diff --git a/README.md b/README.md index e24e477..54d6008 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,12 @@

- GraspKit + GraspKit

Python 3.10+ GitHub Actions + Homepage + Docs MIT License

@@ -13,8 +15,14 @@ and dexterous hands. It synthesizes grasps, plans trajectories, trains policies, and deploys them in simulation or on real robots; it is built on top of [RoboKit](https://github.com/openrobokit/robokit). +

+ Robot hands grasping objects during a real-world run
+ Real-world grasping with dexterous robot hands in San Diego. +

+ ## Quick links +- [Homepage](https://openrobokit.com/graspkit/) - [Docs](https://github.com/openrobokit/graspkit/blob/main/docs/guide/overview.md) - [Quickstart](https://github.com/openrobokit/graspkit/blob/main/docs/guide/quickstart.md) - [Installation](https://github.com/openrobokit/graspkit/blob/main/docs/guide/installation.md) @@ -35,21 +43,21 @@ See [Installation](https://github.com/openrobokit/graspkit/blob/main/docs/guide/ diff --git a/docs/assets/features/embodiments.webp b/docs/assets/features/embodiments.webp deleted file mode 100644 index fcaad67..0000000 Binary files a/docs/assets/features/embodiments.webp and /dev/null differ diff --git a/docs/assets/features/grasp-optimization.webp b/docs/assets/features/grasp-optimization.webp deleted file mode 100644 index 30185a7..0000000 Binary files a/docs/assets/features/grasp-optimization.webp and /dev/null differ diff --git a/docs/assets/features/policy-learning.webp b/docs/assets/features/policy-learning.webp deleted file mode 100644 index 4ea3e4e..0000000 Binary files a/docs/assets/features/policy-learning.webp and /dev/null differ diff --git a/docs/assets/features/sim-to-real.webp b/docs/assets/features/sim-to-real.webp deleted file mode 100644 index 84fcdf3..0000000 Binary files a/docs/assets/features/sim-to-real.webp and /dev/null differ diff --git a/docs/assets/gallery/grasp_optim.webp b/docs/assets/gallery/grasp_optim.webp deleted file mode 100644 index fa58000..0000000 Binary files a/docs/assets/gallery/grasp_optim.webp and /dev/null differ diff --git a/docs/assets/graspkit-banner.webp b/docs/assets/graspkit-banner.webp deleted file mode 100644 index 62d9866..0000000 Binary files a/docs/assets/graspkit-banner.webp and /dev/null differ diff --git a/docs/assets/guide/deploy_capture.webp b/docs/assets/guide/deploy_capture.webp deleted file mode 100644 index e475af1..0000000 Binary files a/docs/assets/guide/deploy_capture.webp and /dev/null differ diff --git a/docs/assets/guide/deploy_pause.webp b/docs/assets/guide/deploy_pause.webp deleted file mode 100644 index 35b4415..0000000 Binary files a/docs/assets/guide/deploy_pause.webp and /dev/null differ diff --git a/docs/assets/guide/deploy_plan.webp b/docs/assets/guide/deploy_plan.webp deleted file mode 100644 index 4f857f8..0000000 Binary files a/docs/assets/guide/deploy_plan.webp and /dev/null differ diff --git a/docs/assets/guide/deploy_release.webp b/docs/assets/guide/deploy_release.webp deleted file mode 100644 index 8d67ed1..0000000 Binary files a/docs/assets/guide/deploy_release.webp and /dev/null differ diff --git a/docs/assets/guide/deploy_segment.webp b/docs/assets/guide/deploy_segment.webp deleted file mode 100644 index a5fbba0..0000000 Binary files a/docs/assets/guide/deploy_segment.webp and /dev/null differ diff --git a/docs/assets/guide/grasp_filters.png b/docs/assets/guide/grasp_filters.png deleted file mode 100644 index 05318ea..0000000 Binary files a/docs/assets/guide/grasp_filters.png and /dev/null differ diff --git a/docs/assets/guide/sharpa_init.webp b/docs/assets/guide/sharpa_init.webp deleted file mode 100644 index 6c83772..0000000 Binary files a/docs/assets/guide/sharpa_init.webp and /dev/null differ diff --git a/docs/assets/guide/sharpa_optim.webp b/docs/assets/guide/sharpa_optim.webp deleted file mode 100644 index 347c657..0000000 Binary files a/docs/assets/guide/sharpa_optim.webp and /dev/null differ diff --git a/docs/assets/guide/xarm7_sharpa_optim.webp b/docs/assets/guide/xarm7_sharpa_optim.webp deleted file mode 100644 index 8fd1a51..0000000 Binary files a/docs/assets/guide/xarm7_sharpa_optim.webp and /dev/null differ diff --git a/docs/guide/deployment.md b/docs/guide/deployment.md index 1bec147..218dc99 100644 --- a/docs/guide/deployment.md +++ b/docs/guide/deployment.md @@ -29,32 +29,32 @@ Click **Capture** to start over whenever it is enabled. Pick a **Scene**. Click **Capture**. See the camera image on the right. -![Captured simulation RGB alongside the xArm7 and xHand scene](../assets/guide/deploy_capture.webp) +![Captured simulation RGB alongside the xArm7 and xHand scene](https://openrobokit.com/assets/docs/graspkit/assets/guide/deploy_capture.webp) ### 2. Segment Click **Segment**. Click the object in the large image. Blue = selected. Click **Submit Mask**. -![SAM2 object mask with a positive prompt and the segmentation controls](../assets/guide/deploy_segment.webp) +![SAM2 object mask with a positive prompt and the segmentation controls](https://openrobokit.com/assets/docs/graspkit/assets/guide/deploy_segment.webp) ### 3. Check Grasp/Trajectory Click **Check Grasp/Trajectory**. **Grasp** picks a candidate. **Trajectory Step** previews its motion. -![A candidate grasp and its planned trajectory beside the segmented red cloud](../assets/guide/deploy_plan.webp) +![A candidate grasp and its planned trajectory beside the segmented red cloud](https://openrobokit.com/assets/docs/graspkit/assets/guide/deploy_plan.webp) ### 4. Deploy Click **Deploy** to move. **Deploy Until Step** sets where to stop. Raise it; click **Deploy** to continue. -![Simulation paused partway through execution using Deploy Until Step](../assets/guide/deploy_pause.webp) +![Simulation paused partway through execution using Deploy Until Step](https://openrobokit.com/assets/docs/graspkit/assets/guide/deploy_pause.webp) ### 5. Release Click **Release**. Hand opens. Arm goes home. `--loop` lets you repeat. -![The completed simulated trajectory, ready to release and park](../assets/guide/deploy_release.webp) +![The completed simulated trajectory, ready to release and park](https://openrobokit.com/assets/docs/graspkit/assets/guide/deploy_release.webp) ### Controls and options diff --git a/docs/guide/grasp_optim.md b/docs/guide/grasp_optim.md index 1d817e7..67ac496 100644 --- a/docs/guide/grasp_optim.md +++ b/docs/guide/grasp_optim.md @@ -1,7 +1,7 @@ # Grasp Optimization (`grasp_optim`) -| ![A floating Sharpa hand optimizing a grasp on a cube](../assets/guide/sharpa_optim.webp) | ![An xArm7 + Sharpa optimizing arm and hand joints together](../assets/guide/xarm7_sharpa_optim.webp) | +| ![A floating Sharpa hand optimizing a grasp on a cube](https://openrobokit.com/assets/docs/graspkit/assets/guide/sharpa_optim.webp) | ![An xArm7 + Sharpa optimizing arm and hand joints together](https://openrobokit.com/assets/docs/graspkit/assets/guide/xarm7_sharpa_optim.webp) | | ----------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | | A floating hand optimizing a grasp. | An arm and hand optimized together on a fixed base. | @@ -67,7 +67,7 @@ The sections below build the same stage step by step in Python. ### Sampling initial poses -![Initial Sharpa hand poses sampled around a mustard bottle](../assets/guide/sharpa_init.webp) +![Initial Sharpa hand poses sampled around a mustard bottle](https://openrobokit.com/assets/docs/graspkit/assets/guide/sharpa_init.webp) Init samples drawn with the palm-down cone below: red is the hand axis (`palm_forward`), gray is the target (world -z). Both start at the wrist; the label is their angle, always within the 60 deg cone. @@ -118,7 +118,7 @@ grasp_qpos = optimizer.optimize([scene], num_samples_per_scene=8) # (1, 8, 18) Grasp filters live in `GraspTrajBuilderConfig` and run first inside `GraspTrajBuilder.build()`. They only mark grasps, they don't drop them. Call `filter_grasps` to run them alone, without motion planning: -![A passing and a failing xHand grasp for each filter: contact-avoid, penetration, hand axis](../assets/guide/grasp_filters.png) +![A passing and a failing xHand grasp for each filter: contact-avoid, penetration, hand axis](https://openrobokit.com/assets/docs/graspkit/assets/guide/grasp_filters.png) ```python from graspkit.configs.experiment_config import GraspTrajBuilderConfig diff --git a/docs/guide/overview.md b/docs/guide/overview.md index a250c88..0b699ac 100644 --- a/docs/guide/overview.md +++ b/docs/guide/overview.md @@ -1,10 +1,14 @@ -![GraspKit](../assets/graspkit-banner.webp) +![GraspKit](https://openrobokit.com/assets/docs/graspkit/assets/graspkit-banner.webp) GraspKit is an end-to-end toolkit for manipulation across various robot embodiments, including grippers and dexterous hands. It synthesizes grasps, plans trajectories, trains policies, and deploys them in simulation or on real robots; it is built on top of [RoboKit](https://github.com/openrobokit/robokit). +[![Robot hands grasping objects during a real-world run](https://openrobokit.com/assets/docs/graspkit/assets/showcase/real-world.webp)](https://openrobokit.com/graspkit/) + +*Real-world grasping with dexterous robot hands in San Diego.* + ## Key features - **Fast grasp optimization.** GPU optimizers refine thousands of grasp seeds per batch against @@ -18,10 +22,10 @@ in simulation or on real robots; it is built on top of [RoboKit](https://github. - **Real-world validation.** Run trained policies on xArm and Vega robots with calibrated cameras and collision-checked motions. See [Real deployment](real_deployment.md). -| [![Fast grasp optimization](../assets/features/grasp-optimization.webp)](grasp_optim.md) | [![Policy training](../assets/features/policy-learning.webp)](training.md) | +| [![Fast grasp optimization](https://openrobokit.com/assets/docs/graspkit/assets/features/grasp-optimization.webp)](grasp_optim.md) | [![Policy training](https://openrobokit.com/assets/docs/graspkit/assets/features/policy-learning.webp)](training.md) | | --- | --- | | **[Fast grasp optimization](grasp_optim.md)** | **[Performant policy training](training.md)** | -| [![All arms, all hands](../assets/features/embodiments.webp)](../reference/robot_hands.md) | [![Real-world robustness](../assets/features/sim-to-real.webp)](real_deployment.md) | +| [![All arms, all hands](https://openrobokit.com/assets/docs/graspkit/assets/features/embodiments.webp)](../reference/robot_hands.md) | [![Real-world robustness](https://openrobokit.com/assets/docs/graspkit/assets/features/sim-to-real.webp)](real_deployment.md) | | **[All arms, all hands](../reference/robot_hands.md)** | **[Real-world robustness](real_deployment.md)** | ## How it works @@ -42,3 +46,4 @@ For the graph of stages and the artifacts between them, see - [Quickstart](quickstart.md) - [Installation](installation.md) - [GitHub](https://github.com/openrobokit/graspkit) +- [Website](https://openrobokit.com/graspkit/) diff --git a/docs/guide/quickstart.md b/docs/guide/quickstart.md index 365b5ab..3c0153b 100644 --- a/docs/guide/quickstart.md +++ b/docs/guide/quickstart.md @@ -11,6 +11,8 @@ uvx --from graspkit python -m graspkit.gallery.grasp_optim Pick a hand, arm, and object. Press *Optimize* to generate grasps, then browse them with the *Grasp* slider. The gallery caches results for the next visit. +![The grasp gallery switching from xHand to a Robotiq gripper](https://openrobokit.com/assets/docs/graspkit/assets/gallery/grasp_optim.webp) + ## Optimize one grasp After [installing](installation.md), run the smallest grasp optimization example: diff --git a/scripts/benchmark/grasp_opt/bench_grasp_optim_results.robokit_opt.md b/scripts/benchmark/grasp_opt/bench_grasp_optim_results.robokit_opt.md index 414d471..e85be5f 100644 --- a/scripts/benchmark/grasp_opt/bench_grasp_optim_results.robokit_opt.md +++ b/scripts/benchmark/grasp_opt/bench_grasp_optim_results.robokit_opt.md @@ -1,4 +1,4 @@ -# Grasp Benchmark Results — mesh_vs_low_sdf (graspkit on robokit contact var and solvers; volcan RTX 3090, idle GPU) +# Grasp Benchmark Results — mesh_vs_low_sdf (graspkit on robokit contact var and solvers; RTX 3090, idle GPU) Date: 2026-09-05 01:29 ## xhand_right_dev_mesh_vs_low_sdf (floating task, 8 scenes × 64 samples = 512 grasps) | Metric | mesh_warp_gd | mesh_warp_lm | low_sdf_warp_gd | low_sdf_warp_lm | diff --git a/scripts/benchmark/grasp_policy/results.csv b/scripts/benchmark/grasp_policy/results.csv index 1d6fbe0..d919328 100644 --- a/scripts/benchmark/grasp_policy/results.csv +++ b/scripts/benchmark/grasp_policy/results.csv @@ -250,8 +250,8 @@ agilex_aloha,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,64,64 agilex_aloha,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,57,57.0,0,0,43,0,6.243239,data/benchmark/grasp_policy/unified_policy/approach1k-validation/agilex_aloha-pointnet-local-cvae-fk-b32-1k-s0/learned-pose-open.jsonl,validation_1k_approach_robot_mask_local_pointnet_joint_pose_evaluator_joint_range_loss,maniskill,depth,cvae,1000,60000,0,uniform,64,6.237489,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, agilex_aloha,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,72,72.0,0,0,28,0,5.302290,data/benchmark/grasp_policy/unified_policy/approach5k-validation/agilex_aloha-pointnet-local-cvae-fk-b32-1p5k-s0/learned-pose-closed.jsonl,validation_1500_approach_robot_mask_local_pointnet_joint_pose_evaluator_joint_range_loss,maniskill,depth,cvae,1500,60000,0,uniform,128,5.292472,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, agilex_aloha,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,55,55.0,0,0,45,0,4.183902,data/benchmark/grasp_policy/unified_policy/approach5k-validation/agilex_aloha-pointnet-local-cvae-fk-b32-1p5k-s0/learned-pose-open.jsonl,validation_1500_approach_robot_mask_local_pointnet_joint_pose_evaluator_joint_range_loss,maniskill,depth,cvae,1500,60000,0,uniform,64,4.175911,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, -panda_allegro,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,2,2.0,0,0,98,0,10.669350,data/benchmark/grasp_policy/unified_policy/approach1k-validation/panda_allegro-rgb-cvae-fk-joint-range-b256-cowles-s0/learned-pose-closed.jsonl,validation_1k_approach_robot_mask_joint_pose_evaluator_joint_range_loss,maniskill,rgb,cvae,1000,30000,0,uniform,128,10.666593,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, -panda_allegro,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,0,0.0,0,0,100,0,8.638556,data/benchmark/grasp_policy/unified_policy/approach1k-validation/panda_allegro-rgb-cvae-fk-joint-range-b256-cowles-s0/learned-pose-open.jsonl,validation_1k_approach_robot_mask_joint_pose_evaluator_joint_range_loss,maniskill,rgb,cvae,1000,30000,0,uniform,64,8.620034,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, +panda_allegro,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,2,2.0,0,0,98,0,10.669350,data/benchmark/grasp_policy/unified_policy/approach1k-validation/panda_allegro-rgb-cvae-fk-joint-range-b256-s0/learned-pose-closed.jsonl,validation_1k_approach_robot_mask_joint_pose_evaluator_joint_range_loss,maniskill,rgb,cvae,1000,30000,0,uniform,128,10.666593,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, +panda_allegro,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,0,0.0,0,0,100,0,8.638556,data/benchmark/grasp_policy/unified_policy/approach1k-validation/panda_allegro-rgb-cvae-fk-joint-range-b256-s0/learned-pose-open.jsonl,validation_1k_approach_robot_mask_joint_pose_evaluator_joint_range_loss,maniskill,rgb,cvae,1000,30000,0,uniform,64,8.620034,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, xarm7_sharpa,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,73,73.0,0,0,27,0,22.809578,data/benchmark/grasp_policy/unified_policy/approach1k-validation/xarm7_sharpa-pointnet-cvae-fk-b256-s0/learned-pose-closed.jsonl,validation_1k_approach_robot_mask_joint_pose_evaluator_joint_range_loss_regenerated_bank,maniskill,depth,cvae,999,30000,0,uniform,128,22.785804,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, yam,arm_aware,independent_jaws,policy_closed_loop,validation,0,learned,100,64,100,8,8.0,0,0,92,0,18.617147,data/benchmark/grasp_policy/unified_policy/approach5k-validation/yam-pointnet-cvae-fk-geometry-b256-5k-s1/learned-pose-closed.jsonl,validation_5k_approach_robot_mask_joint_pose_evaluator_geometry_finetune2k_guidance8,maniskill,depth,cvae,4072,2000,1,uniform,128,18.603747,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, arx_x5,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,83,83.0,0,0,17,0,6.009547,data/benchmark/grasp_policy/unified_policy/approach5k-validation/arx_x5-pointnet-cvae-fk-joint-range-b256-3500-s0/learned-pose-closed.jsonl,validation_3500_approach_robot_mask_joint_pose_evaluator_joint_range_loss,maniskill,depth,cvae,3500,30000,0,uniform,128,6.002164,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, @@ -279,16 +279,16 @@ arx_x5,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,76,76.0,0,0,2 arx_x5,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,82,82.0,0,0,18,0,7.783300,data/benchmark/grasp_policy/unified_policy/approach5k-validation/arx_x5-pointnet-cvae-fk-joint-range-b256-3500-s0/learned-pose-closed-temperature1p3-guidance8.jsonl,validation_3500_approach_robot_mask_joint_pose_evaluator_joint_range_loss_guidance8_temperature1p3,maniskill,depth,cvae,3500,30000,0,uniform,128,7.765536,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, arx_x5,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,84,84.0,0,0,16,0,6.330594,data/benchmark/grasp_policy/unified_policy/approach5k-validation/arx_x5-pointnet-cvae-fk-joint-range-b256-3500-s0/learned-pose-open-temperature0p5-guidance8.jsonl,validation_3500_approach_robot_mask_joint_pose_evaluator_joint_range_loss_guidance8_temperature0p5,maniskill,depth,cvae,3500,30000,0,uniform,64,6.34098,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, 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-yam,arm_aware,independent_jaws,policy_open_loop,validation,0,learned,100,64,100,11,11.0,0,0,89,0,9.221715,data/benchmark/grasp_policy/unified_policy/approach5k-validation/yam-flow-scene-rank-critic-cowles-s1/learned-pose-open-rank.jsonl,validation_5k_yam_local_flow_objectheld_rank,maniskill,depth,flow_transformer,4072,60000,1,1.0187715888023376,64,9.198038,bce_scene_rank_object_disjoint,false,approach_robot_mask,training_statistics,,,, -yam,arm_aware,independent_jaws,policy_closed_loop,validation,0,learned,100,64,100,8,8.0,0,0,92,0,9.919965,data/benchmark/grasp_policy/unified_policy/approach5k-validation/yam-flow-scene-rank-critic-cowles-s1/learned-pose-closed-bce.jsonl,validation_5k_yam_local_flow_objectheld_bce,maniskill,depth,flow_transformer,4072,60000,1,0.2899530827999115,128,9.930355,bce_object_disjoint,false,approach_robot_mask,training_statistics,,,, 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arx_x5,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,90,90.0,0,0,10,0,5.968614,data/benchmark/grasp_policy/unified_policy/approach5k-validation/arx_x5-pointnet-cvae-fk-joint-range-b256-3500-s0/learned-pose-closed-temperature0p4-guidance8.jsonl,validation_3500_approach_robot_mask_joint_pose_evaluator_joint_range_loss_guidance8_temperature0p4,maniskill,depth,cvae,3500,30000,0,uniform,128,5.958576,bce_initial_feedback,true,initial_midpoint_pregrasp,none,,,, -yam,arm_aware,independent_jaws,policy_open_loop,validation,0,learned,100,64,100,7,7.0,0,0,93,0,7.191297,data/benchmark/grasp_policy/unified_policy/approach5k-validation/yam-flow-scene-rank-critic-cowles-s1/learned-pose-open-rank-guidance8.jsonl,validation_5k_yam_local_flow_objectheld_rank_guidance8,maniskill,depth,flow_transformer,4072,60000,1,1.0187715888023376,64,7.178242,bce_scene_rank_object_disjoint,false,approach_robot_mask,training_statistics,,,, 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agilex_aloha,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,40,40.0,0,0,60,0,5.485006,data/benchmark/grasp_policy/unified_policy/teacher-execution-audit-0930/aloha-rgb-distillation/dev-closed.jsonl,validation_1p5k_rgb_depth_feature_distillation_frozen_depth_actor_evaluator_encoder8k,maniskill,rgb,flow_transformer,1500,60000,0,0.5242571234703064,128,5.470937,bce_initial_feedback,false,initial_midpoint_pregrasp,train_mean_std,,,, agilex_aloha,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,58,58.0,0,0,42,0,4.473273,data/benchmark/grasp_policy/unified_policy/teacher-execution-audit-0930/aloha-rgb-resnet-distillation/dev-open.jsonl,validation_1p5k_rgb_pretrained_resnet_depth_feature_distillation_frozen_depth_actor_evaluator_encoder8k,maniskill,rgb,flow_transformer,1500,60000,0,0.5242571234703064,64,4.459463,bce_initial_feedback,false,initial_midpoint_pregrasp,train_mean_std,,,, @@ -318,12 +318,12 @@ 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panda_allegro,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,69,69,0,0,31,0,7.177007,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/validation/depth-native-positive512-critic1408-open-Dev100-g0-cluster/Dev100.jsonl,validation_panda_native_positive_actor512_critic1408_rank64_matched10080_updates_guidance0,maniskill,depth,flow_transformer,2860,20001,0,0.48948270082473755,64,7.168019,selected_success_64_object_disjoint,true,native_initial_training2048,train_mean_std,,,, @@ -349,66 +349,66 @@ panda_allegro,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,66,66, yam,arm_aware,independent_jaws,policy_closed_loop,validation,0,learned,100,64,100,83,83,0,0,17,0,5.861245,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/yam-validation/depth-native-positive1017-critic1024-closed-Dev100-g8-cluster/Dev100.jsonl,validation_yam_native_positive_actor1017_critic1024_rank64_matched10080_updates_guidance8,maniskill,depth,flow_transformer,1023,20001,0,0.6501001119613647,128,5.853674,selected_success_64_object_disjoint,true,native_initial_training1024,train_mean_std,,,, 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panda_allegro,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,64,64,0,0,36,0,7.514752,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/panda-depth-flow-fresh1203/open-Dev100/Dev100.jsonl,validation_panda_depth_flow_new_objects256_critic256_on_policy,maniskill,depth,flow_transformer,4273,20001,0,0.7133260369300842,64,7.502337,selected_success_64_object_disjoint,true,native_current_policy_new_objects256_initial_feedback,train_mean_std,,,, @@ -423,8 +423,8 @@ agilex_aloha,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,71,71,0 agilex_aloha,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,85,85,0,0,15,0,6.524176,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/aloha-warm-post1204/RGB-new256/closed-Dev100/Dev100.jsonl,validation_aloha_RGB_new_objects256_warm20k_critic256,maniskill,RGB,cvae,3913,20001,0,0.3872889578342438,128,6.515346,selected_success_64_object_disjoint,true,native_new_objects256_initial_feedback,train_mean_std,,,, agilex_aloha,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,86,86,0,0,14,0,4.598306,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/aloha-flow-positive-post1204/depth-new128/open-Dev100/Dev100.jsonl,validation_aloha_depth_native_positive128_flow_warm20k_critic256,maniskill,depth,flow_transformer,4169,20001,0,0.5885715484619141,64,4.588633,selected_success_64_object_disjoint,true,native_current_flow_objects256_initial_feedback,train_mean_std,,,, agilex_aloha,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,83,83,0,0,17,0,6.033966,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/aloha-flow-positive-post1204/depth-new128/closed-Dev100/Dev100.jsonl,validation_aloha_depth_native_positive128_flow_warm20k_critic256,maniskill,depth,flow_transformer,4169,20001,0,0.5885715484619141,128,6.028259,selected_success_64_object_disjoint,true,native_current_flow_objects256_initial_feedback,train_mean_std,,,, -arx_x5,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,80,80,0,0,20,0,4.449998,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/arx-RGB-native512-volcan-post1204/open-Dev100/Dev100.jsonl,validation_arx_RGB_flow_new_objects256_critic512_on_policy,maniskill,RGB,flow_transformer,4677,20001,0,0.6682989597320557,64,4.444514,selected_success_64_object_disjoint,true,native_current_policy_new_objects512_initial_feedback,train_mean_std,,,, 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agilex_aloha,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,75,75,0,0,25,0,6.256642,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/aloha-flow-positive-post1204/depth-new256/closed-Dev100/Dev100.jsonl,validation_aloha_depth_native_positive256_flow_warm20k_critic256,maniskill,depth,flow_transformer,4425,20001,0,0.6202026009559631,128,6.245892,selected_success_64_object_disjoint,true,native_current_flow_objects256_initial_feedback,train_mean_std,,,, panda_allegro,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,58,58,0,0,42,0,7.507923,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/panda-flow-positive-post1204/depth-new128/open-Dev100/Dev100.jsonl,validation_panda_depth_native_positive128_flow_warm20k_critic256,maniskill,depth,flow_transformer,4529,20001,0,0.6200418472290039,64,7.497565,selected_success_64_object_disjoint,true,native_current_flow_objects256_initial_feedback,train_mean_std,,,, @@ -651,10 +651,10 @@ xarm7_sharpa,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,70,70 xarm7_sharpa,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,61,61,0,0,39,0,16.765789,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/cluster-evaluator-scaling1024-1206/sharpa-native-depth-CVAE1216/late-measured1218/labels256/post1206/open-Dev100/Dev100.jsonl,validation_sharpa-native-depth-CVAE1216_fresh_evaluator256_native_10080updates_guidance0,maniskill,depth,cvae,4323,10080,0,ranked_BCE,64,16.739817,fixed64_actor_object_held_training_only,,native_initial_feedback256_frozen_sharpa-native-depth-CVAE1216,,,,, 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yam,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,84,84,0,0,16,0,7.608449,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/cluster-evaluator-scaling1024-1206/yam-retained-RGB-guided1217-warm-head1218/late-measured1218/labels1024/post1206/closed-Dev100/Dev100.jsonl,validation_yam-retained-RGB-guided1217-warm-head1218_fresh_evaluator1024_native_10080updates_guidance8,maniskill,rgb,flow_transformer,2819,10080,0,BCE,128,7.603993,fixed64_actor_object_held_training_only,,native_initial_feedback1024_frozen_yam-retained-RGB-guided1217-warm-head1218,,,,, yam,arm_aware,,policy_open_loop,validation,0,learned,100,64,100,87,87,0,0,13,0,6.284667,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/cluster-evaluator-scaling1024-1206/yam-retained-RGB-guided1217-warm-head1218/late-measured1218/labels1024/post1206/open-Dev100/Dev100.jsonl,validation_yam-retained-RGB-guided1217-warm-head1218_fresh_evaluator1024_native_10080updates_guidance8,maniskill,rgb,flow_transformer,2819,10080,0,BCE,64,6.280555,fixed64_actor_object_held_training_only,,native_initial_feedback1024_frozen_yam-retained-RGB-guided1217-warm-head1218,,,,, panda_allegro,arm_aware,,policy_closed_loop,validation,0,learned,100,64,100,78,78,0,0,22,0,10.872877,data/benchmark/grasp_policy/unified_policy/native-positive-recipe-0930/cluster-evaluator-scaling1024-1206/panda-retained-RGB-guided1217-listwise1218/listwise-native-results1218-ranked-measured1218/labels1024/post1206/closed-Dev100/Dev100.jsonl,validation_panda-retained-RGB-guided1217-listwise1218_fresh_evaluator1024_native_10080updates_guidance8,maniskill,rgb,flow_transformer,4273,10080,0,ranked_BCE_listwise,128,10.858955,fixed64_actor_object_held_training_only,,native_initial_feedback1024_frozen_panda-retained-RGB-guided1217-listwise1218,,,,, diff --git a/scripts/tools/record_gallery.py b/scripts/tools/record_gallery.py index 6ae0254..7e6f017 100644 --- a/scripts/tools/record_gallery.py +++ b/scripts/tools/record_gallery.py @@ -3,6 +3,8 @@ Select a free GPU, then run: CUDA_VISIBLE_DEVICES=0 uv run --no-sync --with playwright scripts/tools/record_gallery.py + +Copy the output to the website repo at assets/docs/graspkit/assets/gallery/; openrobokit.com hosts the docs images. """ import time diff --git a/src/graspkit/stages/grasp_optim/optimize_pipeline.py b/src/graspkit/stages/grasp_optim/optimize_pipeline.py index 96130fb..e920c05 100644 --- a/src/graspkit/stages/grasp_optim/optimize_pipeline.py +++ b/src/graspkit/stages/grasp_optim/optimize_pipeline.py @@ -134,7 +134,8 @@ def optimize_grasps( # Stage 2: build the traj builder, the in-loop evaluator, and the grasp optimizer. traj_builder = build_planning_traj_builder(cfg, device, robot_config=robot_config) - evaluator = build_env(cfg.grasp_optim.evaluator, robot_config, device, enable_rendering=False) + # Keep rendering on: render_backend="none" segfaults sapien URDF RenderMaterial creation on a later reconfigure. + evaluator = build_env(cfg.grasp_optim.evaluator, robot_config, device) scene_descriptions = SceneDescription.load_scene_descriptions_msgpack(cfg.scene_description_path) grasp_optimizer = WarpGraspOptimizer( cfg.grasp_optim, robot_config, device, fixed_base_robot_config=cfg.traj_builder.fixed_base_robot_config diff --git a/tests/stages/grasp_optim/test_optimize_pipeline.py b/tests/stages/grasp_optim/test_optimize_pipeline.py index 834db43..dc9f366 100644 --- a/tests/stages/grasp_optim/test_optimize_pipeline.py +++ b/tests/stages/grasp_optim/test_optimize_pipeline.py @@ -55,7 +55,7 @@ def test_optimization_pipeline_saves_survivors_and_optional_labels( monkeypatch.setattr(optimize_pipeline, "cuda_timeit", lambda name: nullcontext()) optimize_pipeline.optimize_grasps(cfg) optimize_pipeline.build_env.assert_called_once_with( - cfg.grasp_optim.evaluator, cfg.robot_config, optimize_pipeline.device, enable_rendering=False + cfg.grasp_optim.evaluator, cfg.robot_config, optimize_pipeline.device ) saved = GraspKitData.load(str(output / "scene.h5")) torch.testing.assert_close(saved.grasp_qpos, qpos[0, :1])
- 1,024 dexterous grasps optimized in one GPU batch
+ 1,024 dexterous grasps optimized in one GPU batch
Fast grasp optimization 		- Object point cloud mapped by a grasp generator to a hand grasp
+ Object point cloud mapped by a grasp generator to a hand grasp
Policy learning
- Different arms with dexterous hands grasping the same object in simulation
+ Different arms with dexterous hands grasping the same object in simulation
All arms, all hands 		- Two real xArm robots with dexterous hands, diagonally split with their aligned simulation twin
+ Two real xArm robots with dexterous hands, diagonally split with their aligned simulation twin
Real-world robustness