- Replace cv2.VideoCapture with PyAV (av library) which handles AV1
codec properly. Decode each video once and index by frame number.
- Use AutoImageProcessor instead of AutoProcessor to avoid loading
the SigLIP tokenizer (which requires sentencepiece).
Made-with: Cursor
Run two parallel KNN analyses per dataset:
1. State-based: KNN in joint-state space
2. Image-based: KNN in SigLIP embedding space (google/siglip-base-patch16-224)
Both measure action chunk variance among cross-episode neighbors.
Comparing them reveals whether visual and proprioceptive similarity
agree on where data is inconsistent.
Output is a 4-row figure: state histogram, image histogram,
overlaid per-episode curves, and spatial heatmap colored by
image-based variance.
Made-with: Cursor
Add three new analysis scripts for dataset quality insight:
- create_frame_grid.py: random frame grid JPG for visual inspection
- workspace_density.py: 3D TCP trajectory clustering with K-means
- action_consistency.py: KNN-based action-state consistency analysis
with action chunk support (default chunk=30) matching policy learning
Also update create_progress_videos.py with configurable camera selection.
Made-with: Cursor
* fix(vqbet): use in-place fill_ to avoid overwriting DDP GPU buffers with CPU tensors
When VQ discretization phase completes, the code was overwriting
register_buffer('discretized') and register_buffer('freeze_codebook')
with torch.tensor(True), which is created on CPU. DDP then fails in
_sync_buffers() with: RuntimeError: No backend type associated with
device type cpu. Fix by updating the buffers in-place with .fill_(True)
so device and registration are preserved.
Made-with: Cursor
* test(vqbet): add regression test for in-place buffer update during discretization
Verifies that discretize() updates the 'discretized' and 'freeze_codebook'
registered buffers in-place (via fill_()) rather than replacing them with new
CPU tensors. The test checks data_ptr() identity and that the tensors remain
registered buffers after the call. This prevents regressions of the DDP fix.
Made-with: Cursor
* test(vqbet): add GPU regression test to verify buffers stay on CUDA after discretize()
Directly catches the original DDP failure mode: when buffers are replaced with
torch.tensor(True) they land on CPU, causing NCCL to raise 'No backend type
associated with device type cpu' in _sync_buffers(). The GPU test places the
model on cuda:0 and asserts both buffers remain on CUDA after discretization.
Made-with: Cursor
* test(vqbet): simplify to single device-check test in test_policies.py
Per reviewer feedback: remove the separate test file and replace the two
CPU/GPU tests (with data_ptr checks) with a single focused test in
tests/policies/test_policies.py that only asserts the registered buffers
remain on the model device after discretize(). Uses DEVICE from tests/utils.py
so it runs on whatever device the CI/user selects (cpu, cuda, mps).
Made-with: Cursor
* style: fix import order in test_policies.py to pass ruff/pre-commit checks
Made-with: Cursor
---------
Co-authored-by: Zhan DiJia <2476100824@example.com>
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
* docs(earthrover): update EarthRover Mini Plus dataset features and descriptions
* refactor(teleop): rename rover action keys to linear_velocity/angular_velocity
* fix(earthrover): align observation and action features with frodobots/berkeley-frodobots-lerobot-7k
* chore: address PR review comments
* ci: retrigger checks
Issue https://github.com/huggingface/lerobot/issues/1707
Action padding mask is set at LeRobotDataset as f"{key}_is_pad".
Wrong key doesn't raise any errors, however, padding mask is ignored,
resulting wrong attention at around the edges of an episode
when multi step actions is enabled (aka. action horizon is greater
than 1).
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Add a `cudnn_deterministic` flag to `TrainPipelineConfig` (default: False)
that sets `torch.backends.cudnn.deterministic = True` and disables benchmark
mode, eliminating CUDA floating-point non-determinism at the cost of ~10-20%
training speed. When False (default) the existing benchmark=True behaviour
is preserved.
* fix(ci): skip HF log in (and tests) in forks and community PRs
* chore(test): remove comment about test meant to be only run locally
* fix(tests): no hf log in decorator for xvla
* fix(test): no decorator in yield
* Add SLURM SARM progress annotation script.
Provide a standalone two-stage compute/aggregate pipeline for RA-BC progress generation so large datasets can be processed in parallel and optionally uploaded to the Hub.
Made-with: Cursor
* fix pr comments
* remove comments
* chore(docstrings): updating v2.1-v3.0 conversion script docstrings to match the new task label
* chore(task): renamming the default index label in the tasks DataFrame to task
* Revert "chore(docstrings): updating v2.1-v3.0 conversion script docstrings to match the new task label"
This reverts commit f55de3255278f23f18b5d955565f6768d094951d.
* chore(docstrings): updating docstrings to match dataset v3.0 architecture
* chore(format): formatting code
* Fixing metadata indexing when writing new Parquet file
Summary:
- addressing this issue: https://github.com/huggingface/lerobot/issues/2401
- vibe-coded bugfix by Claude Sonnet 4.5
* Backing out changes to convert_videos_of_camera
* Addressing Ruff pre-commit complaint
Summary:
- addressing "SIM113 Use `enumerate()` for index variable `ep_idx` in `for` loop"
---------
Co-authored-by: Paul <238953601+pac-robotics@users.noreply.github.com>
* fix(root): adding proper support for the root and new_root arguments
* feat(roots): adding a roots agrument for the merge operation
* chore(clean): cleaning up code
* chore(doctrings): updating doctrings with new features
* fix(repo_id): setting repo_id to None when not needed
* fix(roots/repo_ids): making mypy happy by using repo_ids and roots for merge operation
* fix(path): fixing path related issues
* fix(repo_id): fixing issues related to repo_id
* chore(doctrings): updating docstrings + fix typo
* chore(clean): cleaning code
* fix(split new_repo_id): reverting new_repo_id addition for split operation
* docs(dosctrings): completing docstrings
* fix(repo_ids/roots): improving checks for repo_ids/roots lengths
* fix(repo_ids): making repo_ids optional in MergeConfig but raise if not given
* fix(docstrings): fixing docstrings for split operation
* fix(hints): updating get_output_path hints to accept paths as strings too
* fix(y/N prompts): removing y/N prompts in lerobot_edit_dataset
* fix(merge repo_id): fixing merge operation to use new_repo_id instead of repo_id
* fix(typo): fixing typo in doctrings
* fix(frame_index): making rerun's "frame_index" timeline compatible with behaviour1k datasets
* fix(segfault risk): removing segfault risk by calling batch["index"] in the dataloader loop
Pepijn