GR00T training was dataloader-bound (0->100->0 GPU-utilization sawtooth).
GrootN17VLMEncodeStep ran the Qwen3-VL image processor per frame on PIL images
on the single CPU main-loop thread, and that cost is timed inside dataloading_s
(preprocessor(batch) runs in the main process, not the dataloader workers), so
adding workers cannot hide it.
- Feed the torchvision-backed Qwen3-VL processor (C,H,W) uint8 tensors instead
of a per-frame Image.fromarray PIL roundtrip, and run resize/normalize/patchify
on config.device (GPU) when available. Bit-identical on CPU when no resize is
configured; with a resize only the PIL->torchvision bicubic backend differs
(<2/255 per pixel). The use_albumentations path stays PIL/cv2; reload on a box
without the saved device falls back to CPU.
- Default image_target_size/crop to the N1.7 backbone's training geometry
(256x256 / 230x230) when a checkpoint ships no image sizing (checkpoint_assets
is None, e.g. finetuning nvidia/GR00T-N1.7-3B via repo-id with a new
embodiment). Previously image_target_size=None disabled the resize, so
full-resolution frames were patchified into ~4.7x more vision tokens than the
model was trained on -- inflating dataloading_s (patchify) and update_s (VLM
sequence) and skewing the input distribution. Checkpoints that pin their own
sizing are honored; the default constants are shared with GR00T_N1_7_DEFAULTS.
Net: preprocessing leaves the CPU critical path and the VLM sees the resolution
it was trained on -- faster training/inference and a correct train/serve
distribution. Affects inference too (shared preprocessor); existing checkpoints
still load (backward compatible) but must be retrained to gain the benefits.
The test loaded MODEL_PATH='aractingi/bimanual-handover-groot-10k', an N1.5
checkpoint (config base_model_path=nvidia/GR00T-N1.5-3B, no model_version). On
load, model_version defaults to n1.7 while the base path infers n1.5, so the
version-consistency guard in GrootConfig.__post_init__ raised ValueError and both
test_lerobot_groot_inference and test_lerobot_groot_forward_pass failed. N1.5 is no
longer a supported model_version.
Adopt the test for N1.7:
- MODEL_PATH -> nvidia/GR00T-N1.7-3B (root-level sharded safetensors; loads via
GrootPolicy.from_pretrained as a base N1.7 model).
- Embodiment tag 'gr1' (N1.5) -> 'gr1_unified' (valid N1.7 tag from the checkpoint
embodiment_id.json), via a single EMBODIMENT_TAG constant.
- DUMMY_ACTION_HORIZON 16 -> 40 to match N1.7's native action-chunk size.
- Docstrings/labels updated to 'GR00T N1.7'.
Both tests run and pass on CUDA; full tests/policies/groot/ suite is
73 passed / 0 failed / 0 skipped.
Mirror the tests/policies/pi0_pi05/utils convention: move dump_original_n1_7.py into
a tests/policies/groot/utils/ package (with __init__.py) and update all path
references in the test docstring/skip-message and the policy README.
- Rename test_groot_n1_7_vs_original.py -> test_groot_vs_original.py
- Make the test self-contained: producer script (dump_original_n1_7.py) now lives
next to the test; default artifact dir is repo-relative
(tests/policies/groot/artifacts/), overridable via GROOT_N1_7_PARITY_DIR. The
test only reads artifacts and skips if absent -- it never creates external dirs.
- Heavy .npz artifacts (~6-9MB each) are gitignored and regenerated by the producer;
never committed.
- Drop the verbose 'MULTIPLE EMBODIMENTS' docstring block (kept a one-line note).
- Document the parity procedure in the groot policy README (docs/source/policy_groot_README.md).
- Rename test fn test_groot_n1_7_get_action_parity -> test_groot_get_action_parity.
9/9 embodiments still pass (max|diff| < 3e-6, fp32 eps).
Generalize the original-vs-LeRobot N1.7 output-parity test from a single
libero_sim case to every embodiment tag in the checkpoint (libero_sim, oxe_droid,
real_g1, the real_r1_pro_sharpa family, and the xdof family). Inputs are built
generically from checkpoint metadata; the test discovers per-tag .npz artifacts
and runs one parametrized case each, loading the LeRobot model once via a fixture.
All 9 embodiments match the original to fp32 epsilon (max|diff| < 3e-6), confirming
the integration is correct across the model's full embodiment space and not overfit
to libero_sim.
Verifies the LeRobot GR00T N1.7 integration produces equivalent raw
action_pred to NVIDIA Isaac-GR00T for the same checkpoint, inputs, seed,
precision (fp32) and attention kernel (SDPA): max|diff|=8.9e-7 on the
libero_sim embodiment (GR00T-N1.7-LIBERO/libero_10).
The two impls pin incompatible transformers majors (orig 4.57.3 vs
LeRobot 5.x) and cannot share a process, so the original outputs + exact
collated inputs are produced out-of-process and loaded from an .npz. The
test skips on CI / when the checkpoint or artifact are absent.
* feat(depth): add depth quantization helpers and tests
* feat(video): add ffv1 to supported codecs
* feat(depth): persist depth metadata
* feat(depth): extend quantization tools to better fit the encoding/decoding pipeline
* feat(depth): plumb DepthEncoderConfig through LeRobotDataset and DatasetWriter
* feat(depth): wire StreamingVideoEncoder + writer to depth encoder
* feat(depth): wire DatasetReader to decode_depth_frames
* feat(cameras/realsense): expose async depth in metric meters
* feat(features): route 2D camera shapes to observation.depth.<key>
* feat(robots/so_follower): emit + populate depth keys when use_depth
* feat(record): plumb DepthEncoderConfig through lerobot-record
* feat(viz): render depth observations as rr.DepthImage in Viridis
* feat(depth maps writer): adding support for raw depth maps recording with image writer
* chore(format): format code
* feat(depth shape): ensuring depth maps shape is always including the channel
* feat(is_depth): simplifying is_depth nested name + legacy support
* fix(stop_event): fixing stop_event race condition in camera classes
* fix(plumbing): fixing missing parts in the depth maps pipeline
* chore(typos): fixing typos
* test(fix): fixing exisiting tests to still work with latest features
* tests(depth): adding new tests for depth integration validation
* feat(pix_fmt channels): use PyAv to check get pixel formats number of channels
* feat(refactor): refactor DepthEncoderConfig quantization pipeline, so that the methods do not live in the config class. Add pixel format - channels validation.Move the default pixel format for depth in the config file.
* fix(pre-commit): fixing mutable defautl value
* fix(info): fixing info metadata update when is_depth_map was set
* tests(typos): fixing typos in tests
* fix(realsense): fixing typo in realsense serial number
* fix(normalization): restricting 255 normalization to non depth/uint8 images only
* fix(typo): fixing typo
* fix(TIFF): add missing quantization and cleanup for TIFF files
* feat(batched dequantization): optimizing dequantize_depth for torch based batched dequantization
* feat(tools): adding depth support in LeRobotDataset edition tools
* test(aggregate): extending aggregation tests to depth frames
* test(cleaning): cleaning up tests
* fix(from_video_info): fixing early validation issue in from_video_info
* fix(typo): fixing typo
* fix(is_depth): adding missing doctrings and is_depth arguments in video decoding functions
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* fix(depth units): fixing depth units output for the realsense cameras
* feat(output unit): adding support for output unit specification at dataset reading/training time
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* test(depth): cleaning up depth tests
* test(depth encoding): updating and cleaning video/depth encoding tests
* chore(format): formatting code
* docs(depth): improving depth maps docs
* test(fix): fixing depth tests
* test(dataset tools): adding missing tests for new dataset edition tools features
* chore(format): formatting code
* fix(pyav check): fixing PyAV option validation for integer codec options by normalizing
numeric values before calling `is_integer()`
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* docs(mermaid): fixing mermaid diagram
* fix(rebase): rebase follow up corrections
* feat(dataset tools): adding missing docstrings and features for depth fill support in dataset edition tools
* docs(docstring): updating docstrings
* docs(dataset tools): updating docs
* fix(save images): fixing image saving in dataset tools
* fix(update video info): fixing update video info logic to match the recording and editing use cases
* test(reencode): fixing reencoding monkeypatch
* fix(review): add Claude review
* chore(format): format code
* fix(update video info): ditching the differentiated approahces for video info update - video info are always updated unless for preserved keys.
* chore(rebase): fixing rebase merge conflicts
* test(visualization): fixing visualization tests
* feat(docstrings): adding explicit docstring for encoding parameters. Docstrigns will now show up as description in the CLI --help.
* feat(mm as default): adding a global DEFAULT_DEPTH_UNIT variable setting mm as default depth unit
* fix(RGB <-> camera): renaming camera_encoder to rgb_encoder for clarity
* chore(TODO): removing deprecated TODO
* doc(write_u16_plane): improving docstrings for write_u16_plane
* feat(units): adding constants for depth frames units (m and mm)
* fix(spam): replacing spamming warning but a debug log
* feat(leagcy metadata): adding automatic metadata update for legacy 'video.is_depth_map' feature
* fix(copy&reindex): fixing metadat reshaping for single channel frames
* fix(ImageNet): excluding dpeth frames from ImageNet stats
* fix(PyAV container seek): fixing initial PyAV container seek to be robust againsy codec choice
* feat(lerobot-dataset-viz): adding support for depth in lerobot-dataset-viz
* fix(compress): removing rerun compression for DepthImages
* fix(signle channel squeeze): fixing single channel squeezing
* chore(format): format code
* fix(streaming): adding support for dequantization in streaming_dataset.py
* refactor(read depth): factorizing depth reading methods for realsense camera and adding support for depth-only usage
* chore(renaming): fixing missed RGBEncoderConfig renamings
* docs(renaming): reflecting renamings in a clearer way in the docs
* chore(annotation): excluding depth from the annotation pipeline
* feat(robots): adding depth support in compatible follower robots
* feat(LeSadKiwi): excluding LeKiwi from depth support (for now)
* chore(fail): removing misplaced file
* chore(fail): removing misplaced file
* fix(remove ffv1): removing ffv1 as it does not support MP4
* docs(cheat sheet): adding depth and video encoding to the cheat sheet
* fix(lossless): tuning depth encoding parameters for lossless depth storage
* test(fix): fixing failing tests
* depth(ZMQ): excluding ZMQ from depth support
* Revert "depth(ZMQ): excluding ZMQ from depth support"
This reverts commit b95cf4e4c2.
* fix(image transforms): excluding depth frames from images transforms
* fix(typo): typo
* fix(stats): fixing stats computation for depth frames
* fix(TIFF vs. pytorch): adding an extra uint16 to float32 conversion for depth maps stored as raw TIFF images
* fix(typos): fixing typos
* test(dtype): fixing stats computation typing tests
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi Ai <wsai@stanford.edu>
* refactor(training): rename eval_freq to env_eval_freq
- Rename eval_freq to env_eval_freq to distinguish sim environment evaluation from offline loss evaluation.
* feat(training): add inline offline validation with train/eval split
- Add eval_split config for balanced per-task holdout
- Add eval_steps for periodic inline eval loss computation
- Add max_eval_samples to cap eval cost
* fix(datasets): remap absolute indices in __getitem__ for filtered datasets
* fix(train): vectorize eval subset selection for max_eval_samples
* fix(datasets): Move the remapping into EpisodeAwareSampler via absolute_to_relative_idx
* fix(validation): add eval_split range check and eval_steps warning
Validate eval_split is in [0.0, 1.0) to prevent garbage splits from
out-of-range values. Raise when eval_steps > 0 but eval_split is 0.0
since no offline eval will run.
* fix(train): prepare eval dataloader with accelerator for multi-GPU
Prepare eval_dataloader through accelerator.prepare() so eval data is
sharded across ranks instead of duplicated. Reduce eval_loss across
ranks with mean reduction for consistent logging.
* fix(test): rename eval_freq to env_eval_freq for multi-GPU training
Align the MolmoAct2 implementation with lerobot codebase conventions:
- Rename hf_model/ to molmoact2_hf_model/
- Slim config: move all I/O and runtime logic to modeling
- Remove blanket from 8 vendored files, fix 66 lint issues
- Deduplicate _hf_token() and _resolve_checkpoint_location()
- Make huggingface_hub imports lazy
- Remove custom MolmoAct2CosineDecayWithWarmupSchedulerConfig, use base class
- Extract 13 static/classmethods from MolmoAct2Policy to free functions
- Replace print() with logger in vendored action_tokenizer
- Add module docstrings, class docstring, and key method docstrings
- Add module-level loggers to modeling and processor
- Fix docs: pip to uv install, deduplicate README symlink
- Remove shebangs from all files
* feat(train): FSDP checkpoint saving
* adding docs for FSDP
* adding a test for the fsdp checkpoint path
* cleanup
* fixing final upload to hub
* refactored initial implementation to use torch fsdp api and adding new tests
* fix(datasets): enforce one parquet row group per episode in v3 data writes
LeRobot v3 data shards must hold exactly one row group per episode so a
reader can fetch episode i with pq.ParquetFile(path).read_row_group(i)
(a byte-range read) instead of loading the whole shard. The recording
writer already does this (one write_table per episode); the aggregate
and lerobot-annotate re-write paths instead concatenated many episodes
and wrote them in one shot, collapsing the file to a single row group.
- io_utils: add write_table_one_row_group_per_episode (one ParquetWriter,
one write_table per episode — same pattern as the recording writer);
to_parquet_with_hf_images embeds images then writes per-episode row
groups; to_parquet_one_row_group_per_episode wraps it for plain frames
- aggregate: route non-image data writes through the per-episode writer;
leave the episodes-metadata parquet untouched (already one row/episode)
- annotate: rewrite shards via the per-episode writer instead of a single
bulk pq.write_table
- tests: invariant coverage through the aggregate (image + video) and
annotate paths
No change to on-disk schema, paths, naming, rollover thresholds, or
compression. Readers stay backward-compatible (old collapsed files load).
* Update src/lerobot/datasets/io_utils.py
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* Update src/lerobot/datasets/io_utils.py
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* fix(datasets): correct indentation and add strict= in row-group helper
The web-edited numpy version of write_table_one_row_group_per_episode had an
over-indented line (IndentationError, breaking pre-commit + test collection)
and a zip() without strict=. Fix both; behaviour unchanged.
---------
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
* fix(images/videos): fixing aggregate_pipeline_dataset_features to avoid unwanted images features deletion when videos are not used
* fix(docstrings): improving docstrings
Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>
---------
Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>
* chore(robots): homogenize bi setups
* feat(robots): split openarm mini into single and bi
* refactor(robots): mixin for bi classes
* docs: update docs
* feat(edit-dataset): add `concatenate_videos` opt-out to merge
When merging datasets, source mp4s are concatenated into shards capped at
`video_files_size_in_mb` (default 200 MB). This is great for dataloader
throughput but destroys per-episode (or per-source) video boundaries,
which is undesirable when you want to inspect, ship, or reuse the
individual mp4s.
Add a `concatenate_videos: bool = True` knob plumbed through
`MergeConfig` → `merge_datasets` → `aggregate_datasets` → `aggregate_videos`.
When False, each source mp4 is copied 1:1 to its own destination mp4 with
no re-muxing, so the merge preserves source video boundaries.
Usage:
lerobot-edit-dataset \
--new_repo_id user/merged \
--operation.type=merge \
--operation.repo_ids "['user/a', 'user/b']" \
--operation.concatenate_videos=false
Defaults are unchanged; the dataloader path is unaffected because the
`episodes.parquet` `from_timestamp`/`to_timestamp` index keeps working
regardless of whether each mp4 holds one or many episodes.
* feat(edit-dataset): extend concatenate opt-out to data files
Following review, add a concatenate_data flag mirroring concatenate_videos,
threaded through MergeConfig, merge_datasets, aggregate_datasets, aggregate_data
and append_or_create_parquet_file. Metadata index files still always concatenate.
Also trim the verbose docstrings and comments since the names are
self-explanatory, and extend the existing merge test to cover data files.
Steerable annotation pipeline (lerobot-annotate) that populates the language_persistent and language_events columns introduced in PR 1 (#3467) directly into data/chunk-*/file-*.parquet.
This is PR 2 of the three-PR plan:
PR 1 (Add extensive language support #3467): schema + DSL + rendering, base of this PR
PR 2 (this PR): annotation pipeline writing into PR 1's columns
PR 3: model with language prediction and runtime
A VLM (Qwen-VL family, served on vLLM) watches each episode's video and emits grounded language annotations: subtasks, plans, memory, task rephrasings, interjections + speech, and per-camera VQA. The pipeline is built for production annotation at scale — single-camera grounding, embedded-frame inputs, a describe-then-segment grounding flow, and a deterministic full-episode coverage guarantee — informed by Scale's dense-captioning findings (representation > sampling, rules > reasoning, model capacity is the biggest lever, two-pass systems compound errors)
* fix(datasets): expose a generator on EpisodeAwareSampler for distributed shuffle sync
In distributed training, accelerate can only synchronize the shuffle
permutation across ranks when the sampler exposes a generator attribute.
EpisodeAwareSampler shuffled via the global torch RNG, so disjoint batch
shards relied on every rank's global CPU RNG staying in lockstep forever;
any rank-asymmetric RNG consumption (e.g. eval rollouts on the main
process only) silently desynced the permutations and ranks trained on
overlapping/missing samples.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(train): seed sampler generator and gate dataset download per node
- Pass a generator seeded with cfg.seed to EpisodeAwareSampler so
accelerator.prepare registers it as the synchronized RNG and the
shuffle order is reproducible.
- Gate the initial make_dataset call on is_local_main_process instead of
is_main_process: the global main process only exists on node 0, so on
every other node all local ranks were downloading the dataset and
building the Arrow cache concurrently.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat(datasets): add DeterministicEpisodeAwareSampler with O(1) memory and sample-exact resume
Add a sampler that never materializes frame indices: it stores only
per-episode boundaries (numpy, a few bytes per episode) and maps logical
positions to frame indices on the fly with searchsorted. Shuffling uses a
seeded Feistel permutation over [0, num_frames) (cycle-walking to the
exact domain), so the data order is a pure function of (seed, epoch):
- no RNG state to synchronize across distributed ranks,
- constant memory and zero epoch-boundary cost at any dataset size,
- O(1) seek to any position, enabling sample-exact resume.
Opt in with --deterministic_sampler=true. On resume, lerobot-train maps
the checkpointed step back to (epoch, start_index) via
compute_sampler_state and continues at the exact sample where the run
left off (up to accelerate's even_batches padding at epoch boundaries).
The shuffle is pseudo-random rather than a true uniform permutation, the
standard trade-off in large-scale training loaders.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* refactor(datasets): fold deterministic mode into EpisodeAwareSampler
Instead of a parallel DeterministicEpisodeAwareSampler class, extend the
existing EpisodeAwareSampler with a deterministic=True mode (seeded
Feistel permutation, epoch auto-advance, state_dict/load_state_dict).
The default mode is behavior-identical: same torch.randperm consumption
and the same generator contract accelerate synchronizes; the O(N) Python
index list is replaced by O(num_episodes) boundary arrays in both modes,
with `indices` kept as a back-compat property. Passing a generator
together with deterministic=True is rejected, and the state/seek methods
raise outside deterministic mode.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat(train): enable deterministic_sampler by default
Deterministic data order (sample-exact resume, no cross-rank RNG sync,
O(1) sampler memory) is now the default for map-style training; set
deterministic_sampler=false to restore the legacy RNG-based shuffle.
Streaming datasets ignore the flag (the sampler path only applies to
map-style datasets), replacing the previous hard validation error so
streaming configs keep working with the new default.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat(datasets): default EpisodeAwareSampler to deterministic mode and trim comments
deterministic=True is now the class default as well as the training
default; the legacy RNG path requires an explicit deterministic=False
(the train script's non-deterministic branch passes it). Docstrings and
inline comments slimmed down across the changed files.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* test(sampler): drain resumed trillion-frame sampler via iter() to avoid list() prealloc
list(sampler) calls PyObject_LengthHint -> __len__ (the full 10**12 epoch length) and
preallocates that many slots before iterating, OOMing even though the resumed epoch only
yields 3 frames. Collect through the iterator (no length hint) so the test exercises the
real O(1) seek/drain instead of CPython's list growth heuristic.
* fix(datasets): guard Feistel cycle-walking loop against non-convergence
Replace the unbounded while True in EpisodeAwareSampler._permute with a
bounded for loop capped at _MAX_CYCLE_WALK_STEPS (100) and raise
RuntimeError if the cycle-walk fails to land in [0, num_frames). The
loop is expected to converge in <4 steps on the chosen power-of-two
domain, so the bound is a safety net that should never trip in practice
but prevents a pathological infinite loop.
https://claude.ai/code/session_01HQ15tFrBsHYScjGWosEv22
* fix(datasets): make deterministic-sampler resume robust to world-size changes
compute_sampler_state mapped a checkpointed step back to (epoch, start_index)
using the *current* num_processes, but the number of sampler positions a step
consumes scales with the world size that produced it. Resuming on a different
GPU count therefore landed on the wrong epoch/offset, silently re-seeing or
skipping data.
Record num_processes in training_step.json at checkpoint time and feed the
checkpoint's value into compute_sampler_state on resume, so the data order
resumes at the right position regardless of the new world size. Warn when the
world size changed (the global offset is correct, but per-rank sample-exactness
needs the same topology). Old checkpoints without the field fall back to the
current world size.
Also document compute_sampler_state's assumptions explicitly: num_processes /
batch_size must match the checkpointing run, and accelerate's even_batches=True
padding is mirrored by the ceil(... / num_processes) term.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
* style: apply ruff-format to lerobot_train.py
Collapse the compute_sampler_state(...) call onto one line so the
ruff-format pre-commit hook passes (fixes the failing CI check).
Co-authored-by: Cursor <cursoragent@cursor.com>
* refactor(datasets): use seeded torch.randperm instead of Feistel in EpisodeAwareSampler
Drop the Feistel permutation (and its SplitMix64 hash / cycle-walking) in favor of a
torch.randperm seeded from (seed, epoch). The deterministic mode keeps its key properties
- data order is a pure function of (seed, epoch), so it reproduces on every rank with no
global-RNG synchronization, and
- state_dict / load_state_dict still resume sample-exactly, now by regenerating the epoch's
permutation and slicing from the saved offset.
Construction stays O(num_episodes) (only episode boundaries are stored, never a per-frame
index list). The trade-off vs Feistel: the per-epoch shuffle is again O(num_frames) memory
(the randperm tensor) and no longer O(1)-seekable, in exchange for ~30 fewer LOC and a truly
uniform shuffle. Tests updated: the trillion-frame O(1) test is replaced with a
boundary-storage check and a scale resume-exactness test.
Co-authored-by: Cursor <cursoragent@cursor.com>
* refactor(datasets): make EpisodeAwareSampler always deterministic
With Feistel gone, deterministic and legacy modes were both just torch.randperm and the
deterministic path strictly dominated (reproducible across ranks via the (seed, epoch) seed,
no accelerate generator sync, resumable). Collapse to a single path and drop the redundant
flag:
- remove the `deterministic` and `generator` constructor args, `_iter_default`, and
`_require_deterministic`; `set_epoch` / `state_dict` / `load_state_dict` are now unconditional
- remove the `deterministic_sampler` train config field and the legacy generator branch in
lerobot_train.py (non-streaming map datasets always use the sampler)
- drop the now-obsolete generator/legacy tests
Note: removes the `generator` kwarg from EpisodeAwareSampler (back-compat break vs main); the
order is now a pure function of (seed, epoch), so no cross-rank RNG sync is needed.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(datasets): address sampler review (batch_size resume guard + docs)
- Record batch_size in training_step.json alongside num_processes and feed
the checkpoint's value into compute_sampler_state on resume; warn when it
differs (per-rank sample-exactness needs the same batch size).
- Document the set_epoch vs __iter__ auto-advance coupling on EpisodeAwareSampler
(callers should rely on exactly one mechanism per run).
- Note the broadened (reproducibility-breaking) sampler guard and the no-generator
distributed sharding correctness in lerobot_train.py.
- Add load_training_batch_size + parallel tests.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(train): download dataset once on the global main process
Gate the training dataset download on the global is_main_process (download once to the
shared dataset root, barrier, then every other rank reads the already-populated copy)
instead of per-node is_local_main_process. LeRobotDataset skips its snapshot_download
when try_load() succeeds, so no rank re-downloads. Assumes the dataset root / HF cache is
on storage shared across nodes.
Co-authored-by: Cursor <cursoragent@cursor.com>
* chore(datasets): trim sampler comment and drop duplicate tests
Remove the verbose dataloader-guard comment and the two EpisodeAwareSampler tests
that duplicated existing validation/warning coverage (no coverage loss).
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(datasets): expose a generator on EpisodeAwareSampler for distributed shuffle sync
In distributed training, accelerate can only synchronize the shuffle
permutation across ranks when the sampler exposes a generator attribute.
EpisodeAwareSampler shuffled via the global torch RNG, so disjoint batch
shards relied on every rank's global CPU RNG staying in lockstep forever;
any rank-asymmetric RNG consumption (e.g. eval rollouts on the main
process only) silently desynced the permutations and ranks trained on
overlapping/missing samples.
* fix(train): seed sampler generator and gate dataset download per node
- Pass a generator seeded with cfg.seed to EpisodeAwareSampler so
accelerator.prepare registers it as the synchronized RNG and the
shuffle order is reproducible.
- Gate the initial make_dataset call on is_local_main_process instead of
is_main_process: the global main process only exists on node 0, so on
every other node all local ranks were downloading the dataset and
building the Arrow cache concurrently.
* feat(processor): add in-memory pipeline serialization
Expose processor pipeline config and tensor state without requiring temporary files, so processors can be transported, compared, or hashed directly in memory.
* feat(processor): enhance DataProcessorPipeline with registry support
- Added a new RegisteredLazyTensorStateStep for registry-based serialization tests.
- Improved state filename handling in _get_state_filename method.
- Refactored validation logic in _validate_loaded_config to simplify parameter types.
- Updated tests to verify registry step functionality and ensure correct state loading.
* refactor(processor): update state handling in DataProcessorPipeline
- Introduced a new static method _get_state_key to derive in-memory state keys from serialized filenames.
- Updated state_dict and load_state_dict methods to use suffixless state keys instead of filenames.
- Adjusted related tests to reflect changes in state key handling, ensuring consistency in state management
* fix(processor): update loaded_config argument description in DataProcessorPipeline
- Clarified the documentation for the loaded_config parameter to indicate that it may be a non-dictionary value, enhancing understanding for future developers.
* first commit
* feat(policies): add VLA-JEPA
* feat(policies): add VLA-JEPA
* support vla_jepa
* (feat)policies: add VLA-JEPA
* linting
* adding deps to pyproject.toml
* updating uv lock
* adding guards to avoid needing transformers and diffusers for type checking and basic tests
* fixing action and state dim
* fix warnings with qwen processor kwargs
* fixing wm_loss not propagating
* adjusting obs steps, tublets size to match original implementation
* some more fixes to be closer to the original implem
* adding more tests to ensure good coverage
* align VLA-JEPA architecture with original checkpoint
- Remove stale `action_num_heads` / `action_attention_head_dim` config fields;
DiT head dimensions are now always derived from the preset (DiT-B/L/test).
- Add `num_target_vision_tokens` and `action_max_seq_len` config fields required
by the action head's future-token embedding and positional embedding tables.
- Fix default `qwen_model_name` to 2B (matches all released checkpoints).
- Rename `ActionEncoder` attrs w1/w2/w3 → layer1/layer2/layer3 to match
checkpoint key names; replace `nn.Sequential` decoder/state-encoder with
`_MLP2` (layer1/layer2 naming).
- Fix `VLAJEPAActionHead` to size ActionEncoder and StateEncoder at `inner_dim`
(DiT input width) rather than `action_hidden_size` (DiT output width).
- Rename `DiT.blocks` → `transformer_blocks` and `attn` → `attn1` to match
checkpoint; add alternating cross/self attention (even blocks cross-attend to
Qwen context, odd blocks self-attend).
- Add `DiT-test` preset for unit tests.
- Rewrite `ActionConditionedVideoPredictor` with explicit ViT-style blocks
(`_PredictorBlock` with fused qkv) to match checkpoint structure; rename
`encoder`/`norm`/`proj` → `predictor_blocks`/`predictor_norm`/`predictor_proj`.
* propagate action_is_pad masking through VLA-JEPA policy pipeline
Pass the `action_is_pad` tensor from the batch through to the action head
so padded timesteps are excluded from the flow-matching loss.
* update VLA-JEPA tests for arch changes and action_is_pad
- Switch conftest to use `action_model_type="DiT-test"` now that
`action_num_heads` / `action_attention_head_dim` have been removed.
- Add action_head tests covering fully-padded loss (zero) and equivalence
of action_is_pad=None vs all-zeros mask.
- Remove obsolete `test_native_to_lerobot_wm_only` test.
* add VLA-JEPA documentation
Covers architecture overview, pretrained checkpoints, config reference,
training/eval commands for LIBERO-10, and guidance on fine-tuning for
single-camera datasets.
* add one-shot script to convert ginwind/VLA-JEPA checkpoints to safetensors (will remove once migrated)
* make default params more aligned with paper and pretrained models
- adding possibility of freezing qwen backbone and world model
- added tests for weight loading
* trying out to re-init the action head to avoid pretraining dimension mismatch
* allow different state dim and action dim
* removing missleading future_action_window_size to just use chunk_size
* lots of changes to make existing weights work, need to massively refactor the pre and post processing
* refactoring into using pre and post processor
* pre-commit cleanup
* fixing doc defaults args
Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net>
* adressing dtype zeros issue
* adding guard for diffusers
* fixing training and exal examples
* trying to close success rate gap
* fix qwen norm layer output libero eval is now as expected
* adding instructions for different embodiement + fixing some tests
* smol fix to avoid having default CPU device when training
* fixing misconception about multiview / singleview handling
* removing conversion script
* adding licences
* adding .mdx docs and shortening polivy_vla_jepa_README.md
* removing useless pre-processor
* cleanup
* removing swish in favor of silu
* adding configuration gripper index and threshold
* fixing simlink
---------
Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net>
Co-authored-by: ginwind <ginwind@mail.ustc.edu.cn>
* feat(rewards): add TOPReward reward model
* refactor(rewards): clean up TOPReward processor/model
* fix(rewards/topreward): add missing input keys mm_token_type_ids
* fix(rewards/topreward): fix pyproject extra typo and simplify processor (#3653)
Add lerobot[topreward] extra to all in
pyproject.toml, drop the redundant labels arg in scoring, and
collapse the dead-branch shape check in the encoder processor.
* optmize topreward input processing (#3660)
---------
Co-authored-by: Cole <91766445+jcoleharrison@users.noreply.github.com>
Co-authored-by: Haoming Song <haomingsong24@gmail.com>
PR #3145 added YAML support for policy.path but left two bugs:
1. extract_path_fields_from_config only deleted config_data[field] when
no sibling overrides existed. With siblings, the dict stayed in place
and draccus crashed decoding it as PreTrainedConfig (no 'type' key).
Sibling overrides go into _config_yaml_overrides and are applied later
by from_pretrained(), so the field can always be removed.
2. wrap() updated config_path_cli to the cleaned temp file path but
never propagated it to the draccus.parse fallback branch. cli_args
still contained --config_path=<original>, so draccus read the
original YAML with path: still present.
Tests passed because they (a) called extract_path_fields_from_config
directly and (b) included type: alongside path: in the YAML, sidestepping
both bugs.
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* chore(gr00t): sync with #3606 for fixing gr00t config crash
* fix(pi0&pi05): fix graph break caused by deepcopy of past_key_values in sample_actions
* fix(pi0&pi05): fix frequent recompile caused by compute_layer_complete
* feat(test): add compile test and benchamrk for pi0 and pi05
* feat(test): add comprehensive testing for pi0 and pi05. Including processor, forward, sample action, etc.
VideoDecoderCache used an unbounded dict keyed on absolute path, with no
eviction in the standard LeRobotDataset path. With shuffled iteration over
datasets that have many distinct mp4 files, every DataLoader worker
accumulated one cached (VideoDecoder, fsspec file handle) pair per distinct
path it had ever touched. Per-entry cost is ~3-5 MB of host RAM plus one
open FD; at ~8 k entries this is roughly 30 GB per worker.
This was hit in the wild during a SmolVLA training run on a 4,195-episode
SO-101 dataset (8,390 mp4s, two cameras per episode). dmesg showed
anon-rss climbing to 34.9 GB on a single pt_data_worker before the OOM
killer fired ~30 min into training; with --num_workers=8 the per-worker
peak halved to 17.9 GB, which is the expected inverse-scaling signature
when the leak is per-decode and the workload is split across workers. The
working workaround on the affected platform was --dataset.video_backend=pyav,
because the pyav path opens/closes per call and never touches this cache.
Switch the backing store to an OrderedDict and evict LRU entries when the
cap is reached, closing the evicted file handle inside the lock so we do
not leak FDs either. Default cap is DEFAULT_DECODER_CACHE_SIZE = 100,
overridable via LEROBOT_VIDEO_DECODER_CACHE_SIZE or by passing max_size=
to the constructor; max_size=None restores the legacy unbounded behaviour
for callers that need it.
Validation on the original failing workload (decode_video_frames_torchcodec
called over real mp4s from the affected SO-101 dataset):
unbounded: 300 files -> +1087 MB host RSS, cache=300, still climbing
cap=50: 500 files -> +266 MB host RSS, cache=50, stable
cap=50: 2000 calls -> +312 MB host RSS, cache=50, stable
cap=100: 1000 calls -> +470 MB host RSS, cache=100, stable
Three independent seeded runs at cap=50 agreed to within 1% (263 / 266 /
265 MB delta), and the 2000-call multi-pass run shows RSS plateaus after
the cap is reached instead of drifting.
Tests in tests/datasets/test_video_decoder_cache.py cover:
default-is-bounded, size cap, LRU ordering, FD close on eviction, FD close
on clear(), cache-hit invariance, max_size=None fallback, and env-var
override. No regressions in test_video_encoding.py, test_streaming.py, or
test_dataset_reader.py (73 prior tests still pass alongside the 8 new ones).
* feat(utility): adding video re-encode utility
* feat(edit): adding a new lerobot-edit-dataset tool to re-encode all the videos of a dataset
* chore(format): formatting code
* chore(review): fix Claude reviews
* test(reencode dataset): adding missing test for reencode dataset
* Add extensive language support
* Address review: split persistent/event schemas, drop event timestamps
- recipe.py: derive _VALID_ROLES/_VALID_STREAMS from MessageRole/MessageStream Literals
- dataset_metadata.py: keep CODEBASE_VERSION at v3.0
- language.py: remove RESERVED_STYLES; split arrow/feature schemas into
persistent (with timestamp) and event (without timestamp); add docstrings
- language_render.py: events use frame-row timestamp implicitly; no
per-event timestamp filtering or sorting
- converters.py: drop unused subtask_key passthrough
- add docstrings to new public APIs (recipe, render_messages_processor, collate)
- update tests for split schemas; revert uv.lock
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* Add docstrings to all new helpers; revert uv.lock
Covers private helpers in recipe.py, language.py, language_render.py,
and render_messages_processor.py. Also reverts uv.lock to main (it was
re-generated by `uv run` during local checks).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(language): add motion (persistent) and trace (event-only) styles
Promote the previously-reserved motion/trace styles to first-class core
styles. motion routes to language_persistent (it tracks robot state over
time); trace routes to language_events (single-moment annotations).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(language): per-camera tagging on view-dependent styles
Adds a nullable `camera` field to the language row struct (both persistent
and event variants) so view-dependent styles like `vqa` can carry which
`observation.images.*` view they were grounded against. Without this,
multi-camera datasets ended up with multiple `(vqa, role)` rows at the
same timestamp that the resolver could not disambiguate.
- `language.py`: add `camera` to PERSISTENT_ROW_FIELDS / EVENT_ROW_FIELDS,
to both Arrow struct types and the HF datasets feature mappings;
introduce VIEW_DEPENDENT_STYLES = {vqa, motion, trace} plus
`is_view_dependent_style` and `validate_camera_field` helpers (camera
required iff style is view-dependent).
- `language_render.py`: thread an optional `camera=` kwarg through every
resolver (`active_at`, `emitted_at`, `nth_prev`, `nth_next`) and through
`_matching_rows` / `_select_*`, so recipes can disambiguate per-camera
VQA with `emitted_at(t, style=vqa, role=assistant, camera=...)`.
Without a `camera` filter, multi-row matches keep raising the existing
ambiguity error — which is the desired behaviour on multi-camera data.
- `recipes/pi05_hirobot.yaml`: replace the single `ask_vqa` branch with
`ask_vqa_top` and `ask_vqa_wrist` per-camera sub-recipes (each carrying
the matching image block), keeping the original 0.20 budget and
documenting the customization point for datasets with different cameras.
- Tests: schema test asserts the new field order; new tests cover
`is_view_dependent_style`, `validate_camera_field` (both required and
forbidden directions), per-camera `emitted_at` filtering, and the
ambiguity error when two cameras emit `(vqa, assistant)` at the same
timestamp without a `camera=` filter. RenderMessagesStep + dataset
passthrough fixtures updated to include the new field.
- `docs/source/language_and_recipes.mdx`: document the `camera` field,
the per-camera resolver pattern, and the canonical recipe convention.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(language): drop motion from VIEW_DEPENDENT_STYLES
Motion primitives are described in robot-frame (joint / Cartesian) terms,
not pixel space, so they are camera-agnostic. Only `vqa` (event) and
`trace` (event, pixel-trajectory) are view-dependent.
The `camera` field stays on PERSISTENT_ROW_FIELDS for schema symmetry —
the validator, resolver, and HF feature mapping behave identically across
the two columns regardless of which styles populate `camera` today —
but persistent rows now always have `camera=None` in practice.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(language): task_aug style + automatic ${task} rephrasing rotation
Adds task-prompt diversity (Xiao 2022 / CAST) without touching
``meta/tasks.parquet`` or forcing recipes to opt in. The plan reserved
``task_aug`` as a future style; this lands it now.
- ``language.py``: add ``task_aug`` to ``CORE_STYLES`` and
``PERSISTENT_STYLES``. ``column_for_style("task_aug")`` returns
``language_persistent`` so PR 2 writers route it correctly.
- ``language_render.py``: ``_resolve_task`` now consults the persistent
slice for rows of ``style="task_aug", role="user"``. When any exist
it picks one deterministically by ``sample_idx`` (blake2b-keyed, not
Python's randomized hash) so an epoch sees every rephrasing of every
episode while the same sample still resolves identically across
reruns. Falls back to the canonical ``meta/tasks.parquet`` task when
no rephrasings are present, so existing datasets and unannotated runs
keep their behaviour. Explicit ``task=`` overrides still win.
- Tests: rephrasing coverage across samples, determinism on repeat
``sample_idx``, fallback when persistent has no ``task_aug`` rows,
and explicit override priority.
Recipes get this for free: any ``${task}`` placeholder rotates through
the available rephrasings. Recipes that want the literal canonical task
can override the binding.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(language): tool catalog in meta/info.json + LeRobotDatasetMetadata.tools
Stores OpenAI-style function schemas at ``meta/info.json["tools"]`` so
datasets can declare which tools are available (today: just ``say``;
tomorrow: per-dataset extensions). The ``DEFAULT_TOOLS`` constant
fills in for unannotated datasets so chat-template consumers don't
have to special-case anything.
Three pieces:
- ``language.py``: ``SAY_TOOL_SCHEMA`` and ``DEFAULT_TOOLS``
constants. Single source of truth — PR 2's writer and PR 3's
runtime tool registry will both import from here instead of
duplicating the dict.
- ``dataset_metadata.py``: ``LeRobotDatasetMetadata.tools`` property
reads ``info.json["tools"]`` and falls back to ``DEFAULT_TOOLS``.
Returns deep-copied dicts so callers can mutate the result safely.
- ``docs/source/tools.mdx``: spec page covering the catalog, per-row
invocations, and the three-step "how to add a new tool" workflow
(declare schema, implement, register). Linked from the docs
toctree under the Datasets section.
This lays the groundwork for PR 2's pipeline writing the catalog out
during annotation, and PR 3's ``src/lerobot/tools/`` package shipping
runnable implementations (one file per tool — first up:
``say.py`` wrapping Kyutai's pocket-tts).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* Apply ruff and prettier formatting after merge
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* refactor(language): unify resolver dispatch and prune redundant test scaffolding
* Drop the unused `events` kwarg from `active_at`/`nth_prev`/`nth_next`;
only `emitted_at` actually consults events. The dispatcher in
`_resolve_spec` now passes events conditionally.
* Replace the dual `_persistent_sort_key`/`_event_sort_key` pair with a
single `_row_sort_key` and drop the `sort_key` parameter from
`_select_one`. Event rows lack `timestamp` (it is implicit in the
frame) and now default to `0.0` for sort purposes — the
`(style, role)` tiebreaker is unchanged.
* Inline `_select_latest` into `active_at` (its only caller).
* Collapse `emitted_at`'s dual-branch into one `_select_one` call.
* Tighten `_validate_persistent_resolver` to a single
`column_for_style(style) != LANGUAGE_PERSISTENT` check.
* Parameterize `test_per_camera_blend_renders_both_views` over the two
cameras and factor the sub-recipe builder into `_vqa_subrecipe` so
the test no longer hand-rolls two near-identical recipe blocks.
Net -98 LOC; behavior, public resolver names, and test expectations
unchanged.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(language): always raise on ambiguous resolver matches
`_select_one` previously skipped its ambiguity check whenever any of
`role`/`tool_name`/`camera` was set, on the assumption that the caller
had already pinned down a unique row. That left a real ambiguity hole
for VQA: with two cameras emitting `(vqa, assistant)` at the same
frame, `emitted_at(..., role="assistant")` silently picked the first
sorted row instead of telling the recipe to add `camera=...`. The
existing `test_emitted_at_raises_on_ambiguous_per_camera_vqa` test
already encoded the desired behavior.
Tighten the check: any time `len(rows) > 1` we now raise with the
selectors echoed back, so users see exactly which fields they passed
and that more is needed to disambiguate.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore: fix CI — collapse short ValueError to one line, refresh uv.lock
* `ruff format` on CI (newer version) wants the short `camera=None`
ValueError on a single line.
* `uv.lock` was stale relative to `pyproject.toml`'s `datasets>=4.7.0`
pin (and picked up upstream `s390x` marker fixes for cuda packages).
CI runs `uv sync --locked` which rejected the divergence.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(language): keep base install green — drop processor re-export, gate dataset-extra tests
`lerobot.processor` re-exported `RenderMessagesStep` at the package
level, so importing anything from `lerobot.processor` pulled in
`lerobot.datasets.language` → `lerobot.datasets/__init__.py` →
`require_package("datasets")`, which fails in the Tier 1 base install
that intentionally omits the `[dataset]` extra. The chain bricked
collection for unrelated suites (`tests/policies/pi0_pi05/...`,
`tests/envs/...`, etc.).
* Stop re-exporting `RenderMessagesStep` from `lerobot.processor`. The
only consumer (the test) already imports from the submodule.
Document the deliberate omission in the module docstring.
* Add `pytest.importorskip("datasets", ...)` (and `pandas` where
needed) at the top of the four PR-added tests that exercise the
language stack:
- tests/datasets/test_language.py
- tests/datasets/test_language_render.py
- tests/processor/test_render_messages_processor.py
- tests/utils/test_collate.py
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(language): address review — tools accessor, motion docs, conditional collate
* **`meta.tools` actually reads `info.json["tools"]`.** `DatasetInfo`
had no `tools` field, so `from_dict` silently dropped the key (it
warned about unknown fields then discarded them) and the property
always returned `DEFAULT_TOOLS`. Added `tools: list[dict] | None`
to the dataclass; `to_dict()` drops it when unset so existing
datasets keep a clean `info.json`. Fixed the accessor to read
`self.info.tools` (the previous `.get(...)` would have raised
AttributeError on the dataclass anyway). Added regression tests:
fallback when absent, round-trip from disk, and round-trip
through `DatasetInfo.from_dict` / `to_dict`.
* **`motion` is not view-dependent — fix the docs.** The mdx claimed
rows of style `motion` must carry `camera`, but `VIEW_DEPENDENT_STYLES
= {"vqa", "trace"}` and the validator agrees: motion primitives are
joint/Cartesian-frame, not pixel-space. Updated both call-out
paragraphs in `language_and_recipes.mdx`.
* **Conditional `collate_fn` swap.** Added `meta.has_language_columns`
and gate the `lerobot_collate_fn` swap in `lerobot_train.py` on it,
so non-language datasets keep PyTorch's `default_collate`. Also
added a pass-through test in `test_collate.py` that asserts on a
plain tensor batch the custom collate matches `default_collate`
key-for-key, plus a test for the `None`-sample drop path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* review: dedupe regex, centralize column names, harden collate, more tests
* **#2 — dedupe `_PLACEHOLDER_RE`.** The same regex was compiled in
`recipe.py` and `language_render.py`. Promote to module-level
`PLACEHOLDER_RE` in `recipe.py` (its primary owner — declares
template syntax) and import from `language_render.py`.
* **#3 — centralize language column names.** `io_utils.py` had
hardcoded `{"language_persistent", "language_events"}` literals at
two sites. Replace with `LANGUAGE_COLUMNS` import so a future column
rename can't silently desync.
* **#4 — defensive collate preserved-keys.** `lerobot_collate_fn`
silently filtered language fields from samples that didn't have
them, which would hand downstream consumers a preserved list
shorter than the tensor batch. Now: if any sample carries a key,
every sample in the batch must carry it; otherwise raise a
`ValueError` so the upstream rendering bug surfaces at the boundary.
* **#5 — `_scalar` rejects non-singleton lists.** Previously a zero-
or multi-element list fell through and triggered confusing
`float([])` errors downstream. Now raises `ValueError` with the
actual length.
* **#6 — refactor `_extract_complementary_data`.** Replace 11 lines
of `key = {... if ... else {}}` plus an 11-line splat dict with a
single `_COMPLEMENTARY_KEYS` tuple iterated once.
* **#7 — document `EXTENDED_STYLES`.** Was an empty `set()` with no
comment. Add a docstring explaining it's an intentional extension
point: downstream modules append project-local styles before
`column_for_style` is called.
* **#9 — `tools.mdx` notes the runtime layer is future work.** The
page referenced `src/lerobot/tools/`, `registry.py`, and
`get_tools(meta)` — none exist in this PR. Added a callout at the
start of "How to add your own tool" plus a note on the
implementations paragraph.
* **#10 — tests for YAML round-trip, malformed rows, blend
validation.** `test_recipe.py` grew from 1 case to 12 covering:
blend-or-messages exclusivity, target-turn requirement, blend
emptiness, weight presence/positivity, nested-blend rejection,
`from_dict` with nested blends, `from_yaml` / `load_recipe`
agreement, top-level non-mapping rejection. Added a malformed-row
test for `_normalize_rows` that asserts non-dict entries raise
`TypeError`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* review: emitted_at uses 0.1s tolerance; MessageTurn requires stream at construction
* **Float tolerance in `emitted_at` for persistent styles.** The
``_timestamp(row) == t`` exact-equality check silently missed any
caller that derived ``t`` arithmetically (e.g. ``frame_idx / fps``)
even though the parquet timestamp would only differ by ULPs. Added
``EMITTED_AT_TOLERANCE_S = 0.1`` and check ``abs(...) <= tolerance``
instead, with a docstring explaining why exact equality wasn't
enough and why 0.1 s is safe at typical 30–100 Hz control rates.
Test asserts the new behavior at half-window (matches) and
double-window (no match) using the constant so it stays in sync.
* **`MessageTurn.stream` is required at construction.** It was typed
``MessageStream | None = None`` so YAML could omit ``stream:`` and
pass the dataclass invariant — but ``_validate_rendered`` rejected
``None`` streams later, surfacing the error at the first sample
instead of at recipe load. Now ``__post_init__`` raises
``ValueError`` if ``stream`` is ``None``, with the list of valid
streams in the message. The redundant late-stage check in
``_validate_rendered`` is replaced with a one-line comment that
cites the upstream invariant. Test pins the new construction-time
rejection.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs(tools): drop follow-up-PR references
Reword the two callouts in `tools.mdx` to describe the runtime layer
in present tense ("not part of the catalog layer shipped today",
"those modules don't yet exist in the tree") instead of pointing at a
specific follow-up PR. Keeps the doc honest about what works now
without coupling it to a particular release order.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* review: address CarolinePascal feedback
- language timestamps: float64 -> float32 to match LeRobotDataset frame
timestamps (Arrow struct + HF feature)
- dataset_metadata: hoist `.language` imports to module top — language.py
has no lerobot imports, so there is no circular-import risk
- dataset_metadata: add a `meta.tools` setter that persists the catalog to
info.json and reloads `meta.info`
- feature_utils: validate the `language` dtype instead of returning "" —
warn (non-fatal) when a non-empty value is written at record time
- centralize the scalar-unwrap helper as `lerobot.utils.utils.unwrap_scalar`,
shared by render_messages_processor and language_render
- docs: move `## Layer 2 — recipe anatomy` ahead of the resolver sections,
which describe recipe bindings rather than dataset layout
- language_render: note in EMITTED_AT_TOLERANCE_S that persistent rows change
on a human-action timescale, not the camera frame rate
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Andy Wrenn