+                existing is not None
+                and not np.isclose(existing, getattr(defaults, key), rtol=1e-7, atol=0)
+                and not np.isclose(existing, value, rtol=1e-7, atol=0)
             ):
                 raise ValueError(f"Fluid {key} conflicts with solver_cfg.{key}.")
         points = cfg.spawn.sample(cfg.particle_spacing)
diff --git a/embodichain/lab/sim/spawn/descriptors.py b/embodichain/lab/sim/spawn/descriptors.py
index e3088a8de..aac0b6bd8 100644
--- a/embodichain/lab/sim/spawn/descriptors.py
+++ b/embodichain/lab/sim/spawn/descriptors.py
@@ -1744,6 +1744,7 @@ def _compile_visual_material(
         metallic_map=cfg.metallic_texture,
         ao_map=cfg.ao_texture,
         ior=float(cfg.ior),
+        transmission=cfg.transmission,
     )
     return key, (key, desc)
 
diff --git a/tests/sim/objects/test_asset_material_initialization.py b/tests/sim/objects/test_asset_material_initialization.py
index a4ab33ac4..f2b1bd316 100644
--- a/tests/sim/objects/test_asset_material_initialization.py
+++ b/tests/sim/objects/test_asset_material_initialization.py
@@ -228,3 +228,60 @@ def test_asset_reset_restores_selected_environment_material(asset_type):
     asset.reset(env_ids=[0])
 
     asset.restore_visual_material.assert_called_once_with(env_ids=[0])
+
+
+@pytest.mark.parametrize(
+    "asset_type", (RigidObject, VolumeDeformableObject, SurfaceDeformableObject)
+)
+def test_shape_optical_properties_reach_every_native_material(asset_type):
+    from embodichain.lab.sim.material import VisualMaterialCfg
+    from embodichain.lab.sim.shapes import MeshCfg
+
+    originals = [_material("first"), _material("second"), None]
+    asset, _ = _make_asset(asset_type, originals)
+    asset.cfg = SimpleNamespace(
+        shape=MeshCfg(
+            fpath="/assets/glass.obj",
+            visual_material=VisualMaterialCfg(
+                transmission=0.85,
+                volume_thickness=0.08,
+                volume_attenuation_distance=0.5,
+                volume_attenuation_color=(0.2, 0.6, 0.8),
+            ),
+        )
+    )
+
+    asset._initialize_existing_visual_material()
+
+    for material in originals[:2]:
+        assert material.set_pbr_param.call_args_list == [
+            call("transmission", 0.85),
+            call("volume_thickness", 0.08),
+            call("volume_attenuation_distance", 0.5),
+            call("volume_attenuation_color", 0.2, 0.6, 0.8),
+        ]
+
+
+def test_surface_optics_use_separate_visual_mesh_material():
+    from embodichain.lab.sim.material import VisualMaterialCfg
+    from embodichain.lab.sim.shapes import MeshCfg
+
+    original = _material("visual")
+    asset, _ = _make_asset(SurfaceDeformableObject, [original])
+    asset.cfg = SimpleNamespace(
+        shape=MeshCfg(
+            fpath="/assets/physics.obj",
+            visual_material=VisualMaterialCfg(transmission=0.1),
+        ),
+        visual_shape=MeshCfg(
+            fpath="/assets/visual.obj",
+            visual_material=VisualMaterialCfg(transmission=0.85, volume_thickness=0.08),
+        ),
+    )
+
+    asset._initialize_existing_visual_material()
+
+    assert original.set_pbr_param.call_args_list == [
+        call("transmission", 0.85),
+        call("volume_thickness", 0.08),
+    ]
diff --git a/tests/sim/objects/test_fluid_runtime.py b/tests/sim/objects/test_fluid_runtime.py
index 37848ad96..0c240af54 100644
--- a/tests/sim/objects/test_fluid_runtime.py
+++ b/tests/sim/objects/test_fluid_runtime.py
@@ -18,6 +18,11 @@
 
 from __future__ import annotations
 
+import json
+from pathlib import Path
+import subprocess
+import sys
+
 import pytest
 import torch
 from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
@@ -29,69 +34,89 @@
     RenderCfg,
 )
 
-pytestmark = [pytest.mark.gpu, pytest.mark.requires_sim]
+pytestmark = [pytest.mark.gpu, pytest.mark.subprocess_sim]
 
 
 class TestFluidRuntime:
-    def test_prepare_write_reset_and_repeat(self):
-        from embodichain.lab.sim.cfg import RigidObjectCfg
-        from embodichain.lab.sim.shapes import CubeCfg
+    def test_prepare_write_reset_and_repeat(self, tmp_path: Path) -> None:
+        result_path = tmp_path / "lifecycle.json"
+        result = subprocess.run(
+            [sys.executable, str(Path(__file__).resolve()), str(result_path)],
+            capture_output=True,
+            text=True,
+            timeout=180,
+        )
+        assert result.returncode == 0, result.stdout + result.stderr
+        assert json.loads(result_path.read_text()) == {"num_particles": 64}
+
+
+def _run_fluid_lifecycle() -> int:
+    """Run the complete NoRender lifecycle in a fresh native process."""
+    from dexsim.engine.newton_physics import SPHSolverCfg
+    from embodichain.lab.sim.cfg import RigidObjectCfg
+    from embodichain.lab.sim.shapes import CubeCfg
 
-        sim = SimulationManager(
-            SimulationManagerCfg(
-                headless=True,
-                device="cuda",
-                num_envs=1,
-                physics_dt=0.001,
-                render_cfg=RenderCfg(renderer="no-render"),
-                physics_cfg=NewtonPhysicsCfg(
-                    num_substeps=2,
-                    collision_cfg=None,
-                    solver_cfg={
-                        "solver_type": "sph",
-                        "sph_method": "dfsph",
-                        "particle_support_radius": 0.0225,
-                        "volume_map_boundary_enabled": False,
-                    },
+    sim = SimulationManager(
+        SimulationManagerCfg(
+            headless=True,
+            device="cuda",
+            num_envs=1,
+            physics_dt=0.001,
+            render_cfg=RenderCfg(renderer="no-render"),
+            physics_cfg=NewtonPhysicsCfg(
+                num_substeps=2,
+                collision_cfg=None,
+                solver_cfg=SPHSolverCfg(
+                    sph_method="dfsph",
+                    particle_support_radius=0.0225,
+                    volume_map_boundary_enabled=False,
                 ),
-            )
+            ),
         )
-        fluid = rigid = None
-        try:
-            rigid = sim.add_rigid_object(
-                RigidObjectCfg(
-                    uid="boundary", body_type="kinematic", shape=CubeCfg(size=[0.1] * 3)
-                )
-            )
-            fluid = sim.add_fluid_object(
-                FluidObjectCfg(
-                    uid="water",
-                    particle_spacing=0.01,
-                    init_pos=(0.0, 0.0, 1.0),
-                    spawn=FluidSpawnCfg(kind="box", size=(0.04, 0.04, 0.04)),
-                    render=FluidRenderCfg(mode="none"),
-                )
+    )
+    fluid = rigid = None
+    try:
+        rigid = sim.add_rigid_object(
+            RigidObjectCfg(
+                uid="boundary", body_type="kinematic", shape=CubeCfg(size=[0.1] * 3)
             )
-            with pytest.raises(RuntimeError, match="prepare"):
-                _ = fluid.data
-            sim.prepare()
-            assert sim.get_fluid_object("water") is fluid
-            assert sim.get_asset("water") is fluid
-            assert fluid.num_particles == 64
-            original = fluid.data.default_particle_state_w
-            sim.update(step=3, render_final_step=False)
-            first = fluid.data.particle_state_w
-            assert torch.isfinite(first).all()
-            assert first[..., 2].mean() < original[..., 2].mean()
-            sim.reset_objects_state()
-            torch.testing.assert_close(fluid.data.particle_state_w, original)
-            sim.update(step=3, render_final_step=False)
-            torch.testing.assert_close(
-                fluid.data.particle_state_w, first, rtol=1e-5, atol=1e-6
+        )
+        fluid = sim.add_fluid_object(
+            FluidObjectCfg(
+                uid="water",
+                particle_spacing=0.01,
+                init_pos=(0.0, 0.0, 1.0),
+                spawn=FluidSpawnCfg(kind="box", size=(0.04, 0.04, 0.04)),
+                render=FluidRenderCfg(mode="none"),
             )
-            with pytest.raises(NotImplementedError, match="before prepare"):
-                sim.add_fluid_object(FluidObjectCfg(uid="late"))
-        finally:
-            fluid = rigid = None
-            sim.destroy(exit_process=False)
-            SimulationManager.flush_cleanup_queue()
+        )
+        with pytest.raises(RuntimeError, match="prepare"):
+            _ = fluid.data
+        sim.prepare()
+        assert sim.get_fluid_object("water") is fluid
+        assert sim.get_asset("water") is fluid
+        assert fluid.num_particles == 64
+        original = fluid.data.default_particle_state_w
+        sim.update(step=3, render_final_step=False)
+        first = fluid.data.particle_state_w
+        assert torch.isfinite(first).all()
+        assert first[..., 2].mean() < original[..., 2].mean()
+        sim.reset_objects_state()
+        torch.testing.assert_close(fluid.data.particle_state_w, original)
+        sim.update(step=3, render_final_step=False)
+        torch.testing.assert_close(
+            fluid.data.particle_state_w, first, rtol=1e-5, atol=1e-6
+        )
+        with pytest.raises(NotImplementedError, match="before prepare"):
+            sim.add_fluid_object(FluidObjectCfg(uid="late"))
+        return fluid.num_particles
+    finally:
+        fluid = rigid = None
+        sim.destroy(exit_process=False)
+        SimulationManager.flush_cleanup_queue()
+
+
+if __name__ == "__main__":
+    Path(sys.argv[1]).write_text(
+        json.dumps({"num_particles": _run_fluid_lifecycle()}) + "\n"
+    )
diff --git a/tests/sim/objects/test_visual_object.py b/tests/sim/objects/test_visual_object.py
index 9bb96ab53..1fcc34d94 100644
--- a/tests/sim/objects/test_visual_object.py
+++ b/tests/sim/objects/test_visual_object.py
@@ -493,3 +493,42 @@ def test_visual_material_read_respects_selected_environments():
     visual, _ = _visual()
     assert visual.get_visual_material_inst([1]) == [None]
     assert visual.get_visual_material_inst([]) == []
+
+
+def test_visual_binding_applies_optics_to_all_materials_and_preserves_reset():
+    from unittest.mock import call
+    from embodichain.lab.sim.material import VisualMaterialCfg
+
+    material_cfg = VisualMaterialCfg(
+        transmission=0.85,
+        volume_thickness=0.08,
+        volume_attenuation_distance=0.5,
+        volume_attenuation_color=(0.2, 0.6, 0.8),
+    )
+    visual = VisualObject(
+        VisualObjectCfg(
+            uid="glass",
+            shape=MeshCfg(fpath="/assets/glass.obj", visual_material=material_cfg),
+        )
+    )
+    visual._initialize_spawn_declaration(1)
+    handle = _Handle(0.0)
+    originals = [MagicMock(name="first"), MagicMock(name="second")]
+    for i, material in enumerate(originals):
+        material.get_name.return_value = f"material_{i}"
+        material.get_template.return_value.get_name.return_value = f"template_{i}"
+    handle.render.materials = originals.copy()
+    visual.attach_spawn_handles([handle])
+
+    visual.bind_spawn(SimpleNamespace(topology_revision=1, world=_World([handle])))
+    handle.render.materials = [None, None]
+    visual.reset()
+
+    assert handle.render.materials == originals
+    for material in originals:
+        assert material.set_pbr_param.call_args_list == [
+            call("transmission", 0.85),
+            call("volume_thickness", 0.08),
+            call("volume_attenuation_distance", 0.5),
+            call("volume_attenuation_color", 0.2, 0.6, 0.8),
+        ]
diff --git a/tests/sim/spawn/test_descriptors.py b/tests/sim/spawn/test_descriptors.py
index e441bfc91..921ef95cf 100644
--- a/tests/sim/spawn/test_descriptors.py
+++ b/tests/sim/spawn/test_descriptors.py
@@ -2380,3 +2380,49 @@ def test_rigid_descriptor_rejects_orientation_without_principal_moments() -> Non
     )
     with pytest.raises(ValueError, match="requires explicit principal inertia"):
         rigid_desc_from_cfg(cfg)
+
+
+@pytest.mark.parametrize(
+    "kind", ["rigid", "visual", "volume", "surface", "surface_visual"]
+)
+def test_shape_optical_transmission_reaches_spawn_material(kind):
+    from embodichain.lab.sim.cfg import (
+        RigidObjectCfg,
+        VisualObjectCfg,
+        VolumeDeformableObjectCfg,
+        SurfaceDeformableObjectCfg,
+    )
+    from embodichain.lab.sim.material import VisualMaterialCfg
+    from embodichain.lab.sim.spawn.descriptors import (
+        visual_desc_from_cfg,
+        volume_deformable_desc_from_cfg,
+        surface_deformable_desc_from_cfg,
+    )
+
+    cfg_type, compile_descriptor = {
+        "rigid": (RigidObjectCfg, rigid_desc_from_cfg),
+        "visual": (VisualObjectCfg, visual_desc_from_cfg),
+        "volume": (VolumeDeformableObjectCfg, volume_deformable_desc_from_cfg),
+        "surface": (SurfaceDeformableObjectCfg, surface_deformable_desc_from_cfg),
+        "surface_visual": (
+            SurfaceDeformableObjectCfg,
+            surface_deformable_desc_from_cfg,
+        ),
+    }[kind]
+    cfg = cfg_type(
+        uid="glass",
+        shape=MeshCfg(
+            fpath="/assets/glass.obj",
+            visual_material=VisualMaterialCfg(transmission=0.85),
+        ),
+    )
+
+    if kind == "surface_visual":
+        cfg.visual_shape = MeshCfg(
+            fpath="/assets/render.obj", visual_material=cfg.shape.visual_material
+        )
+        cfg.shape.visual_material = VisualMaterialCfg(transmission=0.1)
+
+    _, materials = compile_descriptor(cfg)
+
+    assert next(iter(materials.values())).transmission == 0.85
diff --git a/tests/sim/test_fluid_cfg.py b/tests/sim/test_fluid_cfg.py
index 3f98047c1..c451dddab 100644
--- a/tests/sim/test_fluid_cfg.py
+++ b/tests/sim/test_fluid_cfg.py
@@ -283,3 +283,78 @@ def test_fluid_renderer_gate_allows_supported_backends(
     assert SimulationManager.add_fluid_object(manager, cfg) is facade
     assert manager._fluid_objects["water"] is facade
     manager._spawn_scene.declare.assert_called_once()
+
+
+@pytest.mark.parametrize("representation", ["native", "mapping", "full_mapping"])
+@pytest.mark.parametrize("density,spacing", [(1000.0, 0.0015), (800.0, 0.003)])
+def test_solver_defaults_allow_fluid_owned_density_and_spacing(
+    representation, density, spacing, monkeypatch
+):
+    from dataclasses import asdict
+    from unittest.mock import Mock
+    from dexsim.engine.newton_physics import SPHSolverCfg
+    from embodichain.lab.sim import SimulationManager
+
+    solver = SPHSolverCfg(sph_method="dfsph")
+    if representation == "mapping":
+        solver = {"solver_type": "sph", "sph_method": "dfsph"}
+    elif representation == "full_mapping":
+        solver = asdict(solver)
+    manager = _fluid_manager_for_renderer("no-render")
+    manager.sim_config.physics_cfg.solver_cfg = solver
+    monkeypatch.setattr(SimulationManager, "_get_process_renderer", lambda: None)
+    monkeypatch.setattr("embodichain.lab.sim.sim_manager.FluidObject", Mock())
+    cfg = FluidObjectCfg(
+        uid="water",
+        density=density,
+        particle_spacing=spacing,
+        spawn=FluidSpawnCfg(positions=[[0.0, 0.0, 0.0]]),
+        render={"mode": "none"},
+    )
+
+    SimulationManager.add_fluid_object(manager, cfg)
+
+    descriptor = manager._spawn_scene.declare.call_args.args[2]
+    assert descriptor.density == density
+    assert descriptor.particle_spacing == spacing
+    if representation != "mapping":
+        values = asdict(solver) if representation == "native" else solver
+        assert values["rest_density"] == SPHSolverCfg().rest_density
+        assert values["particle_diameter"] == SPHSolverCfg().particle_diameter
+
+
+@pytest.mark.parametrize("representation", ["native", "mapping"])
+@pytest.mark.parametrize("conflict", ["rest_density", "particle_diameter", None])
+def test_solver_nondefault_physical_values_must_match_fluid(
+    representation, conflict, monkeypatch
+):
+    from dataclasses import asdict
+    from unittest.mock import Mock
+    from dexsim.engine.newton_physics import SPHSolverCfg
+    from embodichain.lab.sim import SimulationManager
+
+    density, spacing = 800.0, 0.003
+    solver = SPHSolverCfg(rest_density=density, particle_diameter=spacing)
+    if conflict is not None:
+        setattr(solver, conflict, 1200.0 if conflict == "rest_density" else 0.02)
+    if representation == "mapping":
+        solver = asdict(solver)
+    manager = _fluid_manager_for_renderer("no-render")
+    manager.sim_config.physics_cfg.solver_cfg = solver
+    monkeypatch.setattr(SimulationManager, "_get_process_renderer", lambda: None)
+    monkeypatch.setattr("embodichain.lab.sim.sim_manager.FluidObject", Mock())
+    cfg = FluidObjectCfg(
+        uid="water",
+        density=density,
+        particle_spacing=spacing,
+        spawn=FluidSpawnCfg(positions=[[0.0, 0.0, 0.0]]),
+        render={"mode": "none"},
+    )
+
+    if conflict is None:
+        SimulationManager.add_fluid_object(manager, cfg)
+        manager._spawn_scene.declare.assert_called_once()
+    else:
+        with pytest.raises(ValueError, match=f"Fluid {conflict} conflicts"):
+            SimulationManager.add_fluid_object(manager, cfg)
+        manager._spawn_scene.declare.assert_not_called()

From 848ca610f8a970ba1621606a107d55e26e8d6b06 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Fri, 23 Oct 2026 00:44:24 +0800
Subject: [PATCH 15/19] refactor(fluid): separate construction from source
 resolution

---
 .../topics/simulation-system/fluids.md        |  7 ++
 embodichain/lab/sim/objects/fluid.py          | 79 +++++++++++++++----
 embodichain/lab/sim/sim_manager.py            |  5 +-
 tests/sim/objects/test_fluid_object.py        | 79 +++++++++++++++++--
 tests/sim/spawn/test_scene.py                 |  6 +-
 tests/sim/test_fluid_cfg.py                   | 51 ++++++++++++
 6 files changed, 202 insertions(+), 25 deletions(-)

diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
index 22127568e..66af5be19 100644
--- a/agent_context/topics/simulation-system/fluids.md
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -49,6 +49,13 @@ binding and reset do not reopen source files. Particle count is fixed after
 preparation, and all fluids share density and spacing. Adding, replacing or
 removing rigid assets after preparing a particle scene requires a new scene.
 
+`FluidObject(cfg, device)` creates an unregistered facade without sampling or
+state allocation. The manager resolves the source recipe and reference pose,
+then privately initializes the facade's local particle template, initial
+rotation and write-invalidation hook. Spawn receives the same sampled points
+and resolved pose. Derived caches never become additional config fields;
+binding and reset reuse them while the input config remains a source recipe.
+
 ## Rendering and validation
 
 Native fluids require FastRT (`fast-rt`) or OfflineRT (`rt`). Hybrid's Vulkan
diff --git a/embodichain/lab/sim/objects/fluid.py b/embodichain/lab/sim/objects/fluid.py
index 29a37ff9d..e1bdf9e0a 100644
--- a/embodichain/lab/sim/objects/fluid.py
+++ b/embodichain/lab/sim/objects/fluid.py
@@ -73,25 +73,55 @@ class FluidObject:
     def __init__(
         self,
         cfg: FluidObjectCfg,
-        positions: np.ndarray,
-        pose: np.ndarray,
-        device: torch.device,
-        *,
-        on_write: Callable[[Sequence[int]], None],
+        device: torch.device = torch.device("cpu"),
     ) -> None:
+        """Create an unregistered facade without sampling or allocating state.
+
+        Args:
+            cfg: Particle-source recipe and initial physical/render settings.
+            device: Device for state and resolved source caches after declaration.
+        """
         self.cfg = deepcopy(cfg)
-        self.uid = cfg.uid
-        self.device = device
-        self._positions = torch.as_tensor(positions.copy(), device=device)
-        self._initial_pose = torch.as_tensor(pose.copy(), device=device)
+        self.uid = self.cfg.uid
+        self.device = torch.device(device)
+        self._rest_positions_local: torch.Tensor | None = None
+        self._initial_rotation: torch.Tensor | None = None
         self._entities: list[Any] = []
         self._spawn_result = None
         self._data: FluidObjectData | None = None
-        self._num_instances = 0
+        self._declared_num_instances: int | None = None
+        self._on_write: Callable[[Sequence[int]], None] | None = None
+
+    def _initialize_spawn_source(
+        self,
+        rest_positions_local: np.ndarray,
+        initial_rotation: np.ndarray,
+        *,
+        on_write: Callable[[Sequence[int]], None],
+    ) -> None:
+        """Cache Manager-resolved source data without changing the input config."""
+        if self._rest_positions_local is not None:
+            raise RuntimeError("Fluid source is already initialized.")
+        positions = np.asarray(rest_positions_local, dtype=np.float32)
+        rotation = np.asarray(initial_rotation, dtype=np.float32)
+        if positions.ndim != 2 or positions.shape[1] != 3 or not len(positions):
+            raise ValueError("Fluid source requires nonempty (N, 3) positions.")
+        if rotation.shape != (3, 3) or not np.isfinite(rotation).all():
+            raise ValueError("Fluid source requires a finite (3, 3) initial rotation.")
+        if not callable(on_write):
+            raise TypeError("Fluid state invalidation callback must be callable.")
+        self._rest_positions_local = torch.as_tensor(
+            positions.copy(), device=self.device
+        )
+        self._initial_rotation = torch.as_tensor(rotation.copy(), device=self.device)
         self._on_write = on_write
 
     def _initialize_spawn_declaration(self, num_instances: int) -> None:
-        self._num_instances = num_instances
+        if num_instances <= 0:
+            raise ValueError("FluidObject requires a positive instance count.")
+        if self._declared_num_instances not in (None, num_instances):
+            raise RuntimeError("FluidObject instance count is already declared.")
+        self._declared_num_instances = num_instances
 
     @property
     def is_declared(self) -> bool:
@@ -101,12 +131,18 @@ def is_declared(self) -> bool:
     @property
     def num_instances(self) -> int:
         """Number of scene-owned replicas."""
-        return self._num_instances
+        if self._declared_num_instances is None:
+            raise RuntimeError("FluidObject must be registered through SpawnScene.")
+        return self._declared_num_instances
 
     @property
     def num_particles(self) -> int:
         """Fixed particle count per replica."""
-        return len(self._positions)
+        if self._rest_positions_local is None:
+            raise RuntimeError(
+                "Fluid source requires SimulationManager.add_fluid_object()."
+            )
+        return len(self._rest_positions_local)
 
     @property
     def particle_mass(self) -> float:
@@ -135,6 +171,12 @@ def bind_spawn(self, scene: Any) -> None:
         """Bind Scene batch state and capture configured initial velocities."""
         if self._data is not None:
             return
+        if (
+            self._rest_positions_local is None
+            or self._initial_rotation is None
+            or self._on_write is None
+        ):
+            raise RuntimeError("Fluid source must be initialized before binding.")
         data = FluidObjectData(self._entities, scene, self.device)
         if data._state.n_nodes != self.num_particles:
             raise RuntimeError("Spawn changed the fluid particle count.")
@@ -142,7 +184,7 @@ def bind_spawn(self, scene: Any) -> None:
         velocity = torch.as_tensor(
             self.cfg.spawn.initial_velocity, device=self.device, dtype=torch.float32
         )
-        state[..., 3:] = velocity @ self._initial_pose[:3, :3].T
+        state[..., 3:] = velocity @ self._initial_rotation.T
         data._state._apply_nodal_state(
             state[..., :3], state[..., 3:], list(range(self.num_instances))
         )
@@ -186,13 +228,15 @@ def write_particle_state_to_sim(
             raise ValueError("Fluid state must be finite.")
         if not ids:
             return
+        assert self._on_write is not None
         self._on_write(ids)
         data._state._apply_nodal_state(state[..., :3], state[..., 3:], ids)
 
     def reset(self, env_ids: Sequence[int] | None = None) -> None:
         """Restore selected initial particle states without advancing time."""
+        data = self.data
         ids = self._indices(env_ids)
-        self.write_particle_state_to_sim(self.data.default_particle_state_w[ids], ids)
+        self.write_particle_state_to_sim(data.default_particle_state_w[ids], ids)
 
     def reset_to_pose(
         self, pose_w: torch.Tensor, env_ids: Sequence[int] | None = None
@@ -203,6 +247,7 @@ def reset_to_pose(
             pose_w: Finite ``(selected E, 7)`` poses with unit quaternions.
             env_ids: Unique instance indices, or None for all.
         """
+        _ = self.data
         ids = self._indices(env_ids)
         if tuple(pose_w.shape) != (len(ids), 7) or not torch.isfinite(pose_w).all():
             raise ValueError("pose_w must be finite (selected E, 7).")
@@ -214,8 +259,10 @@ def reset_to_pose(
         ):
             raise ValueError("pose_w requires unit xyzw quaternions.")
         rotation = matrix_from_quat(pose[:, 3:])
+        assert self._rest_positions_local is not None
         points = (
-            torch.einsum("eij,nj->eni", rotation, self._positions) + pose[:, None, :3]
+            torch.einsum("eij,nj->eni", rotation, self._rest_positions_local)
+            + pose[:, None, :3]
         )
         velocity = torch.as_tensor(
             self.cfg.spawn.initial_velocity, device=self.device, dtype=torch.float32
diff --git a/embodichain/lab/sim/sim_manager.py b/embodichain/lab/sim/sim_manager.py
index ad3fcf97b..4d61a7ab5 100644
--- a/embodichain/lab/sim/sim_manager.py
+++ b/embodichain/lab/sim/sim_manager.py
@@ -2931,8 +2931,9 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
             render_material=material,
             per_env=True,
         )
-        fluid = FluidObject(
-            cfg, points, pose, self.device, on_write=self._invalidate_fluid_state
+        fluid = FluidObject(cfg, self.device)
+        fluid._initialize_spawn_source(
+            points, pose[:3, :3], on_write=self._invalidate_fluid_state
         )
         self._spawn_scene.declare("fluid_object", cfg.uid, descriptor, facade=fluid)
         self._fluid_objects[cfg.uid] = fluid
diff --git a/tests/sim/objects/test_fluid_object.py b/tests/sim/objects/test_fluid_object.py
index 9cd19b2bc..b02033f7f 100644
--- a/tests/sim/objects/test_fluid_object.py
+++ b/tests/sim/objects/test_fluid_object.py
@@ -54,10 +54,10 @@ def fluid():
     scene.create_particle_set_batch = lambda entities: ParticleBatch(scene, entities)
     result = FluidObject(
         FluidObjectCfg(uid="water", spawn=FluidSpawnCfg(positions=points)),
-        points,
-        np.eye(4, dtype=np.float32),
         torch.device("cpu"),
-        on_write=lambda ids: None,
+    )
+    result._initialize_spawn_source(
+        points, np.eye(3, dtype=np.float32), on_write=lambda ids: None
     )
     result._initialize_spawn_declaration(2)
     result.attach_spawn_handles(
@@ -121,10 +121,10 @@ def test_integer_initial_velocity_is_normalized():
             uid="water",
             spawn=FluidSpawnCfg(positions=points, initial_velocity=(1, 0, 0)),
         ),
-        points,
-        np.eye(4, dtype=np.float32),
         torch.device("cpu"),
-        on_write=lambda ids: None,
+    )
+    fluid._initialize_spawn_source(
+        points, np.eye(3, dtype=np.float32), on_write=lambda ids: None
     )
     fluid._initialize_spawn_declaration(1)
     fluid.attach_spawn_handles([SimpleNamespace(index=0, particle_count=1)])
@@ -149,3 +149,70 @@ def test_fluid_write_respects_render_frame_cache_scope(inside_frame):
     SimulationManager._ensure_render_state(sim)
     assert len(calls) == (1 if inside_frame else 2)
     assert sim._render_state_published is (True if inside_frame else None)
+
+
+def test_constructor_only_copies_config_without_sampling(monkeypatch):
+    from unittest.mock import Mock
+
+    sample = Mock(side_effect=AssertionError("Construction must not sample."))
+    monkeypatch.setattr(FluidSpawnCfg, "sample", sample)
+    cfg = FluidObjectCfg(uid="water", spawn=FluidSpawnCfg(kind="box"))
+
+    fluid = FluidObject(cfg)
+    cfg.spawn.size = (9.0, 9.0, 9.0)
+
+    assert fluid.device == torch.device("cpu")
+    assert fluid.is_declared
+    assert fluid.cfg.spawn.size == (0.05, 0.05, 0.05)
+    sample.assert_not_called()
+    with pytest.raises(RuntimeError, match="source"):
+        _ = fluid.num_particles
+    with pytest.raises(RuntimeError, match="registered"):
+        _ = fluid.num_instances
+
+
+def test_binding_requires_a_resolved_source():
+    from unittest.mock import Mock
+
+    fluid = FluidObject(FluidObjectCfg(uid="water"))
+    fluid._initialize_spawn_declaration(1)
+    fluid.attach_spawn_handles([SimpleNamespace(index=0, particle_count=1)])
+    scene = SimpleNamespace(create_particle_set_batch=Mock())
+
+    with pytest.raises(RuntimeError, match="source"):
+        fluid.bind_spawn(scene)
+
+    scene.create_particle_set_batch.assert_not_called()
+
+
+def test_source_cache_is_independent_and_rotates_initial_velocity():
+    points = np.array([[0.01, 0.0, 0.0]], dtype=np.float32)
+    rotation = np.array([[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
+    fluid = FluidObject(
+        FluidObjectCfg(
+            uid="water",
+            init_rot=(0.0, 0.0, 90.0),
+            spawn=FluidSpawnCfg(positions=points, initial_velocity=(1, 0, 0)),
+        )
+    )
+    writes = []
+    fluid._initialize_spawn_source(points, rotation, on_write=writes.append)
+    fluid._initialize_spawn_declaration(1)
+    scene = SimpleNamespace(state=torch.tensor([[[0.0, 0.01, 0.0, 0.0, 0.0, 0.0]]]))
+    scene.create_particle_set_batch = lambda entities: ParticleBatch(scene, entities)
+    fluid.attach_spawn_handles([SimpleNamespace(index=0, particle_count=1)])
+    points.fill(99)
+    rotation.fill(0)
+
+    fluid.bind_spawn(scene)
+
+    torch.testing.assert_close(
+        fluid.data.particle_vel_w, torch.tensor([[[0.0, 1.0, 0.0]]])
+    )
+    fluid.reset_to_pose(torch.tensor([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]]))
+    torch.testing.assert_close(
+        fluid.data.particle_pos_w, torch.tensor([[[0.01, 0.0, 0.0]]])
+    )
+    assert writes == [[0]]
+    with pytest.raises(RuntimeError, match="already"):
+        fluid._initialize_spawn_source(points, rotation, on_write=writes.append)
diff --git a/tests/sim/spawn/test_scene.py b/tests/sim/spawn/test_scene.py
index aeea7eb87..d9fc0508c 100644
--- a/tests/sim/spawn/test_scene.py
+++ b/tests/sim/spawn/test_scene.py
@@ -26,6 +26,7 @@
 from embodichain.lab.sim.objects import (
     Articulation,
     DeformableObject,
+    FluidObject,
     RigidObject,
     RigidObjectGroup,
     Robot,
@@ -161,7 +162,7 @@ def test_rigid_object_receives_instance_count_only_when_declared() -> None:
 
 @pytest.mark.parametrize(
     "facade_type",
-    [RigidObject, Articulation, Robot, RigidObjectGroup, DeformableObject],
+    [RigidObject, Articulation, Robot, RigidObjectGroup, DeformableObject, FluidObject],
 )
 def test_object_constructors_hide_spawn_lifecycle_parameters(
     facade_type: type[object],
@@ -170,6 +171,9 @@ def test_object_constructors_hide_spawn_lifecycle_parameters(
 
     assert "declared_num_instances" not in parameters
     assert "spawn_result" not in parameters
+    assert "positions" not in parameters
+    assert "pose" not in parameters
+    assert "on_write" not in parameters
 
 
 def test_runtime_config_attaches_articulation_before_preparing_it() -> None:
diff --git a/tests/sim/test_fluid_cfg.py b/tests/sim/test_fluid_cfg.py
index c451dddab..9ffd4924d 100644
--- a/tests/sim/test_fluid_cfg.py
+++ b/tests/sim/test_fluid_cfg.py
@@ -358,3 +358,54 @@ def test_solver_nondefault_physical_values_must_match_fluid(
         with pytest.raises(ValueError, match=f"Fluid {conflict} conflicts"):
             SimulationManager.add_fluid_object(manager, cfg)
         manager._spawn_scene.declare.assert_not_called()
+
+
+def test_manager_resolves_source_once_without_rewriting_config(monkeypatch):
+    from unittest.mock import Mock
+    from types import SimpleNamespace
+    from embodichain.lab.sim import SimulationManager
+    from embodichain.lab.sim.cfg import RigidObjectCfg
+    from embodichain.lab.sim.spawn.descriptors import _pose_from_cfg
+
+    points = np.array([[0.01, 0.0, 0.0]], dtype=np.float32)
+    sampled = []
+
+    def sample(source, spacing):
+        sampled.append(spacing)
+        return points.copy()
+
+    monkeypatch.setattr(FluidSpawnCfg, "sample", sample)
+    monkeypatch.setattr(SimulationManager, "_get_process_renderer", lambda: None)
+    factory = Mock()
+    monkeypatch.setattr("embodichain.lab.sim.sim_manager.FluidObject", factory)
+    manager = _fluid_manager_for_renderer("no-render")
+    reference = RigidObjectCfg(
+        uid="cup", init_pos=(0.2, 0.3, 0.4), init_rot=(10.0, 20.0, 30.0)
+    )
+    manager._rigid_objects["cup"] = SimpleNamespace(cfg=reference)
+    cfg = FluidObjectCfg(
+        uid="water",
+        init_pos=(0.01, 0.02, 0.03),
+        init_rot=(15.0, 25.0, 35.0),
+        spawn=FluidSpawnCfg(kind="box", reference_uid="cup"),
+        render={"mode": "none"},
+    )
+    before = cfg.to_dict()
+    expected_pose = _pose_from_cfg(reference) @ _pose_from_cfg(cfg)
+
+    fluid = SimulationManager.add_fluid_object(manager, cfg)
+
+    assert sampled == [cfg.particle_spacing]
+    assert cfg.to_dict() == before
+    assert len(factory.call_args.args) == 2
+    assert isinstance(factory.call_args.args[0], FluidObjectCfg)
+    assert factory.call_args.args[1] == manager.device
+    assert factory.call_args.kwargs == {}
+    fluid._initialize_spawn_source.assert_called_once()
+    args = fluid._initialize_spawn_source.call_args
+    np.testing.assert_array_equal(args.args[0], points)
+    np.testing.assert_allclose(args.args[1], expected_pose[:3, :3])
+    assert args.kwargs["on_write"] is manager._invalidate_fluid_state
+    descriptor = manager._spawn_scene.declare.call_args.args[2]
+    np.testing.assert_array_equal(descriptor.positions, points)
+    np.testing.assert_allclose(descriptor.pose, expected_pose)

From c24a6fd151c7a862b7891a15efce06eba4511019 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Fri, 23 Oct 2026 01:43:31 +0800
Subject: [PATCH 16/19] perf(fluid): vectorize render clipping across particle
 ranges

---
 embodichain/lab/sim/physics/newton.py        |  66 ++-
 scripts/benchmark/scene/fluid_render_sync.py | 462 +++++++++++++++++++
 tests/sim/test_fluid_render_sync.py          | 189 ++++++++
 3 files changed, 712 insertions(+), 5 deletions(-)
 create mode 100644 scripts/benchmark/scene/fluid_render_sync.py
 create mode 100644 tests/sim/test_fluid_render_sync.py

diff --git a/embodichain/lab/sim/physics/newton.py b/embodichain/lab/sim/physics/newton.py
index 28e17ad5e..7c5077d22 100644
--- a/embodichain/lab/sim/physics/newton.py
+++ b/embodichain/lab/sim/physics/newton.py
@@ -28,6 +28,7 @@
 
 if TYPE_CHECKING:
     import dexsim
+    import torch
 
     from embodichain.lab.sim.sim_manager import SimulationManagerCfg
     from embodichain.lab.sim.objects.fluid import FluidObject
@@ -60,6 +61,8 @@ def __init__(self, manager) -> None:
         self._differentiable_runtime = None
         self._runtime_device: str | None = None
         self._configured_solver_type: str | None = None
+        self._fluid_render_clip_key: tuple[object, ...] | None = None
+        self._fluid_render_clip_z: torch.Tensor | None = None
 
     @property
     def solver_type(self) -> str | None:
@@ -116,15 +119,24 @@ def sync_render_state(self, result: "dexsim.scene.Scene") -> None:
         fluids = getattr(self._manager, "_fluid_objects", {})
         clipped = [f for f in fluids.values() if f.cfg.render.clip_below_z is not None]
         if not clipped:
+            self._fluid_render_clip_key = None
+            self._fluid_render_clip_z = None
             backend.sync_to_dexsim(result.world)
             return
-        positions = wp.to_torch(backend.state_0.particle_q).clone()
+        ranges = []
         for fluid in clipped:
             for entity in fluid._entities:
-                binding = entity._physics_binding
-                start = binding.particle_start
-                selected = positions[start : start + entity.particle_count]
-                selected[selected[:, 2] < fluid.cfg.render.clip_below_z] = float("nan")
+                start = entity._physics_binding.particle_start
+                ranges.append(
+                    (
+                        start,
+                        start + entity.particle_count,
+                        fluid.cfg.render.clip_below_z,
+                    )
+                )
+        positions = self._clip_fluid_render_positions(
+            wp.to_torch(backend.state_0.particle_q), tuple(ranges)
+        )
         backend.render_sync.sync(
             result.world,
             backend.state_0.body_q,
@@ -132,6 +144,47 @@ def sync_render_state(self, result: "dexsim.scene.Scene") -> None:
             simulation_time=backend.sim_time,
         )
 
+    def _clip_fluid_render_positions(
+        self,
+        positions: torch.Tensor,
+        ranges: tuple[tuple[int, int, float], ...],
+    ) -> torch.Tensor:
+        """Create an owned render copy with one comparison and one selection.
+
+        A full-buffer fluid uses a scalar cutoff. Mixed particle scenes cache
+        immutable per-particle cutoffs; changing layout, dtype, device or CUDA
+        stream replaces that cache. Each output owns fresh storage so a later
+        publication cannot overwrite data still consumed by the render bridge.
+        """
+        import torch
+
+        if len(ranges) == 1 and ranges[0][:2] == (0, len(positions)):
+            self._fluid_render_clip_key = None
+            self._fluid_render_clip_z = None
+            return torch.where(
+                positions[:, 2:3] < ranges[0][2], float("nan"), positions
+            )
+
+        # Cache reads and initialization stay on the same Torch stream. A stream
+        # change gets a new tensor, leaving the old allocation's lifetime ordered
+        # on its creation stream without adding a device-wide synchronization.
+        stream = (
+            torch.cuda.current_stream(positions.device).cuda_stream
+            if positions.is_cuda
+            else None
+        )
+        key = (positions.shape, positions.dtype, positions.device, stream, ranges)
+        if key != self._fluid_render_clip_key:
+            cutoffs = positions.new_full((len(positions), 1), float("-inf"))
+            for start, end, height in ranges:
+                cutoffs[start:end].fill_(height)
+            self._fluid_render_clip_z = cutoffs
+            self._fluid_render_clip_key = key
+        assert self._fluid_render_clip_z is not None
+        return torch.where(
+            positions[:, 2:3] < self._fluid_render_clip_z, float("nan"), positions
+        )
+
     @property
     def supports_fluid_worlds(self) -> bool:
         """Whether the loaded SPH artifact supports isolated worlds and reset."""
@@ -213,6 +266,9 @@ def prepare_for_teardown(self) -> None:
         if self._runtime_device is not None and self._runtime_device.startswith("cuda"):
             wp.synchronize_device(self._runtime_device)
 
+        self._fluid_render_clip_key = None
+        self._fluid_render_clip_z = None
+
         world = getattr(self._manager, "_world", None)
         if world is None:
             return
diff --git a/scripts/benchmark/scene/fluid_render_sync.py b/scripts/benchmark/scene/fluid_render_sync.py
new file mode 100644
index 000000000..1e7ddc4e1
--- /dev/null
+++ b/scripts/benchmark/scene/fluid_render_sync.py
@@ -0,0 +1,462 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Compare fluid render clipping and optional native surface publication.
+
+Run: python -m scripts.benchmark.scene.fluid_render_sync --device cuda:0 --native
+Inputs are device resident. Physics stepping is excluded. The optional native
+cases measure bridge publication and a complete camera frame separately.
+"""
+
+from __future__ import annotations
+
+import argparse
+from collections.abc import Callable
+from datetime import datetime
+from importlib.metadata import PackageNotFoundError, version
+from pathlib import Path
+import platform
+import statistics
+import time
+from types import SimpleNamespace
+
+import numpy as np
+import psutil
+import torch
+import warp as wp
+
+from embodichain.lab.sim.physics.newton import NewtonPhysicsBackend
+
+
+def _legacy_clip(
+    positions: torch.Tensor, ranges: tuple[tuple[int, int, float], ...]
+) -> torch.Tensor:
+    """Reproduce the original clone and per-fluid boolean assignment."""
+    output = positions.clone()
+    for start, end, height in ranges:
+        selected = output[start:end]
+        selected[selected[:, 2] < height] = float("nan")
+    return output
+
+
+def _synchronize(device: torch.device) -> None:
+    """Complete all participating CUDA streams before recording wall time."""
+    if device.type == "cuda":
+        torch.cuda.synchronize(device)
+
+
+def _measure(
+    operation: Callable[[], object], device: torch.device, args: argparse.Namespace
+) -> dict[str, object]:
+    """Measure steady-state completed calls and a separate retained output."""
+    cuda = device.type == "cuda"
+    _synchronize(device)
+    initial = torch.cuda.memory_allocated(device) if cuda else 0
+    start = time.perf_counter()
+    result = operation()
+    _synchronize(device)
+    first_call_ms = (time.perf_counter() - start) * 1000
+    del result
+    for _ in range(args.warmup):
+        result = operation()
+        _synchronize(device)
+        del result
+    persistent = torch.cuda.memory_allocated(device) - initial if cuda else None
+    samples = []
+    for _ in range(args.repeats):
+        _synchronize(device)
+        start = time.perf_counter()
+        result = operation()
+        _synchronize(device)
+        samples.append((time.perf_counter() - start) * 1000)
+        del result
+
+    process = psutil.Process()
+    cpu_before = process.memory_info().rss
+    gpu_before = torch.cuda.memory_allocated(device) if cuda else 0
+    if cuda:
+        torch.cuda.reset_peak_memory_stats(device)
+    result = operation()
+    _synchronize(device)
+    mib = 1024**2
+    memory = {
+        "cpu_delta_mb": (process.memory_info().rss - cpu_before) / mib,
+        "gpu_delta_mb": (
+            (torch.cuda.memory_allocated(device) - gpu_before) / mib if cuda else None
+        ),
+        "peak_gpu_mb": torch.cuda.max_memory_allocated(device) / mib if cuda else None,
+        "extra_peak_gpu_mb": (
+            (torch.cuda.max_memory_allocated(device) - gpu_before) / mib
+            if cuda
+            else None
+        ),
+        "warmup_gpu_delta_mb": persistent / mib if persistent is not None else None,
+    }
+    del result
+    return {
+        "cost_time_ms": statistics.median(samples),
+        "p95_ms": float(np.percentile(samples, 95)),
+        "first_call_ms": first_call_ms,
+        **memory,
+    }
+
+
+def _paired_latencies(
+    operations: dict[str, Callable[[], object]],
+    device: torch.device,
+    args: argparse.Namespace,
+) -> dict[str, dict[str, float]]:
+    """Alternate equivalent calls to reduce GPU clock and thermal-order bias."""
+    samples: dict[str, list[float]] = {name: [] for name in operations}
+    order = list(operations)
+    for index in range(args.warmup + args.repeats):
+        for name in order if index % 2 == 0 else reversed(order):
+            _synchronize(device)
+            start = time.perf_counter()
+            result = operations[name]()
+            _synchronize(device)
+            if index >= args.warmup:
+                samples[name].append((time.perf_counter() - start) * 1000)
+            del result
+    return {
+        name: {
+            "cost_time_ms": statistics.median(values),
+            "p95_ms": float(np.percentile(values, 95)),
+        }
+        for name, values in samples.items()
+    }
+
+
+def _benchmark_clipping(
+    count: int, fluids: int, device: torch.device, args: argparse.Namespace
+) -> tuple[list[dict[str, object]], list[dict[str, object]]]:
+    """Compare equivalent clipping masks, including partial particle ranges."""
+    generator = torch.Generator(device=device).manual_seed(42)
+    positions = torch.randn((count, 3), device=device, generator=generator)
+    before = positions.clone()
+    # Multiple ranges leave gaps to exercise particles outside the clipped fluids.
+    ranges = tuple(
+        (
+            count * i // fluids,
+            count * (i + 1) // fluids - (1 if fluids > 1 else 0),
+            0.1 * i,
+        )
+        for i in range(fluids)
+    )
+    backend = NewtonPhysicsBackend(SimpleNamespace())
+    operations = {
+        "legacy": lambda: _legacy_clip(positions, ranges),
+        "vectorized": lambda: backend._clip_fluid_render_positions(positions, ranges),
+    }
+    expected = positions.clone()
+    heights = torch.full((count,), float("-inf"), device=device)
+    for start, end, height in ranges:
+        heights[start:end] = height
+    expected[positions[:, 2] < heights] = float("nan")
+    perf_rows, metric_rows = [], []
+    for algorithm, operation in operations.items():
+        # Warmup is part of _measure; correctness checks must not prefill its cache.
+        perf = _measure(operation, device, args)
+        cutoff = backend._fluid_render_clip_z if algorithm == "vectorized" else None
+        perf["cutoff_cache_mb"] = (
+            cutoff.numel() * cutoff.element_size() / 1024**2
+            if cutoff is not None
+            else 0.0
+        )
+        actual = operation()
+        torch.testing.assert_close(actual, expected, equal_nan=True, rtol=0, atol=0)
+        torch.testing.assert_close(positions, before, rtol=0, atol=0)
+        identity = {
+            "scope": "clipping",
+            "particles": count,
+            "fluids": fluids,
+            "algorithm": algorithm,
+        }
+        perf_rows.append({**identity, **perf})
+        metric_rows.append({**identity, "success_rate": 1.0, "source_unchanged": True})
+        del actual
+    paired = _paired_latencies(operations, device, args)
+    for row in perf_rows:
+        row.update(paired[str(row["algorithm"])])
+    return perf_rows, metric_rows
+
+
+def _benchmark_native(
+    count: int, device: torch.device, args: argparse.Namespace
+) -> tuple[list[dict[str, object]], list[dict[str, object]]]:
+    """Measure full native surface publication with identical quality settings."""
+    from dexsim.engine.newton_physics.backend_registry import get_newton_backend
+    from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
+    from embodichain.lab.sim.cfg import (
+        FluidObjectCfg,
+        FluidRenderCfg,
+        FluidSpawnCfg,
+        NewtonPhysicsCfg,
+        RenderCfg,
+    )
+    from embodichain.lab.sim.sensors import CameraCfg
+
+    side = int(np.ceil(count ** (1 / 3)))
+    points = np.column_stack(np.unravel_index(np.arange(count), (side,) * 3))
+    points = np.asarray((points - side / 2) * 0.003, dtype=np.float32)
+    sim = SimulationManager(
+        SimulationManagerCfg(
+            headless=True,
+            device=str(device),
+            num_envs=1,
+            render_cfg=RenderCfg(renderer="fast-rt"),
+            physics_cfg=NewtonPhysicsCfg(
+                num_substeps=1,
+                collision_cfg=None,
+                solver_cfg={"solver_type": "sph", "volume_map_boundary_enabled": False},
+            ),
+        )
+    )
+    fluid = camera = None
+    try:
+        fluid = sim.add_fluid_object(
+            FluidObjectCfg(
+                uid="water",
+                particle_spacing=0.003,
+                spawn=FluidSpawnCfg(positions=points),
+                render=FluidRenderCfg(
+                    mode="mesh_surface",
+                    clip_below_z=0.0,
+                    mesh_surface={"gpu_enabled": True, "max_res": 128},
+                ),
+            )
+        )
+        camera = sim.add_sensor(
+            CameraCfg(
+                uid="benchmark_camera",
+                width=320,
+                height=240,
+                intrinsics=(350, 350, 160, 120),
+                extrinsics=CameraCfg.ExtrinsicsCfg(
+                    eye=(0.18, -0.24, 0.28), target=(0, 0, 0), up=(0, 0, 1)
+                ),
+            )
+        )
+        sim.prepare()
+        native = get_newton_backend(sim.spawn_result.world)
+        positions = wp.to_torch(native.state_0.particle_q)
+        before = positions.clone()
+
+        def legacy() -> None:
+            # Include the manager/backend lookup and range traversal performed
+            # by the previous public sync_render_state implementation.
+            result = sim.spawn_result
+            current = get_newton_backend(result.world)
+            clipped_fluids = [
+                item
+                for item in sim._fluid_objects.values()
+                if item.cfg.render.clip_below_z is not None
+            ]
+            clipped = wp.to_torch(current.state_0.particle_q).clone()
+            for item in clipped_fluids:
+                for entity in item._entities:
+                    start = entity._physics_binding.particle_start
+                    selected = clipped[start : start + entity.particle_count]
+                    selected[selected[:, 2] < item.cfg.render.clip_below_z] = float(
+                        "nan"
+                    )
+            current.render_sync.sync(
+                result.world,
+                current.state_0.body_q,
+                wp.from_torch(clipped, dtype=wp.vec3),
+                simulation_time=current.sim_time,
+            )
+            if sim._render_state_published is not None:
+                sim._render_state_published = True
+
+        perf_rows, metric_rows = [], []
+        for scope in ("native bridge publication", "native camera frame"):
+            operations = {}
+            for algorithm, publish in {
+                "legacy": legacy,
+                "vectorized": sim.sync_render_state,
+            }.items():
+
+                def operation(publish: Callable[[], None] = publish) -> object:
+                    publish()
+                    if scope == "native camera frame":
+                        sim._world.render_camera_group([camera.group_id])
+                        camera.update(fetch_only=True)
+                        return camera.get_data()["color"]
+                    return None
+
+                operations[algorithm] = operation
+                perf = _measure(operation, device, args)
+                cutoff = (
+                    sim.physics._fluid_render_clip_z
+                    if algorithm == "vectorized"
+                    else None
+                )
+                perf["cutoff_cache_mb"] = (
+                    cutoff.numel() * cutoff.element_size() / 1024**2
+                    if cutoff is not None
+                    else 0.0
+                )
+                torch.testing.assert_close(positions, before, rtol=0, atol=0)
+                identity = {
+                    "scope": scope,
+                    "particles": count,
+                    "fluids": 1,
+                    "algorithm": algorithm,
+                }
+                perf_rows.append({**identity, **perf})
+                metric_rows.append(
+                    {**identity, "success_rate": 1.0, "source_unchanged": True}
+                )
+            paired = _paired_latencies(operations, device, args)
+            for row in perf_rows:
+                if row["scope"] == scope:
+                    row.update(paired[str(row["algorithm"])])
+        return perf_rows, metric_rows
+    finally:
+        fluid = camera = None
+        sim.destroy(exit_process=False)
+        SimulationManager.flush_cleanup_queue()
+
+
+def _write_report(
+    path: Path,
+    perf_rows: list[dict[str, object]],
+    metric_rows: list[dict[str, object]],
+    notes: list[str],
+) -> None:
+    """Write exactly three tables, including every evaluated algorithm."""
+    leaderboard = []
+    for algorithm in sorted({str(row["algorithm"]) for row in metric_rows}):
+        results = [row for row in metric_rows if row["algorithm"] == algorithm]
+        leaderboard.append(
+            {
+                "algorithm": algorithm,
+                "overall_success_rate": statistics.mean(
+                    float(row["success_rate"]) for row in results
+                ),
+                "cases": len(results),
+            }
+        )
+    leaderboard.sort(key=lambda row: -float(row["overall_success_rate"]))
+    leaderboard = [{"rank": index, **row} for index, row in enumerate(leaderboard, 1)]
+    lines = ["# Fluid render synchronization benchmark", "", *notes, ""]
+    for title, rows in (
+        ("Time & Memory", perf_rows),
+        ("Success & Other Metrics", metric_rows),
+        ("Leaderboard", leaderboard),
+    ):
+        lines.extend([f"## {title}", ""])
+        if rows:
+            headers = list(rows[0])
+            lines.extend(
+                [
+                    "| " + " | ".join(headers) + " |",
+                    "| " + " | ".join(["---"] * len(headers)) + " |",
+                ]
+            )
+            for row in rows:
+                values = [
+                    f"{row[key]:.6f}" if isinstance(row[key], float) else str(row[key])
+                    for key in headers
+                ]
+                lines.append("| " + " | ".join(values) + " |")
+        else:
+            lines.extend(["| status |", "| --- |", "| skipped |"])
+        lines.append("")
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text("\n".join(lines), encoding="utf-8")
+
+
+def run_all_benchmarks(args: argparse.Namespace) -> None:
+    """Run clipping/native scopes and save one reproducible Markdown report."""
+    device = torch.device(args.device)
+    notes = [
+        f"- Generated: {datetime.now().isoformat(timespec='seconds')}",
+        f"- CPU: {platform.processor()}; Torch {torch.__version__}; Warp {wp.__version__}",
+        f"- Device: {device}; dtype=float32; seed=42; CPU threads={torch.get_num_threads()}",
+        f"- Warmup={args.warmup}; repeats={args.repeats}; median/p95 completed-call wall time.",
+        "- Latency uses paired alternating implementation order. Memory/cache statistics use a separate warmed pass.",
+        "- First-call timing precedes warmup and includes cutoff-cache setup; process/World startup and input generation are excluded.",
+        "- Device-resident inputs; device-wide CUDA synchronization; no physics steps. Native camera scope includes 320x240 FastRT rendering and image fetch.",
+        "- CPU memory is retained RSS delta. GPU memory covers the Torch allocator only, excluding native/Warp allocations.",
+        "- Incremental peak is measured separately after warmup. Cutoff cache reports logical buffer bytes; warmup GPU delta may include unrelated lazy allocation/release.",
+        "- Clipping quality checks exact positions/NaN masks and source preservation. Native success checks completed publication and unchanged physics; camera pixels are covered by test_fluid_visibility.py.",
+        "- Native surface settings: FastRT, spp=1, mesh_surface GPU enabled, max_res=128; other RenderCfg/FluidMeshSurfaceRenderDesc defaults.",
+    ]
+    try:
+        notes.append(f"- DexSim distribution: {version('dexsim_engine')}")
+    except PackageNotFoundError:
+        notes.append("- DexSim distribution metadata unavailable.")
+    notes.extend(f"- {note}" for note in args.note)
+    perf_rows, metric_rows = [], []
+    if device.type == "cuda" and not torch.cuda.is_available():
+        notes.append("- Skipped: CUDA is unavailable.")
+    else:
+        if device.type == "cuda":
+            notes.append(f"- GPU: {torch.cuda.get_device_name(device)}")
+        with torch.no_grad():
+            for count in args.sizes:
+                for fluids in args.fluid_counts:
+                    perf, metrics = _benchmark_clipping(count, fluids, device, args)
+                    perf_rows.extend(perf)
+                    metric_rows.extend(metrics)
+            if args.native and device.type == "cuda":
+                for count in args.native_sizes:
+                    perf, metrics = _benchmark_native(count, device, args)
+                    perf_rows.extend(perf)
+                    metric_rows.extend(metrics)
+            elif args.native:
+                notes.append("- Native publication skipped: CUDA is required.")
+    _write_report(args.output, perf_rows, metric_rows, notes)
+    for row in perf_rows:
+        print(
+            f"{row['scope']}: n={row['particles']} fluids={row['fluids']} "
+            f"{row['algorithm']} {row['cost_time_ms']:.6f} ms"
+        )
+    print(f"Markdown report saved: {args.output}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--device", default="cuda:0")
+    parser.add_argument(
+        "--sizes", nargs="+", type=int, default=[1728, 76607, 1_000_000]
+    )
+    parser.add_argument("--fluid-counts", nargs="+", type=int, default=[1, 8])
+    parser.add_argument("--native", action="store_true")
+    parser.add_argument("--native-sizes", nargs="+", type=int, default=[1728, 76607])
+    parser.add_argument("--warmup", type=int, default=10)
+    parser.add_argument("--repeats", type=int, default=60)
+    parser.add_argument(
+        "--note", action="append", default=[], help="Additional measurement conditions"
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("outputs/benchmarks")
+        / f"fluid_render_sync_{datetime.now():%Y%m%d_%H%M%S}.md",
+    )
+    args = parser.parse_args()
+    if (
+        args.warmup < 1
+        or args.repeats < 1
+        or min(args.sizes + args.native_sizes + args.fluid_counts) < 1
+    ):
+        parser.error("sizes, fluid counts, warmup and repeats must be positive")
+    if min(args.sizes) < max(args.fluid_counts):
+        parser.error("particle counts must be at least the number of fluid ranges")
+    run_all_benchmarks(args)
diff --git a/tests/sim/test_fluid_render_sync.py b/tests/sim/test_fluid_render_sync.py
new file mode 100644
index 000000000..849b0c4d8
--- /dev/null
+++ b/tests/sim/test_fluid_render_sync.py
@@ -0,0 +1,189 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""Fluid render clipping preserves physics state and publication ownership."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pytest
+import torch
+import warp as wp
+
+from embodichain.lab.sim.physics.newton import NewtonPhysicsBackend
+
+pytestmark = pytest.mark.no_sim
+
+
+def _backend() -> NewtonPhysicsBackend:
+    return NewtonPhysicsBackend(SimpleNamespace(has_native_renderer=True))
+
+
+@pytest.mark.parametrize(
+    "ranges",
+    [((0, 5, 0.0),), ((1, 3, 0.0),), ((0, 2, 0.0), (3, 5, 1.0))],
+)
+def test_clip_only_hides_selected_particles_below_world_height(ranges):
+    source = torch.tensor(
+        [[1, 2, -1], [2, 3, 0], [3, 4, -2], [4, 5, 0.5], [5, 6, 1]],
+        dtype=torch.float32,
+    )
+    before = source.clone()
+    expected = source.clone()
+    for start, end, height in ranges:
+        for index in range(start, end):
+            if source[index, 2] < height:
+                expected[index] = float("nan")
+
+    rendered = _backend()._clip_fluid_render_positions(source, ranges)
+
+    torch.testing.assert_close(rendered, expected, equal_nan=True, rtol=0, atol=0)
+    torch.testing.assert_close(source, before, rtol=0, atol=0)
+    assert rendered.data_ptr() != source.data_ptr()
+
+
+def test_repeated_publications_do_not_overwrite_previous_render_input():
+    backend = _backend()
+    source = torch.tensor([[1.0, 2, -1], [3, 4, 1], [5, 6, -2]])
+    ranges = ((0, 1, 0.0), (2, 3, -3.0))
+    first = backend._clip_fluid_render_positions(source, ranges)
+    saved = first.clone()
+    source[:, 2] += 5
+
+    second = backend._clip_fluid_render_positions(source, ranges)
+
+    torch.testing.assert_close(first, saved, equal_nan=True, rtol=0, atol=0)
+    torch.testing.assert_close(second, source, rtol=0, atol=0)
+    assert first.data_ptr() != second.data_ptr()
+
+
+def test_clip_layout_cache_tracks_height_ranges_shape_and_dtype():
+    backend = _backend()
+    source = torch.tensor([[1.0, 2, -1], [3, 4, -2], [5, 6, 1]])
+    first_ranges = ((0, 1, 0.0), (2, 3, 2.0))
+    backend._clip_fluid_render_positions(source, first_ranges)
+    cached = backend._fluid_render_clip_z
+    backend._clip_fluid_render_positions(source + 1, first_ranges)
+    assert backend._fluid_render_clip_z is cached
+
+    changed = backend._clip_fluid_render_positions(source, ((1, 2, -3.0),))
+    torch.testing.assert_close(changed, source, rtol=0, atol=0)
+    assert backend._fluid_render_clip_z is not cached
+
+    for resized in (source[:2], source.to(torch.float64)):
+        actual = backend._clip_fluid_render_positions(resized, ((0, 1, -3.0),))
+        torch.testing.assert_close(actual, resized, rtol=0, atol=0)
+        assert actual.dtype == resized.dtype
+
+
+def test_single_fluid_needs_no_per_particle_height_cache():
+    backend = _backend()
+    source = torch.ones(3, 3)
+    backend._clip_fluid_render_positions(source, ((1, 2, 0.0),))
+
+    result = backend._clip_fluid_render_positions(source, ((0, 3, 0.0),))
+
+    torch.testing.assert_close(result, source)
+    assert backend._fluid_render_clip_z is None
+    assert backend._fluid_render_clip_key is None
+
+
+def test_native_publication_preserves_body_state_time_and_unclipped_ranges(monkeypatch):
+    from dexsim.engine.newton_physics import backend_registry
+
+    wp.init()
+    source = torch.tensor([[1.0, 2, -1], [3, 4, -2], [5, 6, -3], [7, 8, 1]])
+    before = source.clone()
+    published = []
+
+    def publish(world, body_q, particle_q, *, simulation_time):
+        published.append((world, body_q, wp.to_torch(particle_q), simulation_time))
+
+    native = SimpleNamespace(
+        state_0=SimpleNamespace(
+            particle_q=wp.from_torch(source, dtype=wp.vec3), body_q=object()
+        ),
+        sim_time=4.5,
+        sync_to_dexsim=Mock(),
+        render_sync=SimpleNamespace(sync=publish),
+    )
+    monkeypatch.setattr(backend_registry, "get_newton_backend", lambda world: native)
+    backend = _backend()
+    backend._manager._fluid_objects = {
+        "clipped": SimpleNamespace(
+            cfg=SimpleNamespace(render=SimpleNamespace(clip_below_z=0.0)),
+            _entities=[
+                SimpleNamespace(
+                    _physics_binding=SimpleNamespace(particle_start=start),
+                    particle_count=1,
+                )
+                for start in (0, 3)
+            ],
+        ),
+        "unclipped": SimpleNamespace(
+            cfg=SimpleNamespace(render=SimpleNamespace(clip_below_z=None))
+        ),
+    }
+    scene = SimpleNamespace(world=object())
+
+    backend.sync_render_state(scene)
+
+    world, body_q, particles, sim_time = published[0]
+    assert world is scene.world
+    assert body_q is native.state_0.body_q
+    assert sim_time == native.sim_time
+    assert torch.isnan(particles[0]).all()
+    torch.testing.assert_close(particles[1:], source[1:], rtol=0, atol=0)
+    torch.testing.assert_close(source, before, rtol=0, atol=0)
+    native.sync_to_dexsim.assert_not_called()
+
+    backend._manager._fluid_objects["clipped"].cfg.render.clip_below_z = None
+    backend.sync_render_state(scene)
+    native.sync_to_dexsim.assert_called_once_with(scene.world)
+    assert backend._fluid_render_clip_z is None
+
+
+def test_teardown_releases_clip_metadata_even_without_a_world():
+    backend = _backend()
+    backend._clip_fluid_render_positions(torch.ones(3, 3), ((1, 2, 0.0),))
+
+    backend.prepare_for_teardown()
+
+    assert backend._fluid_render_clip_z is None
+    assert backend._fluid_render_clip_key is None
+
+
+@pytest.mark.gpu
+def test_clip_cache_is_safe_across_nondefault_cuda_streams():
+    backend = _backend()
+    source = torch.tensor([[1.0, 2, -1], [3, 4, 1], [5, 6, -2]], device="cuda")
+    ranges = ((0, 1, 0.0), (2, 3, -3.0))
+    streams = [torch.cuda.Stream(), torch.cuda.Stream()]
+    initial = torch.cuda.current_stream()
+    results = []
+    for index in (0, 1, 0):
+        stream = streams[index]
+        stream.wait_stream(initial)
+        with torch.cuda.stream(stream):
+            results.append(backend._clip_fluid_render_positions(source, ranges))
+    torch.cuda.synchronize()
+
+    expected = source.clone()
+    expected[0] = float("nan")
+    for result in results:
+        torch.testing.assert_close(result, expected, equal_nan=True, rtol=0, atol=0)

From cddb815ec09e3f9863eb7d9e58e3d6b56bb275b5 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Tue, 27 Oct 2026 22:27:29 +0800
Subject: [PATCH 17/19] feat(tutorial): render fluid mesh surface by default

---
 scripts/tutorials/sim/create_fluid.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/tutorials/sim/create_fluid.py b/scripts/tutorials/sim/create_fluid.py
index b68389236..3e609f294 100644
--- a/scripts/tutorials/sim/create_fluid.py
+++ b/scripts/tutorials/sim/create_fluid.py
@@ -16,7 +16,7 @@
 
 """Drop a procedural water block into a box using DexSim SPH.
 
-Run ``python scripts/tutorials/sim/create_fluid.py`` for native particles, or
+Run ``python scripts/tutorials/sim/create_fluid.py`` for a reconstructed mesh surface, or
 add ``--headless --renderer no-render`` for physics only. The scene uses five
 primitive walls and needs no task assets, robot, Gym or Task Program.
 """
@@ -124,7 +124,7 @@ def main(args: argparse.Namespace | None = None) -> None:
                 init_pos=(0, 0, 0.16),
                 spawn=FluidSpawnCfg(kind="box", size=(0.12, 0.12, 0.12)),
                 render=FluidRenderCfg(
-                    mode="none" if args.renderer == "no-render" else "particles"
+                    mode="none" if args.renderer == "no-render" else "mesh_surface"
                 ),
             )
         )

From 0835fc900419df806c2ac2882e8a3315d5a17873 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Tue, 27 Oct 2026 23:12:27 +0800
Subject: [PATCH 18/19] refactor(fluid): route DFSPH through DexUni

---
 .../topics/simulation-system/fluids.md        |  23 ++-
 .../physics-configuration.md                  |   2 +
 .../topics/simulation-system/rendering.md     |   4 +
 embodichain/lab/sim/cfg/fluid.py              |   6 +-
 embodichain/lab/sim/cfg/physics.py            |  23 +--
 embodichain/lab/sim/physics/newton.py         | 102 +++++++---
 embodichain/lab/sim/sim_manager.py            |  29 ++-
 scripts/benchmark/scene/fluid_render_sync.py  |  49 ++++-
 scripts/tutorials/sim/create_fluid.py         |  14 +-
 tests/sim/objects/test_fluid_runtime.py       |  66 ++++--
 tests/sim/objects/test_fluid_visibility.py    |  78 +++++--
 tests/sim/test_fluid_cfg.py                   | 192 ++++++++++++++++--
 12 files changed, 461 insertions(+), 127 deletions(-)

diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
index 1fafc8fc7..f4ae32330 100644
--- a/agent_context/topics/simulation-system/fluids.md
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -1,8 +1,9 @@
 # Initial-fill fluids
 
-`cfg/fluid.py` owns source, object and render configuration. Numerical settings
-remain DexSim `SPHSolverCfg`, accepted directly or through
-`NewtonPhysicsCfg.solver_cfg` with `solver_type: sph`.
+`cfg/fluid.py` owns source, object and render configuration. Fluids use DexUni,
+which dispatches DFSPH when the scene contains fluid particles. Optional
+numerical settings belong to DexSim `SPHSolverCfg`, nested under
+`NewtonPhysicsCfg.solver_cfg.sph_cfg`; omitting them uses native DFSPH defaults.
 `SimulationManager.add_fluid_object()` declares through SpawnScene and binds
 `objects/fluid.py` in place at `prepare()`.
 
@@ -13,17 +14,20 @@ DexSim derives the effective solver values from the fluid descriptors.
 
 ## Runtime requirements
 
-Fluid simulation requires Newton CUDA, no gradients and fixed particle topology.
+Fluid simulation requires Newton CUDA, a DexSim build exposing
+`DexUniSolverCfg.sph_cfg`, no gradients and fixed particle topology.
 Multiple environments require a DexSim SPH solver that explicitly advertises
 both world isolation and selected-world reset; arena spacing alone does not
 establish isolation. The development build
 `0.5.1+0b8422cc6f6717058bd15371e61bd35cdbbe3c0c` supports both. The published
-`0.5.1rc1` SPH solver lacks them and is limited to one fluid environment; its
-compatibility with the current DexSim adaptation has not been requalified.
+`0.5.1rc1` artifact does not provide the DexUni/DFSPH composition and cannot
+run this fluid path.
 
+The current native DexUni/DFSPH implementation is a prototype: DFSPH follows
+the rigid step with one-way rigid-to-fluid interaction. It requires the pure
+MuJoCo rigid path and cannot mix fluids with cloth or soft-body particles.
 Online robot pouring, Gym declarations and replay belong to the dependent task
-layer and require their respective DexSim capabilities. Do not infer those
-features from support for independent initial-fill SPH worlds.
+layer; two-way fluid feedback is not implied by DexUni's rigid/soft coupling.
 
 ## State and sampling
 
@@ -68,7 +72,8 @@ follow the renderer requirements of the [material system](materials.md).
 `scripts/tutorials/sim/create_fluid.py` demonstrates procedural box sampling,
 five primitive container walls, prepare, step, state reads and reset. Pass
 `--num-envs` to use multiple independent worlds on a capable SPH build. It
-needs no robot, task configuration or downloaded assets:
+disables MuJoCo rigid-rigid contacts for its fixed container while retaining
+DFSPH boundary collision. It needs no robot, task configuration or downloaded assets:
 
 ```bash
 python scripts/tutorials/sim/create_fluid.py --headless --renderer no-render
diff --git a/agent_context/topics/simulation-system/physics-configuration.md b/agent_context/topics/simulation-system/physics-configuration.md
index 62646316c..3eab43177 100644
--- a/agent_context/topics/simulation-system/physics-configuration.md
+++ b/agent_context/topics/simulation-system/physics-configuration.md
@@ -45,6 +45,8 @@ supports full-surface rigid-soft feedback in current DexSim builds. Native
 validation owns compatible contact modes and option combinations. Prescribed kinematic trajectories
 cannot respond to contact reactions. Forward solver expert options through
 `NewtonPhysicsCfg.solver_cfg`; DexSim owns their validation and dispatch.
+The current [DFSPH composition](fluids.md) remains one-way even when DexUni's
+rigid/soft coupling options are set to two-way.
 Articulation gravity compensation and passive damping are separate creation
 settings; see [articulation adapters](articulation-adapters.md).
 `async_sync`, `rigid_transform_sync_mode`, `capture_acquire_timeout` and
diff --git a/agent_context/topics/simulation-system/rendering.md b/agent_context/topics/simulation-system/rendering.md
index 61d324785..00432ede8 100644
--- a/agent_context/topics/simulation-system/rendering.md
+++ b/agent_context/topics/simulation-system/rendering.md
@@ -96,6 +96,10 @@ The Newton backend step hook suppresses automatic DexSim publication during
 manager-owned updates to avoid publishing twice. The manager honors explicit
 `sync_to_renderer=True` even without consumers; `None`/`False` publish only for
 consumers. Raw DexSim `World.update()` retains its configured automatic policy.
+When fluids are present, publication orders Newton and Torch work before the
+native CUDA staging copy and waits for that copy before a camera reads the frame.
+After an explicit fluid-state write, it first drains prior staged updates so a
+busy native double buffer cannot silently discard the newer state.
 Render-thread recording only consumes already-published state and must never
 call the blocking physics-to-render bridge. Marker publication while paused uses
 `capture_visualization(force=True)` without advancing physics.
diff --git a/embodichain/lab/sim/cfg/fluid.py b/embodichain/lab/sim/cfg/fluid.py
index 5d3d873cd..aa3a94b2f 100644
--- a/embodichain/lab/sim/cfg/fluid.py
+++ b/embodichain/lab/sim/cfg/fluid.py
@@ -145,14 +145,14 @@ def __post_init__(self) -> None:
 
 @configclass
 class FluidObjectCfg(ObjectBaseCfg):
-    """A fixed-count Newton SPH fluid, declared before scene preparation.
+    """A fixed-count DexUni/DFSPH fluid, declared before scene preparation.
 
     Density and spacing determine mass and must agree across fluid objects.
     Solver viscosity, surface tension and iteration controls belong exclusively
-    to DexSim's SPHSolverCfg supplied to NewtonPhysicsCfg.solver_cfg.
+    to DexSim's SPHSolverCfg supplied through DexUniSolverCfg.sph_cfg.
 
     This config owns density and spacing. Native SPH defaults for those fields
-    are placeholders in both mapping and native solver configs; non-default
+    are placeholders in both mapping and native ``sph_cfg`` values; non-default
     solver values must match the fluid declaration.
     """
 
diff --git a/embodichain/lab/sim/cfg/physics.py b/embodichain/lab/sim/cfg/physics.py
index c417cdd61..c3e3f5b75 100644
--- a/embodichain/lab/sim/cfg/physics.py
+++ b/embodichain/lab/sim/cfg/physics.py
@@ -450,21 +450,6 @@ def to_dexsim_cfg(
             "featherstone": FeatherstoneSolverCfg,
             "vbd": VBDSolverCfg,
         }
-        # Import SPH lazily so older DexSim builds can still run other solvers.
-        if isinstance(self.solver_cfg, Mapping):
-            requested = (
-                self.solver_cfg.get("solver_type")
-                or self.solver_cfg.get("class_type")
-                or "auto"
-            )
-            if _normalize_newton_solver_type(str(requested)) == "sph":
-                try:
-                    from dexsim.engine.newton_physics import SPHSolverCfg
-                except ImportError as exc:
-                    raise ImportError(
-                        "SPH requires a DexSim build exporting SPHSolverCfg."
-                    ) from exc
-                solver_cfg_map["sph"] = SPHSolverCfg
         solver_cfg = _newton_solver_cfg_to_dexsim(
             solver_cfg=self.solver_cfg,
             solver_cfg_map=solver_cfg_map,
@@ -526,9 +511,11 @@ def to_dexsim_cfg(
 def _normalize_newton_solver_type(solver_type: str) -> str:
     """Normalize public EmbodiChain and DexSim Newton solver aliases."""
     key = solver_type.replace("-", "_").lower()
+    if key in {"sph", "sphsolvercfg"}:
+        logger.log_error(
+            "Standalone SPH is not supported by this mapping; configure DexUni with sph_cfg instead."
+        )
     aliases = {
-        "sph": "sph",
-        "sphsolvercfg": "sph",
         "auto": "dexuni",
         "autosolver": "dexuni",
         "autosolvercfg": "dexuni",
@@ -566,7 +553,7 @@ def _normalize_newton_solver_type(solver_type: str) -> str:
         logger.log_error(
             f"Unsupported Newton solver type '{solver_type}'. "
             "Expected one of 'auto', 'dexuni', 'mjwarp', 'xpbd', 'semi_implicit', "
-            "'featherstone', 'vbd', or 'sph'."
+            "'featherstone', or 'vbd'."
         )
     return aliases[key]
 
diff --git a/embodichain/lab/sim/physics/newton.py b/embodichain/lab/sim/physics/newton.py
index a0c697122..20f4b933f 100644
--- a/embodichain/lab/sim/physics/newton.py
+++ b/embodichain/lab/sim/physics/newton.py
@@ -63,6 +63,7 @@ def __init__(self, manager) -> None:
         self._configured_solver_type: str | None = None
         self._fluid_render_clip_key: tuple[object, ...] | None = None
         self._fluid_render_clip_z: torch.Tensor | None = None
+        self._fluid_render_dirty = False
 
     @property
     def solver_type(self) -> str | None:
@@ -118,31 +119,67 @@ def sync_render_state(self, result: "dexsim.scene.Scene") -> None:
             )
         fluids = getattr(self._manager, "_fluid_objects", {})
         clipped = [f for f in fluids.values() if f.cfg.render.clip_below_z is not None]
+        native_stream = producer_stream = None
+        if fluids and str(backend.state_0.particle_q.device).startswith("cuda"):
+            import torch
+
+            # DexSim stages fluid positions asynchronously on CUDA's default
+            # stream. Order the Newton step and clipping before that copy, and
+            # complete the copy before a camera consumes the staged geometry.
+            device = torch.device(str(backend.state_0.particle_q.device))
+            producer_stream = torch.cuda.current_stream(device)
+            warp_stream = wp.stream_to_torch(backend.state_0.particle_q.device)
+            if producer_stream.cuda_stream != warp_stream.cuda_stream:
+                producer_stream.wait_stream(warp_stream)
+            native_stream = torch.cuda.default_stream(device)
+            if self._fluid_render_dirty:
+                # A previous staged copy may still occupy the native double
+                # buffer. Drain it before submitting a newer explicit write;
+                # otherwise the renderer can silently discard the new state.
+                flush = getattr(result.world, "_flush_render_updates", None)
+                if not callable(flush):
+                    raise RuntimeError(
+                        "This DexSim build cannot synchronize fluid render updates."
+                    )
+                status = flush()
+                if status is not None and int(status) < 0:
+                    raise RuntimeError("Could not drain previous fluid render updates.")
         if not clipped:
             self._fluid_render_clip_key = None
             self._fluid_render_clip_z = None
-            backend.sync_to_dexsim(result.world)
-            return
-        ranges = []
-        for fluid in clipped:
-            for entity in fluid._entities:
-                start = entity._physics_binding.particle_start
-                ranges.append(
-                    (
-                        start,
-                        start + entity.particle_count,
-                        fluid.cfg.render.clip_below_z,
+            positions = None
+        else:
+            ranges = []
+            for fluid in clipped:
+                for entity in fluid._entities:
+                    start = entity._physics_binding.particle_start
+                    ranges.append(
+                        (
+                            start,
+                            start + entity.particle_count,
+                            fluid.cfg.render.clip_below_z,
+                        )
                     )
+            positions = self._clip_fluid_render_positions(
+                wp.to_torch(backend.state_0.particle_q), tuple(ranges)
+            )
+        if native_stream is not None:
+            if native_stream.cuda_stream != producer_stream.cuda_stream:
+                native_stream.wait_stream(producer_stream)
+        try:
+            if positions is None:
+                backend.sync_to_dexsim(result.world)
+            else:
+                backend.render_sync.sync(
+                    result.world,
+                    backend.state_0.body_q,
+                    wp.from_torch(positions, dtype=wp.vec3),
+                    simulation_time=backend.sim_time,
                 )
-        positions = self._clip_fluid_render_positions(
-            wp.to_torch(backend.state_0.particle_q), tuple(ranges)
-        )
-        backend.render_sync.sync(
-            result.world,
-            backend.state_0.body_q,
-            wp.from_torch(positions, dtype=wp.vec3),
-            simulation_time=backend.sim_time,
-        )
+        finally:
+            if native_stream is not None:
+                native_stream.synchronize()
+        self._fluid_render_dirty = False
 
     def _clip_fluid_render_positions(
         self,
@@ -185,10 +222,19 @@ def _clip_fluid_render_positions(
             positions[:, 2:3] < self._fluid_render_clip_z, float("nan"), positions
         )
 
+    @property
+    def supports_dexuni_fluids(self) -> bool:
+        """Whether DexSim exposes the composed DexUni/DFSPH configuration."""
+        if self.solver_type != "dexuni":
+            return False
+        from dexsim.engine.newton_physics import DexUniSolverCfg
+
+        return "sph_cfg" in getattr(DexUniSolverCfg, "__dataclass_fields__", {})
+
     @property
     def supports_fluid_worlds(self) -> bool:
         """Whether the loaded SPH artifact supports isolated worlds and reset."""
-        if self.solver_type != "sph":
+        if not self.supports_dexuni_fluids:
             return False
         from dexsim.engine.newton_physics.solver_runtime import ensure_solver_available
 
@@ -209,10 +255,13 @@ def invalidate_fluid_state(self, env_ids: Sequence[int] | None = None) -> None:
         from dexsim.engine.newton_physics.backend_registry import get_newton_backend
 
         backend = get_newton_backend(self._manager._world)
-        if backend is None or backend.solver_type != "sph":
-            raise RuntimeError("Prepared SPH backend required.")
+        if backend is None or backend.solver_type != "dexuni":
+            raise RuntimeError("Prepared DexUni fluid backend required.")
+        solver = getattr(backend, "sph_solver", None)
+        if solver is None:
+            raise RuntimeError("Prepared DexUni SPH solver required.")
         backend.invalidate_cuda_graph("fluid state write")
-        solver = getattr(backend, "sph_solver", backend.solver)
+        self._fluid_render_dirty = True
         if getattr(solver, "supports_world_reset", False):
             mask = [
                 env_ids is None or i in env_ids
@@ -234,7 +283,9 @@ def fluid_diagnostics(self, fluid: FluidObject) -> dict[str, object]:
         import torch
 
         backend = get_newton_backend(self._manager._world)
-        solver = getattr(backend, "sph_solver", backend.solver)
+        solver = getattr(backend, "sph_solver", None)
+        if solver is None:
+            raise RuntimeError("Prepared DexUni SPH solver required.")
         density = wp.to_torch(solver.particle_density)
         values = []
         for entity in fluid._entities:
@@ -268,6 +319,7 @@ def prepare_for_teardown(self) -> None:
 
         self._fluid_render_clip_key = None
         self._fluid_render_clip_z = None
+        self._fluid_render_dirty = False
 
         world = getattr(self._manager, "_world", None)
         if world is None:
diff --git a/embodichain/lab/sim/sim_manager.py b/embodichain/lab/sim/sim_manager.py
index f02825c31..fc6d5e30a 100644
--- a/embodichain/lab/sim/sim_manager.py
+++ b/embodichain/lab/sim/sim_manager.py
@@ -2937,17 +2937,25 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
 
         if self.physics.name != "newton" or self.device.type != "cuda":
             raise NotImplementedError("Fluid objects require Newton on CUDA.")
+        if (
+            getattr(self, "_active_newton_solver_type", None) != "dexuni"
+            or self.sim_config.physics_cfg.requires_grad
+        ):
+            raise NotImplementedError(
+                "Fluid objects require non-differentiable DexUni."
+            )
+        if not getattr(self.physics, "supports_dexuni_fluids", False):
+            raise NotImplementedError(
+                "Fluid objects require a DexSim build supporting DexUniSolverCfg.sph_cfg."
+            )
+        if getattr(self, "_deformable_objects", {}):
+            raise NotImplementedError("DexUni fluids cannot coexist with deformables.")
         if self.num_envs != 1 and not getattr(
             self.physics, "supports_fluid_worlds", False
         ):
             raise NotImplementedError(
                 "Batch fluids require a DexSim SPH build with world isolation and selected-world reset."
             )
-        if (
-            self._active_newton_solver_type != "sph"
-            or self.sim_config.physics_cfg.requires_grad
-        ):
-            raise NotImplementedError("Fluid objects require non-differentiable SPH.")
         if self.spawn_result is not None:
             raise NotImplementedError("Declare fluids before prepare().")
         if not isinstance(cfg, FluidObjectCfg):
@@ -2981,6 +2989,11 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
             ):
                 raise ValueError("All fluids must share density and particle_spacing.")
         configured = self.sim_config.physics_cfg.solver_cfg
+        configured = (
+            configured.get("sph_cfg")
+            if isinstance(configured, Mapping)
+            else getattr(configured, "sph_cfg", None)
+        )
         from dexsim.engine.newton_physics import SPHSolverCfg
 
         defaults = SPHSolverCfg()
@@ -2998,7 +3011,9 @@ def add_fluid_object(self, cfg: FluidObjectCfg) -> FluidObject:
                 and not np.isclose(existing, getattr(defaults, key), rtol=1e-7, atol=0)
                 and not np.isclose(existing, value, rtol=1e-7, atol=0)
             ):
-                raise ValueError(f"Fluid {key} conflicts with solver_cfg.{key}.")
+                raise ValueError(
+                    f"Fluid {key} conflicts with solver_cfg.sph_cfg.{key}."
+                )
         points = cfg.spawn.sample(cfg.particle_spacing)
         pose = _pose_from_cfg(cfg)
         if cfg.spawn.reference_uid is not None:
@@ -3091,6 +3106,8 @@ def add_deformable_object(self, cfg: DeformableObjectCfg) -> DeformableObject:
                 "Newton deformable particle sets currently require a CUDA device."
             )
         solver_type = self._active_newton_solver_type
+        if solver_type == "dexuni" and getattr(self, "_fluid_objects", {}):
+            raise NotImplementedError("DexUni fluids cannot coexist with deformables.")
         supported_solvers = {"auto", "dexuni", "xpbd", "semi_implicit", "vbd"}
         if solver_type not in supported_solvers:
             raise NotImplementedError(
diff --git a/scripts/benchmark/scene/fluid_render_sync.py b/scripts/benchmark/scene/fluid_render_sync.py
index 1e7ddc4e1..0e8a0b569 100644
--- a/scripts/benchmark/scene/fluid_render_sync.py
+++ b/scripts/benchmark/scene/fluid_render_sync.py
@@ -221,7 +221,13 @@ def _benchmark_native(
             physics_cfg=NewtonPhysicsCfg(
                 num_substeps=1,
                 collision_cfg=None,
-                solver_cfg={"solver_type": "sph", "volume_map_boundary_enabled": False},
+                solver_cfg={
+                    "solver_type": "dexuni",
+                    "sph_cfg": {
+                        "sph_method": "dfsph",
+                        "volume_map_boundary_enabled": False,
+                    },
+                },
             ),
         )
     )
@@ -265,6 +271,10 @@ def legacy() -> None:
                 for item in sim._fluid_objects.values()
                 if item.cfg.render.clip_below_z is not None
             ]
+            producer = torch.cuda.current_stream(device)
+            warp_stream = wp.stream_to_torch(current.state_0.particle_q.device)
+            if producer.cuda_stream != warp_stream.cuda_stream:
+                producer.wait_stream(warp_stream)
             clipped = wp.to_torch(current.state_0.particle_q).clone()
             for item in clipped_fluids:
                 for entity in item._entities:
@@ -273,12 +283,18 @@ def legacy() -> None:
                     selected[selected[:, 2] < item.cfg.render.clip_below_z] = float(
                         "nan"
                     )
-            current.render_sync.sync(
-                result.world,
-                current.state_0.body_q,
-                wp.from_torch(clipped, dtype=wp.vec3),
-                simulation_time=current.sim_time,
-            )
+            staging = torch.cuda.default_stream(device)
+            if staging.cuda_stream != producer.cuda_stream:
+                staging.wait_stream(producer)
+            try:
+                current.render_sync.sync(
+                    result.world,
+                    current.state_0.body_q,
+                    wp.from_torch(clipped, dtype=wp.vec3),
+                    simulation_time=current.sim_time,
+                )
+            finally:
+                staging.synchronize()
             if sim._render_state_published is not None:
                 sim._render_state_published = True
 
@@ -415,10 +431,21 @@ def run_all_benchmarks(args: argparse.Namespace) -> None:
                     perf_rows.extend(perf)
                     metric_rows.extend(metrics)
             if args.native and device.type == "cuda":
-                for count in args.native_sizes:
-                    perf, metrics = _benchmark_native(count, device, args)
-                    perf_rows.extend(perf)
-                    metric_rows.extend(metrics)
+                try:
+                    from dexsim.engine.newton_physics import DexUniSolverCfg
+                except ImportError:
+                    DexUniSolverCfg = None
+                if "sph_cfg" not in getattr(
+                    DexUniSolverCfg, "__dataclass_fields__", {}
+                ):
+                    notes.append(
+                        "- Native publication skipped: DexUni/DFSPH is unavailable."
+                    )
+                else:
+                    for count in args.native_sizes:
+                        perf, metrics = _benchmark_native(count, device, args)
+                        perf_rows.extend(perf)
+                        metric_rows.extend(metrics)
             elif args.native:
                 notes.append("- Native publication skipped: CUDA is required.")
     _write_report(args.output, perf_rows, metric_rows, notes)
diff --git a/scripts/tutorials/sim/create_fluid.py b/scripts/tutorials/sim/create_fluid.py
index 3e609f294..03e875486 100644
--- a/scripts/tutorials/sim/create_fluid.py
+++ b/scripts/tutorials/sim/create_fluid.py
@@ -14,7 +14,7 @@
 # limitations under the License.
 # ----------------------------------------------------------------------------
 
-"""Drop a procedural water block into a box using DexSim SPH.
+"""Drop a procedural water block into a box using DexUni and DFSPH.
 
 Run ``python scripts/tutorials/sim/create_fluid.py`` for a reconstructed mesh surface, or
 add ``--headless --renderer no-render`` for physics only. The scene uses five
@@ -85,10 +85,14 @@ def main(args: argparse.Namespace | None = None) -> None:
                 num_substeps=4,
                 collision_cfg=None,
                 solver_cfg={
-                    "solver_type": "sph",
-                    "sph_method": "dfsph",
-                    "particle_diameter": spacing,
-                    "particle_support_radius": 2.25 * spacing,
+                    "solver_type": "dexuni",
+                    # The container is fixed; DFSPH still collides with its SDF.
+                    "mujoco_options": {"disable_contacts": True},
+                    "sph_cfg": {
+                        "sph_method": "dfsph",
+                        "particle_diameter": spacing,
+                        "particle_support_radius": 2.25 * spacing,
+                    },
                 },
             ),
             visualization=visualization_cfg_from_args(args),
diff --git a/tests/sim/objects/test_fluid_runtime.py b/tests/sim/objects/test_fluid_runtime.py
index f675013be..20e982252 100644
--- a/tests/sim/objects/test_fluid_runtime.py
+++ b/tests/sim/objects/test_fluid_runtime.py
@@ -38,10 +38,18 @@
 
 
 class TestFluidRuntime:
-    @pytest.mark.parametrize("num_envs", [1, 2])
+    @pytest.mark.parametrize(
+        "num_envs,automatic",
+        [(1, False), (2, False), (1, True)],
+        ids=["single-explicit", "two-explicit", "single-default"],
+    )
     def test_prepare_write_reset_and_repeat(
-        self, tmp_path: Path, num_envs: int
+        self, tmp_path: Path, num_envs: int, automatic: bool
     ) -> None:
+        from dexsim.engine.newton_physics import DexUniSolverCfg
+
+        if "sph_cfg" not in getattr(DexUniSolverCfg, "__dataclass_fields__", {}):
+            pytest.skip("This DexSim build does not compose SPH through DexUni.")
         if num_envs > 1:
             from dexsim.engine.newton_physics.solver_runtime import (
                 ensure_solver_available,
@@ -54,14 +62,15 @@ def test_prepare_write_reset_and_repeat(
             ):
                 pytest.skip("This DexSim SPH build supports only a single world.")
 
-        def run_worlds(count: int) -> dict:
-            result_path = tmp_path / f"lifecycle-{count}.json"
+        def run_worlds(count: int, use_default: bool) -> dict:
+            result_path = tmp_path / f"lifecycle-{count}-{use_default}.json"
             result = subprocess.run(
                 [
                     sys.executable,
                     str(Path(__file__).resolve()),
                     str(result_path),
                     str(count),
+                    str(int(use_default)),
                 ],
                 capture_output=True,
                 text=True,
@@ -71,11 +80,12 @@ def run_worlds(count: int) -> dict:
             state = json.loads(result_path.read_text())
             assert state["num_particles"] == 64
             assert state["num_envs"] == count
+            assert state["automatic"] is use_default
             return state
 
-        result = run_worlds(num_envs)
+        result = run_worlds(num_envs, automatic)
         if num_envs > 1:
-            reference = run_worlds(1)
+            reference = run_worlds(1, automatic)
             torch.testing.assert_close(
                 torch.tensor(result["first_velocity"]),
                 torch.tensor(reference["first_velocity"]),
@@ -84,9 +94,10 @@ def run_worlds(count: int) -> dict:
             )
 
 
-def _run_fluid_lifecycle(num_envs: int) -> dict[str, object]:
+def _run_fluid_lifecycle(num_envs: int, automatic: bool) -> dict[str, object]:
     """Run the complete NoRender lifecycle in a fresh native process."""
-    from dexsim.engine.newton_physics import SPHSolverCfg
+    from dexsim.engine.newton_physics import DexUniSolverCfg, SPHSolverCfg
+    from dexsim.engine.newton_physics.backend_registry import get_newton_backend
     from embodichain.lab.sim.cfg import RigidObjectCfg
     from embodichain.lab.sim.shapes import CubeCfg
 
@@ -103,21 +114,35 @@ def _run_fluid_lifecycle(num_envs: int) -> dict[str, object]:
             physics_cfg=NewtonPhysicsCfg(
                 num_substeps=2,
                 collision_cfg=None,
-                solver_cfg=SPHSolverCfg(
-                    sph_method="dfsph",
-                    particle_support_radius=0.0225,
-                    volume_map_boundary_enabled=False,
+                solver_cfg=(
+                    None
+                    if automatic
+                    else DexUniSolverCfg(
+                        sph_cfg=SPHSolverCfg(
+                            sph_method="dfsph",
+                            particle_support_radius=0.0225,
+                            volume_map_boundary_enabled=False,
+                        )
+                    )
                 ),
             ),
         )
     )
-    fluid = rigid = None
+    fluid = rigid = moving = None
     try:
         rigid = sim.add_rigid_object(
             RigidObjectCfg(
                 uid="boundary", body_type="kinematic", shape=CubeCfg(size=[0.1] * 3)
             )
         )
+        moving = sim.add_rigid_object(
+            RigidObjectCfg(
+                uid="moving",
+                body_type="dynamic",
+                init_pos=(0.2, 0.0, 1.0),
+                shape=CubeCfg(size=[0.04] * 3),
+            )
+        )
         fluid = sim.add_fluid_object(
             FluidObjectCfg(
                 uid="water",
@@ -130,6 +155,10 @@ def _run_fluid_lifecycle(num_envs: int) -> dict[str, object]:
         with pytest.raises(RuntimeError, match="prepare"):
             _ = fluid.data
         sim.prepare()
+        backend = get_newton_backend(sim.spawn_result.world)
+        assert backend.solver_type == "dexuni"
+        assert backend.sph_solver is not None
+        assert backend.cfg.solver_cfg.sph_cfg.sph_method == "dfsph"
         assert sim.get_fluid_object("water") is fluid
         assert sim.get_asset("water") is fluid
         assert fluid.num_particles == 64
@@ -141,11 +170,16 @@ def _run_fluid_lifecycle(num_envs: int) -> dict[str, object]:
         first = fluid.data.particle_state_w
         assert torch.isfinite(first).all()
         assert torch.all(first[..., 2].mean(dim=1) < original[..., 2].mean(dim=1))
+        diagnostics = sim.get_fluid_diagnostics("water")
+        assert diagnostics["particle_density"].shape == (num_envs, 64)
+        assert torch.isfinite(diagnostics["particle_density"]).all()
         if num_envs > 1:
             torch.testing.assert_close(
                 first[0, :, 3:], first[1, :, 3:], rtol=1e-4, atol=1e-5
             )
+            body_before = moving.body_state.clone()
             fluid.reset(env_ids=[0])
+            torch.testing.assert_close(moving.body_state, body_before, rtol=0, atol=0)
             selected = fluid.data.particle_state_w
             torch.testing.assert_close(selected[0], original[0], rtol=0, atol=0)
             torch.testing.assert_close(selected[1:], first[1:], rtol=0, atol=0)
@@ -164,15 +198,17 @@ def _run_fluid_lifecycle(num_envs: int) -> dict[str, object]:
         return {
             "num_particles": fluid.num_particles,
             "num_envs": num_envs,
+            "automatic": automatic,
             "first_velocity": first[0, :, 3:].cpu().tolist(),
         }
     finally:
-        fluid = rigid = None
+        fluid = rigid = moving = None
         sim.destroy(exit_process=False)
         SimulationManager.flush_cleanup_queue()
 
 
 if __name__ == "__main__":
     Path(sys.argv[1]).write_text(
-        json.dumps(_run_fluid_lifecycle(int(sys.argv[2]))) + "\n"
+        json.dumps(_run_fluid_lifecycle(int(sys.argv[2]), bool(int(sys.argv[3]))))
+        + "\n"
     )
diff --git a/tests/sim/objects/test_fluid_visibility.py b/tests/sim/objects/test_fluid_visibility.py
index e831cf420..acfc10ab9 100644
--- a/tests/sim/objects/test_fluid_visibility.py
+++ b/tests/sim/objects/test_fluid_visibility.py
@@ -28,9 +28,21 @@
 pytestmark = [pytest.mark.gpu, pytest.mark.subprocess_sim]
 
 
-def test_fluid_surface_changes_record_camera_pixels_in_every_environment(tmp_path):
+@pytest.mark.parametrize("clipping", [False, True])
+def test_fluid_surface_changes_record_camera_pixels_in_every_environment(
+    tmp_path, clipping
+):
+    from dexsim.engine.newton_physics import DexUniSolverCfg
+
+    if "sph_cfg" not in getattr(DexUniSolverCfg, "__dataclass_fields__", {}):
+        pytest.skip("This DexSim build does not compose SPH through DexUni.")
     result = subprocess.run(
-        [sys.executable, str(Path(__file__).resolve()), str(tmp_path)],
+        [
+            sys.executable,
+            str(Path(__file__).resolve()),
+            str(tmp_path),
+            str(int(clipping)),
+        ],
         capture_output=True,
         text=True,
         timeout=180,
@@ -40,10 +52,14 @@ def test_fluid_surface_changes_record_camera_pixels_in_every_environment(tmp_pat
     assert results
     for row in results:
         assert row["visible_blue_pixels"] > 100
-        assert row["visible_blue_pixels"] > 4 * row["hidden_blue_pixels"]
+        for hidden, restored in zip(
+            row["hidden_blue_pixels"], row["restored_blue_pixels"]
+        ):
+            assert row["visible_blue_pixels"] > 4 * hidden
+            assert restored > 100 and restored > 4 * hidden
 
 
-def _run_visibility_worker(output: Path) -> None:
+def _run_visibility_worker(output: Path, clipping: bool) -> None:
     import numpy as np
     import torch
     from PIL import Image
@@ -78,11 +94,14 @@ def _run_visibility_worker(output: Path) -> None:
                 num_substeps=1,
                 collision_cfg=None,
                 solver_cfg={
-                    "solver_type": "sph",
-                    "volume_map_boundary_enabled": False,
-                    "particle_support_radius": 0.00675,
-                    "pressure_relaxation": 0.2,
-                    "max_velocity": 2.0,
+                    "solver_type": "dexuni",
+                    "sph_cfg": {
+                        "sph_method": "dfsph",
+                        "volume_map_boundary_enabled": False,
+                        "particle_support_radius": 0.00675,
+                        "pressure_relaxation": 0.2,
+                        "max_velocity": 2.0,
+                    },
                 },
             ),
         )
@@ -98,7 +117,7 @@ def _run_visibility_worker(output: Path) -> None:
                 render=FluidRenderCfg(
                     mode="mesh_surface",
                     render_radius=0.0009,
-                    clip_below_z=0.05,
+                    clip_below_z=0.05 if clipping else None,
                     material=VisualMaterialCfg(
                         base_color=[0.05, 0.35, 0.9, 1],
                         roughness=0.1,
@@ -139,10 +158,30 @@ def capture():
         before = fluid.data.particle_state_w
         hidden_state = before.clone()
         hidden_state[..., 0] += 10
-        fluid.write_particle_state_to_sim(hidden_state)
-        hidden = capture()
-        # Visibility captures do not advance physics; state restoration is exact.
+        hidden_frames = []
+        restored_frames = []
+        # Several publications may occur before the next camera consumes one.
+        for state in (before, hidden_state):
+            fluid.write_particle_state_to_sim(state)
+            sim.sync_render_state()
+        hidden_frames.append(capture())
         fluid.write_particle_state_to_sim(before)
+        restored_frames.append(capture())
+        for _ in range(3):
+            fluid.write_particle_state_to_sim(hidden_state)
+            hidden_frames.append(capture())
+            # Visibility captures do not advance physics.
+            fluid.write_particle_state_to_sim(before)
+            restored_frames.append(capture())
+        stream = torch.cuda.Stream(device=fluid.device)
+        stream.wait_stream(torch.cuda.current_stream(fluid.device))
+        with torch.cuda.stream(stream):
+            fluid.write_particle_state_to_sim(hidden_state)
+            # Force the renderer's staging copy to wait for producer work.
+            torch.cuda._sleep(20_000_000)
+            hidden_frames.append(capture())
+            fluid.write_particle_state_to_sim(before)
+            restored_frames.append(capture())
         torch.testing.assert_close(fluid.data.particle_state_w, before, rtol=0, atol=0)
 
         def blue_pixels(frame):
@@ -157,12 +196,19 @@ def blue_pixels(frame):
         results = []
         for env_id in range(num_envs):
             Image.fromarray(visible[env_id]).save(output / f"visible_env{env_id}.png")
-            Image.fromarray(hidden[env_id]).save(output / f"hidden_env{env_id}.png")
+            Image.fromarray(hidden_frames[-1][env_id]).save(
+                output / f"hidden_env{env_id}.png"
+            )
             results.append(
                 {
                     "env_id": env_id,
                     "visible_blue_pixels": blue_pixels(visible[env_id]),
-                    "hidden_blue_pixels": blue_pixels(hidden[env_id]),
+                    "hidden_blue_pixels": [
+                        blue_pixels(frame[env_id]) for frame in hidden_frames
+                    ],
+                    "restored_blue_pixels": [
+                        blue_pixels(frame[env_id]) for frame in restored_frames
+                    ],
                 }
             )
         (output / "visibility.json").write_text(json.dumps(results, indent=2) + "\n")
@@ -173,4 +219,4 @@ def blue_pixels(frame):
 
 
 if __name__ == "__main__":
-    _run_visibility_worker(Path(sys.argv[1]))
+    _run_visibility_worker(Path(sys.argv[1]), bool(int(sys.argv[2])))
diff --git a/tests/sim/test_fluid_cfg.py b/tests/sim/test_fluid_cfg.py
index 9ffd4924d..b1ab4c12f 100644
--- a/tests/sim/test_fluid_cfg.py
+++ b/tests/sim/test_fluid_cfg.py
@@ -46,16 +46,30 @@ def test_fluid_round_trip_keeps_native_render_options():
 
 
 def test_native_sph_and_yaml_mapping_use_same_solver():
-    from dexsim.engine.newton_physics import SPHSolverCfg
+    from dexsim.engine.newton_physics import DexUniSolverCfg, SPHSolverCfg
+
+    if "sph_cfg" not in getattr(DexUniSolverCfg, "__dataclass_fields__", {}):
+        pytest.skip("This DexSim build does not compose SPH through DexUni.")
 
     source = SPHSolverCfg(sph_method="dfsph", viscosity=0.0006)
-    native = NewtonPhysicsCfg(solver_cfg=source).to_dexsim_cfg(0)
+    native = NewtonPhysicsCfg(solver_cfg=DexUniSolverCfg(sph_cfg=source)).to_dexsim_cfg(
+        0
+    )
     mapping = NewtonPhysicsCfg(
-        solver_cfg={"solver_type": "sph", "sph_method": "dfsph", "viscosity": 0.0006}
+        solver_cfg={
+            "solver_type": "dexuni",
+            "sph_cfg": {"sph_method": "dfsph", "viscosity": 0.0006},
+        }
     ).to_dexsim_cfg(0)
-    assert type(native.solver_cfg) is SPHSolverCfg
-    assert type(mapping.solver_cfg) is SPHSolverCfg
-    assert mapping.solver_cfg == source
+    assert type(native.solver_cfg) is DexUniSolverCfg
+    assert type(mapping.solver_cfg) is DexUniSolverCfg
+    assert native.solver_cfg.sph_cfg == source
+    assert mapping.solver_cfg.sph_cfg == source
+
+
+def test_standalone_sph_mapping_points_to_dexuni_migration():
+    with pytest.raises(RuntimeError, match="configure DexUni with sph_cfg"):
+        NewtonPhysicsCfg(solver_cfg={"solver_type": "sph"}).to_dexsim_cfg(0)
 
 
 @pytest.mark.parametrize("kind", ["box", "sphere", "cylinder"])
@@ -123,6 +137,134 @@ def test_manager_rejects_unsupported_fluid_worlds_before_declaration(backend, nu
         SimulationManager.add_fluid_object(manager, FluidObjectCfg(uid="water"))
 
 
+@pytest.mark.parametrize(
+    "solver_cfg",
+    [None, {"solver_type": "dexuni", "sph_cfg": {"sph_method": "dfsph"}}],
+)
+def test_fluid_declaration_accepts_dexuni_dispatch(solver_cfg, monkeypatch):
+    from unittest.mock import Mock
+    from embodichain.lab.sim import SimulationManager
+
+    manager = _fluid_manager_for_renderer("no-render")
+    manager._active_newton_solver_type = "dexuni"
+    manager.physics.supports_dexuni_fluids = True
+    manager.sim_config.physics_cfg.solver_cfg = solver_cfg
+    monkeypatch.setattr(SimulationManager, "_get_process_renderer", lambda: None)
+    monkeypatch.setattr("embodichain.lab.sim.sim_manager.FluidObject", Mock())
+    cfg = FluidObjectCfg(
+        uid="water", spawn=FluidSpawnCfg(positions=[[0, 0, 0]]), render={"mode": "none"}
+    )
+
+    SimulationManager.add_fluid_object(manager, cfg)
+
+    manager._spawn_scene.declare.assert_called_once()
+
+
+def test_dexuni_nested_sph_density_conflict_is_rejected(monkeypatch):
+    from embodichain.lab.sim import SimulationManager
+
+    manager = _fluid_manager_for_renderer("no-render")
+    manager._active_newton_solver_type = "dexuni"
+    manager.physics.supports_dexuni_fluids = True
+    manager.sim_config.physics_cfg.solver_cfg = {
+        "solver_type": "dexuni",
+        "sph_cfg": {"sph_method": "dfsph", "rest_density": 1200.0},
+    }
+    monkeypatch.setattr(SimulationManager, "_get_process_renderer", lambda: None)
+
+    with pytest.raises(ValueError, match="rest_density conflicts"):
+        SimulationManager.add_fluid_object(manager, FluidObjectCfg(uid="water"))
+    manager._spawn_scene.declare.assert_not_called()
+
+
+@pytest.mark.parametrize("solver,capability", [("sph", True), ("dexuni", False)])
+def test_fluid_requires_a_dexuni_build_with_sph_support(solver, capability):
+    from embodichain.lab.sim import SimulationManager
+
+    manager = _fluid_manager_for_renderer("no-render")
+    manager._active_newton_solver_type = solver
+    manager.physics.supports_dexuni_fluids = capability
+    with pytest.raises(NotImplementedError, match="DexUni"):
+        SimulationManager.add_fluid_object(manager, FluidObjectCfg(uid="water"))
+    manager._spawn_scene.declare.assert_not_called()
+
+
+def test_dexuni_fluid_rejects_existing_deformables():
+    from embodichain.lab.sim import SimulationManager
+
+    manager = _fluid_manager_for_renderer("no-render")
+    manager._deformable_objects = {"cloth": object()}
+    with pytest.raises(NotImplementedError, match="cannot coexist"):
+        SimulationManager.add_fluid_object(manager, FluidObjectCfg(uid="water"))
+    manager._spawn_scene.declare.assert_not_called()
+
+
+def test_dexuni_deformable_rejects_existing_fluid():
+    from types import SimpleNamespace
+    import torch
+    from embodichain.lab.sim import SimulationManager
+
+    manager = SimpleNamespace(
+        physics=SimpleNamespace(name="newton", supports_surface_deformables=True),
+        device=torch.device("cuda"),
+        _active_newton_solver_type="dexuni",
+        _fluid_objects={"water": object()},
+    )
+    with pytest.raises(NotImplementedError, match="cannot coexist"):
+        SimulationManager.add_deformable_object(
+            manager, SimpleNamespace(deformable_type="surface")
+        )
+
+
+@pytest.mark.parametrize("has_sph_cfg", [False, True])
+def test_dexuni_fluid_capability_tracks_native_config(monkeypatch, has_sph_cfg):
+    from types import SimpleNamespace
+    import dexsim.engine.newton_physics as native
+    from embodichain.lab.sim.physics.newton import NewtonPhysicsBackend
+
+    fields = {"sph_cfg": object()} if has_sph_cfg else {}
+    fake_cfg = type("FakeDexUniCfg", (), {"__dataclass_fields__": fields})
+    monkeypatch.setattr(native, "DexUniSolverCfg", fake_cfg)
+    backend = NewtonPhysicsBackend(SimpleNamespace())
+    backend._configured_solver_type = "dexuni"
+    assert backend.supports_dexuni_fluids is has_sph_cfg
+
+
+def test_fluid_reset_invalidates_sph_without_resetting_dexuni_bodies(monkeypatch):
+    from types import SimpleNamespace
+    from unittest.mock import Mock
+
+    import warp as wp
+    from dexsim.engine.newton_physics import backend_registry
+    from embodichain.lab.sim.physics.newton import NewtonPhysicsBackend
+
+    wp.init()
+    sph = SimpleNamespace(supports_world_reset=True, reset=Mock())
+    solid = SimpleNamespace(reset=Mock())
+    native = SimpleNamespace(
+        solver_type="dexuni",
+        sph_solver=sph,
+        solver=solid,
+        invalidate_cuda_graph=Mock(),
+        model=SimpleNamespace(world_count=2, device="cpu"),
+        _runtime=SimpleNamespace(current_state=object()),
+    )
+    monkeypatch.setattr(backend_registry, "get_newton_backend", lambda _: native)
+    backend = NewtonPhysicsBackend(SimpleNamespace(_world=object()))
+
+    backend.invalidate_fluid_state([1])
+
+    native.invalidate_cuda_graph.assert_called_once()
+    solid.reset.assert_not_called()
+    assert sph.reset.call_args.args[0] is native._runtime.current_state
+    assert sph.reset.call_args.kwargs["world_mask"].numpy().tolist() == [
+        False,
+        True,
+        False,
+    ]
+    assert sph.reset.call_args.kwargs["flags"] == 0
+
+
 def test_points_source_validates_spacing():
     with pytest.raises(ValueError, match="spacing"):
         FluidSpawnCfg(positions=[[0, 0, 0]]).sample(0)
@@ -174,10 +316,12 @@ def _fluid_manager_for_renderer(renderer):
     from embodichain.lab.sim.cfg import RenderCfg
 
     return SimpleNamespace(
-        physics=SimpleNamespace(name="newton", configure_world=Mock()),
+        physics=SimpleNamespace(
+            name="newton", configure_world=Mock(), supports_dexuni_fluids=True
+        ),
         device=torch.device("cuda"),
         num_envs=1,
-        _active_newton_solver_type="sph",
+        _active_newton_solver_type="dexuni",
         spawn_result=None,
         sim_config=SimulationManagerCfg(
             headless=True,
@@ -292,14 +436,18 @@ def test_solver_defaults_allow_fluid_owned_density_and_spacing(
 ):
     from dataclasses import asdict
     from unittest.mock import Mock
-    from dexsim.engine.newton_physics import SPHSolverCfg
+    from dexsim.engine.newton_physics import DexUniSolverCfg, SPHSolverCfg
     from embodichain.lab.sim import SimulationManager
 
-    solver = SPHSolverCfg(sph_method="dfsph")
-    if representation == "mapping":
-        solver = {"solver_type": "sph", "sph_method": "dfsph"}
-    elif representation == "full_mapping":
-        solver = asdict(solver)
+    sph = SPHSolverCfg(sph_method="dfsph")
+    if representation == "native":
+        if "sph_cfg" not in getattr(DexUniSolverCfg, "__dataclass_fields__", {}):
+            pytest.skip("This DexSim build does not compose SPH through DexUni.")
+        solver = DexUniSolverCfg(sph_cfg=sph)
+    elif representation == "mapping":
+        solver = {"solver_type": "dexuni", "sph_cfg": {"sph_method": "dfsph"}}
+    else:
+        solver = {"solver_type": "dexuni", "sph_cfg": asdict(sph)}
     manager = _fluid_manager_for_renderer("no-render")
     manager.sim_config.physics_cfg.solver_cfg = solver
     monkeypatch.setattr(SimulationManager, "_get_process_renderer", lambda: None)
@@ -318,7 +466,7 @@ def test_solver_defaults_allow_fluid_owned_density_and_spacing(
     assert descriptor.density == density
     assert descriptor.particle_spacing == spacing
     if representation != "mapping":
-        values = asdict(solver) if representation == "native" else solver
+        values = asdict(sph) if representation == "native" else solver["sph_cfg"]
         assert values["rest_density"] == SPHSolverCfg().rest_density
         assert values["particle_diameter"] == SPHSolverCfg().particle_diameter
 
@@ -330,15 +478,21 @@ def test_solver_nondefault_physical_values_must_match_fluid(
 ):
     from dataclasses import asdict
     from unittest.mock import Mock
-    from dexsim.engine.newton_physics import SPHSolverCfg
+    from dexsim.engine.newton_physics import DexUniSolverCfg, SPHSolverCfg
     from embodichain.lab.sim import SimulationManager
 
     density, spacing = 800.0, 0.003
-    solver = SPHSolverCfg(rest_density=density, particle_diameter=spacing)
+    sph = SPHSolverCfg(
+        sph_method="dfsph", rest_density=density, particle_diameter=spacing
+    )
     if conflict is not None:
-        setattr(solver, conflict, 1200.0 if conflict == "rest_density" else 0.02)
+        setattr(sph, conflict, 1200.0 if conflict == "rest_density" else 0.02)
     if representation == "mapping":
-        solver = asdict(solver)
+        solver = {"solver_type": "dexuni", "sph_cfg": asdict(sph)}
+    else:
+        if "sph_cfg" not in getattr(DexUniSolverCfg, "__dataclass_fields__", {}):
+            pytest.skip("This DexSim build does not compose SPH through DexUni.")
+        solver = DexUniSolverCfg(sph_cfg=sph)
     manager = _fluid_manager_for_renderer("no-render")
     manager.sim_config.physics_cfg.solver_cfg = solver
     monkeypatch.setattr(SimulationManager, "_get_process_renderer", lambda: None)

From c224a59dd8fe716ec5a3389fcee7054852e104a7 Mon Sep 17 00:00:00 2001
From: yuecideng <dengyueci@qq.com>
Date: Tue, 27 Oct 2026 23:36:20 +0800
Subject: [PATCH 19/19] perf(tutorial): use static fluid container

---
 agent_context/topics/simulation-system/fluids.md | 11 ++++++-----
 scripts/tutorials/sim/create_fluid.py            |  4 +---
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/agent_context/topics/simulation-system/fluids.md b/agent_context/topics/simulation-system/fluids.md
index f4ae32330..51016ff70 100644
--- a/agent_context/topics/simulation-system/fluids.md
+++ b/agent_context/topics/simulation-system/fluids.md
@@ -24,8 +24,9 @@ establish isolation. The development build
 run this fluid path.
 
 The current native DexUni/DFSPH implementation is a prototype: DFSPH follows
-the rigid step with one-way rigid-to-fluid interaction. It requires the pure
-MuJoCo rigid path and cannot mix fluids with cloth or soft-body particles.
+the rigid step with one-way rigid-to-fluid interaction. When rigid bodies are
+present, it requires the pure MuJoCo rigid path; static collision shapes do not
+require a rigid-body solver. It cannot mix fluids with cloth or soft-body particles.
 Online robot pouring, Gym declarations and replay belong to the dependent task
 layer; two-way fluid feedback is not implied by DexUni's rigid/soft coupling.
 
@@ -71,9 +72,9 @@ follow the renderer requirements of the [material system](materials.md).
 
 `scripts/tutorials/sim/create_fluid.py` demonstrates procedural box sampling,
 five primitive container walls, prepare, step, state reads and reset. Pass
-`--num-envs` to use multiple independent worlds on a capable SPH build. It
-disables MuJoCo rigid-rigid contacts for its fixed container while retaining
-DFSPH boundary collision. It needs no robot, task configuration or downloaded assets:
+`--num-envs` to use multiple independent worlds on a capable SPH build. The
+fixed container uses static SDF collision shapes, so DFSPH can collide with it
+without running a rigid-body solver. It needs no robot, task configuration or downloaded assets:
 
 ```bash
 python scripts/tutorials/sim/create_fluid.py --headless --renderer no-render
diff --git a/scripts/tutorials/sim/create_fluid.py b/scripts/tutorials/sim/create_fluid.py
index 03e875486..9e837b379 100644
--- a/scripts/tutorials/sim/create_fluid.py
+++ b/scripts/tutorials/sim/create_fluid.py
@@ -86,8 +86,6 @@ def main(args: argparse.Namespace | None = None) -> None:
                 collision_cfg=None,
                 solver_cfg={
                     "solver_type": "dexuni",
-                    # The container is fixed; DFSPH still collides with its SDF.
-                    "mujoco_options": {"disable_contacts": True},
                     "sph_cfg": {
                         "sph_method": "dfsph",
                         "particle_diameter": spacing,
@@ -112,7 +110,7 @@ def main(args: argparse.Namespace | None = None) -> None:
             sim.add_rigid_object(
                 RigidObjectCfg(
                     uid=uid,
-                    body_type="kinematic",
+                    body_type="static",
                     init_pos=position,
                     shape=MeshCfg(
                         vertices=mesh.vertices,