[SaveAanchal]

Stacked on #717.

What

What vendors deliver, and the check every delivery passes before it is converted to Lance. One raw MCAP per whole recording: episode_<id>/episode.mcap, plus an optional annotation.mcap for labels.

• egomimic/rldb/mcap/SPEC.md: the vendor-facing spec, covering:
  • layout and clock (absolute Unix ns);
  • human and Eva topic tables;
  • episode info: lab / site / vendor as in the Lance pilot, and vendor-recording-id for recording_id;
  • hands: hand-joints names each vendor's layout, and the wrist frame convention;
  • frames, cameras (one frame per message, lens models, upright rotation), tasks and labels;
  • how each Lance field is filled.
• spec.py: the same rules as data. schemas.py: the egoverse.* messages, rendered to proto/egoverse.proto for vendors.
• validate.py: python -m egomimic.rldb.mcap.validate episode_<id>/....

  • Check groups: presence and types, video, clocks, calibration and frame graph, IMU, poses, and task/label spans. The label rules are the same ones lance/annotations.py applies.

  • Frame rules the converter relies on:

    • a camera's video, -info and -info-extra must name the same frame;
    • on a head-worn rig, the main (steps) camera needs a /tf_static path to head, which is how the steps are placed;
    • /tf_static translations must be finite, as well as its rotations being unit.

    Each of these used to pass validation and then fail inside conversion.

  • Output: PASS or the list of problems, plus the Lance recording_id the episode will get.

Tests

• tests/unit/test_mcap_validate.py: synthetic episodes, each with one breakage, must report exactly that problem. That includes a calibration naming another frame than its video, a steps camera fixed in the world instead of on the head, and a NaN static translation. Also checks proto/enum sync and the Lance ID and annotation types.
• tests/integration/test_mcap_abc.py: real episodes, via EGOVERSE_TEST_* paths.
• Real Aria check: two Aria recordings (VRS + MPS) converted to the spec both pass.
• Ryan's columns: every column of aria_to_lance's steps rebuilds from those MCAPs alone.

pyproject.toml adds mcap, mcap-protobuf-support and foxglove-schemas-protobuf, and ships the .proto.

🤖 Generated with Claude Code

[RyanPCo]

Warning

This pull request is not mergeable via GitHub because a downstack PR is open. Once all requirements are satisfied, merge this PR as a stack on Graphite.
Learn more

• aria_to_lance: half-open task and alias end #727
• trainHydra: record the split and Lance versions a run read #726
• rldb, algo, eval: leave an untracked hand out of losses, stats and bounds #725
• rldb/lance: require_hands_in_frame and per-hand sample masks #724
• rldb/lance: action_clamp and history_floor at segment bounds #723
• rldb/lance: one leaf per recording, sampled over its task segments #722
• rldb/mcap: vendor MCAP spec 0.1 and validator #718 👈 (View in Graphite)
• rldb: move MultiDataset and format-neutral pieces out of zarr/ #717
• lance migration #716
• pi: keep per-sample intrinsics so overlays use the episode camera #690 : 2 other dependent PRs (#691 , #707 )
• data: multi-frame image memory window with per-step mask; history stride in seconds #689
• norm stats: cache sampled rows per episode so splits share them #687 : 1 other dependent PR (#688 )
• data: exclude Mecka episodes with failed egomotion; repin val videos #686
• eval: log validation videos to wandb #684 : 1 other dependent PR (#685 )
• slurm: stop cancelled jobs from leaking whole GPUs #682 : 1 other dependent PR (#683 )
• algo: RDT (Robotics Diffusion Transformer) on the HPT chassis #680 : 1 other dependent PR (#681 )
• pi: initialize from PaliGemma with a scratch action expert #677 : 1 other dependent PR (#678 )
• perf: vectorized image augs and a torch.compile knob for HPT and Pi; Pi clips gradients like openpi #676
• loader: one zarr read per key, not one per keymap entry #674
• eval: build the pinned-episode video batches only where they are used #673
• checkpoints: last.ckpt as a symlink, not a second full copy #672
• data: the mecka freeform folding operator-scaling ladder (k = 1/2/4) #671
• transforms: per-step wrist framing for proprio history #670
• viz: one keypoint renderer, the horizon trace #669
• configs: the flagship keypoint HPT arm, on the seen/unseen operator split #668
• trainer: ddp_bf16_compress, DDP with a bf16 gradient all-reduce #667
• eval: overlay videos play at the source rate under DDP #666
• hpt: build the policy on CPU and let Lightning place it #664 : 1 other dependent PR (#665 )
• hpt: never re-initialise pretrained stems; hash-check them; mask text padding #645 : 1 other dependent PR (#646 )
• obs: proprio history (K proprio steps, one token each, learned time embedding) #650
• loss: mask the repeat-last action padding out of the HPT loss #649
• resnet: keep a frozen backbone in eval mode; give backbones their own LR group #652
• augs: per-sample image augmentation for HPT train-mode augs #651
• eval: deterministic validation (seeded prompts + flow sampling); log-only grad-norm MAD #644
• data: mecka_fold_flagship_top3_hpt_6d, the 3-operator flagship train split #643
• val: subsampled metric loaders + pinned contiguous video loaders #642
• infra: mecka 6D / keypoint recipes, 300M models, defaults, and the rollout revert #640
• eval: layout-aware frame-invariant metrics, and a train-split viz head #659
• data: stop letting one bad episode or a rotation channel take a run down #658
• norm: guard degenerate channels, horizon pooling, and no rot6d normalization #657
• keypoints: the 144-D wrist-first hand-keypoint action becomes the human default #656
• mecka: fix the double-mirrored LEFT wrist frame, and correct existing zarrs #655
• 6d: PI goes 6D-only; Eva and Human gain the continuous-rotation modes #654
• 6d: continuous rotation representation, bimanual layouts, shared norm helpers #653
• train: give each Slurm slurm job gets its own cache dir to avoid conflict #638 : 1 other dependent PR (#639 )
• write git version into the run dir for reproducibility #637
• train: don't load PI base weights on resume and verify that checkpoint path exists #636
• configs: declare proprio and actions dims once per HPT model config #634 : 1 other dependent PR (#635 )
• cache norm stats for data configs to minimize recomputation #633
• import cloud packages lazily to support local training #631 : 1 other dependent PR (#632 )
• cache dataset resolver for faster training initialization #630
• validate episode hashes in data config before training #628
• modify pi camera map to match data #629
• add tests to ensure training works between every model and data vendor #626
• run CI tests on every PR #625
• add env variables for user-specific paths #624
• build: set pyproject and uv as the dependency managers #623
• fix(configs): fix paths for data hydra yaml configs #622
• refactor(algo): delete ACT #627 : 1 other dependent PR (#621 )
• tests: create dedicated test folder with unit and integration tests #620
• add pyright type checker #619
• main

This stack of pull requests is managed by Graphite. Learn more about stacking.