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feat/codec-parameters
332 Commits
| Author | SHA1 | Message | Date | |
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 CarolinePascal
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a3c670b987 | chore(fromat): formatting code | ||
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CarolinePascal
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be1180b240 | test(artifacts): cleaning up artifacts for the video encoding tests | ||
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CarolinePascal
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9b3c752b64 | chore(format): formatting code, fixing error messages and variable names | ||
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CarolinePascal
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51b9038458 | chore(PyAV): cleaning up PyAV utils and encoding parameters checks to stick to the minimun required tooling. | ||
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CarolinePascal
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cc9a2e5c99 | chore(format): fixing formatting issues | ||
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CarolinePascal
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a2376389f9 | test(new): adding new tests for encoding related features | ||
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CarolinePascal
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57a619ab02 | test(existing): adapting existing tests | ||
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CarolinePascal
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71f39f6912 | chore(video backend): renaming codec into video_backend in get_safe_default_video_backend() | ||
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Khalil Meftah
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cd6b43ea7a | fix(train): migrate legacy RA-BC fields in train config loading (#3480) | ||
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Maxime Ellerbach
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cb0a944941 |
refactor(datasets): replace untyped dict with typed DatasetInfo dataclass (#3472)
* refactor(datasets): replace untyped dict with typed DatasetInfo dataclass Introduce typed DatasetInfo dataclass to replace untyped dict representation of info.json. Changes: - Add DatasetInfo dataclass with explicit fields and validation - Implement __post_init__ for shape conversion (list ↔ tuple) - Add dict-style compatibility layer (__getitem__, __setitem__, .get()) - Add from_dict() and to_dict() for JSON serialization - Update io_utils to use load_info/write_info with DatasetInfo - Update dataset utilities and metadata to use attribute access - Remove aggregate.py dict-style field access - Add tests fixture support for DatasetInfo Benefits: - Type safety with IDE auto-completion - Validation at construction time - Explicit schema documentation * fix pre-commit * update docstring inside DatasetInfo.from_dict() * sorts the unknown to have deterministic output Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> * refactoring the last few old fieds * fix crop dataset roi type mismatch * use consistantly int for data and video_files_size_in_mb --------- Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> Co-authored-by: jjolla93 <jjolla93@gmail.com> |
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Khalil Meftah
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8a3d64033f |
Reward models refactor (#3142)
* feat(rewards): add RewardModelConfig and PreTrainedRewardModel base classes * refactor(rewards): migrate Classifier from policies/sac/reward_model/ to rewards/classifier/ * refactor(rewards): migrate SARM from policies/sarm/ to rewards/sarm/ * refactor(rewards): add rewards/factory.py and remove reward model code from policies/factory.py * refactor(rewards): update imports and delete old reward model locations * test(rewards): add reward model tests and update existing test imports * fix(rewards): restore full Classifier and SARM implementations * test(rewards): restore missing CUDA and mixed precision classifier processor tests * refactor(lerobot_train.py): remove rabc specific configuration and replace it with a generic samplerweight class in lerobot_train * refactor(lerobot_train.py): add missing sampling weight script * linter + missing files * add testing for sampl weighter * revert some useless changes, improve typing * update docs * add automatic detection of the progress path * remove type exp * improve comment * fix: move rabc.py to rewards/sarm/ and update import paths * refactor(imports): update reward model imports to new module structure * refactor(imports): update reward model imports to reflect new module structure * refactor(imports): conditionally import pandas based on availability * feat(configs): add reward_model field to TrainPipelineConfig and Hub fields to RewardModelConfig * refactor(policies): remove reward model branches from policy factory and __init__ * refactor(rewards): expand __init__ facade and fix SARMConfig __post_init__ crash * feat(train): route reward model training through rewards/factory instead of policies/factory * refactor(train): streamline reward model training logic * fix(rewards): ensure FileNotFoundError is raised for missing config_file * refactor(train): update __get_path_fields__ to include reward_model for config loading * refactor(classifier): remove redundant input normalization in predict_reward method * fix(train): raise ValueError for non-trainable reward models in train function * refactor(pretrained_rm): add model card template * refactor(tests): reward models * refactor(sarm): update reset method and remove unused action prediction methods * refactor(wandb): differentiate tags for reward model and policy training in cfg_to_group function * fix(train): raise ValueError for PEFT usage in reward model training * refactor(rewards): enhance RewardModelConfig with device handling and delta indices properties --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> |
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Steven Palma
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ca87ccd941 |
feat(rollout): decouple policy deployment from data recording with new lerobot-rollout CLI (#3413)
* feat(scripts): lerobot-rollout * fix(rollout) require dataset in dagger + use duration too * fix(docs): dagger num_episodes * test(rollout): fix expectations * fix(rollout): features check * fix(rollout): device and task propagation + feature pos + warn fps + move rename_map config * docs(rollout): edit rename_map instructions * chore(rollout): multiple minor improvements * chore(rollout): address coments + minor improvements * fix(rollout): enable default * fix(tests): default value RTCConfig * fix(rollout): robot_observation_processor and notify_observation at policy frequency instead of interpolator rate Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): prevent relativeactions with sync inference engine Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): rtc reanchor to non normalized state Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): fixing the episode length to use hwc (#3469) also reducing default length to 5 minutes * feat(rollout): go back to initial position is now a config * fix(rollout): properly propagating video_files_size_in_mb to lerobot_dataset (#3470) * chore(rollout): note about dagger correction stage * chore(docs): update comments and docstring * fix(test): move rtc relative out of rollout module * fix(rollout): address the review comments --------- Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Maxime Ellerbach <maxime.ellerbach@huggingface.co> |
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Steven Palma
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1add460678 |
fix(policy): loss normalization for padded actions in ACT, Diffusion, and MultiTaskDiT (#3442)
* Fix loss normalization for padded actions in ACT, Diffusion, and MultiTaskDiT When action_is_pad masks out padded timesteps, the subsequent .mean() still divides by the total element count (including zeroed-out padding), underestimating the loss. With 60-70% padding this can cut the effective gradient signal by 2-3x. Replace mask-then-mean with mask-then-sum / valid-count for all three affected policies. TDMPC is not affected because it sums over time before averaging over batch. Fixes #3353 * linting Co-authored-by: whats2000 <60466660+whats2000@users.noreply.github.com> Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> * Update src/lerobot/policies/diffusion/modeling_diffusion.py Co-authored-by: whats2000 <60466660+whats2000@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * Update src/lerobot/policies/multi_task_dit/modeling_multi_task_dit.py Co-authored-by: whats2000 <60466660+whats2000@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * Update src/lerobot/policies/multi_task_dit/modeling_multi_task_dit.py Co-authored-by: whats2000 <60466660+whats2000@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * apply ACT loss normalization suggestion from review Divide by num_valid (timesteps * action_dim) instead of just timesteps, matching the diffusion/multi_task_dit fix. Addresses review from @whats2000 (https://github.com/huggingface/lerobot/pull/3377#discussion_r3106845791). * fix(test): update safetensor act --------- Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Yufeng He <40085740+he-yufeng@users.noreply.github.com> Co-authored-by: Maxime Ellerbach <maxime@ellerbach.net> Co-authored-by: whats2000 <60466660+whats2000@users.noreply.github.com> |
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Pepijn
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282c31cfef |
feat(envs): add RoboMME benchmark (#3311)
* feat(envs): add RoboMME benchmark integration - RoboMME env wrapper with image/wrist_image/state observations - Docker image with Vulkan, SAPIEN, mani-skill deps - CI workflow: 1-episode smoke eval with pepijn223/smolvla_robomme - preprocess_observation: handle image/wrist_image/state keys - pyproject.toml: robomme extra Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor(docker): rebase RoboMME image on huggingface/lerobot-gpu Mirror the libero/metaworld pattern: start from the nightly GPU image (which already has apt deps, uv, venv, and lerobot[all] preinstalled) and only layer on what RoboMME uniquely needs — the Vulkan libs ManiSkill/SAPIEN requires, plus the robomme extra with the gymnasium/numpy overrides. Drops 48 lines of duplicated base setup (CUDA FROM, python install, user creation, venv init, base apt deps) that the nightly image already provides. Net: 102 → 54 lines. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs(robomme): drop prototype-branch note and move dataset to lerobot/robomme - Remove the "Related work" block referencing the prototype branch feat/robomme-integration; the PR stands on its own. - Point all dataset references at lerobot/robomme (docs, env module docstring, RoboMMEEnvConfig docstring) — this is the canonical HF location once the dataset is mirrored. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(robomme): make docs build + fast tests green 1. Docs: add robomme to _toctree.yml under Benchmarks so doc-builder's TOC integrity check stops rejecting the new page. 2. Fast tests: robomme's mani-skill transitively pins numpy<2 which is unsatisfiable against the project's numpy>=2 base pin, so `uv sync` couldn't resolve a universal lockfile. Drop robomme as a pyproject extra entirely — it truly cannot coexist with the rest of the dep tree. The Dockerfile installs robomme directly from its git URL via `uv pip install --override`, which was already the runtime path. pyproject, docs, env docstrings, and the CI job comment all now point to the docker-only install. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * test(robomme): realign unit tests with current env API The tests were written against an earlier env layout and never updated when the wrapper was refactored, so CI's fast-test job was failing with: - KeyError: 'front_rgb' / 'wrist_rgb' — these were renamed to the lerobot-canonical 'image' / 'wrist_image' keys (matching the dataset columns and preprocess_observation's built-in fallbacks). - AssertionError: 'robomme' not in result — create_robomme_envs now returns {task_name: {task_id: env}}, not {'robomme': {...}}, so comma-separated task lists work. - ModuleNotFoundError: lerobot.envs.lazy_vec_env — LazyVectorEnv was removed; create_robomme_envs is straightforward synchronous now. Rewrite the 7 failing cases against the current API, drop the three LazyVectorEnv tests, and add a multi-task test so the new comma-separated task parsing is covered. Stub install/teardown is moved into helpers (`_install_robomme_stub` / `_uninstall_robomme_stub`) so individual tests stop repeating six boilerplate lines. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * ci: point benchmark eval checkpoints at the lerobot/ org mirrors pepijn223/smolvla_* → lerobot/smolvla_* across every benchmark job in this branch (libero, metaworld, and the per-branch benchmark). The checkpoints were mirrored into the lerobot/ org and that's the canonical location going forward. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: integrate PR #3311 review feedback - envs: rename obs keys to pixels/image, pixels/wrist_image, agent_pos - envs: add __post_init__ for dynamic action_dim in RoboMMEEnv config - envs: remove special-case obs conversion in utils.py (no longer needed) - ci: add Docker Hub login, HF_USER_TOKEN guard, --env.task_ids=[0] - scripts: extract_task_descriptions supports multiple task_ids - docs: title to # RoboMME, add image, restructure eval section - tests: update all key assertions to match new obs naming Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(docs): use correct RoboMME teaser image URL Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * ci(robomme): smoke-eval 10 tasks instead of 5 Broader coverage on the RoboMME benchmark CI job: bump the smoke eval from 5 tasks to 10 (one episode each), all drawn from ROBOMME_TASKS. Tasks now run: PickXtimes, BinFill, StopCube, MoveCube, InsertPeg, SwingXtimes, VideoUnmask, ButtonUnmask, PickHighlight, PatternLock. Updated the parse_eval_metrics.py `--task` label from the single `PickXtimes` stub to the full comma list so the metrics artifact reflects what was actually run. `parse_eval_metrics.py` already reads `overall` for multi-task runs, so no parser change is needed. Made-with: Cursor * fix(robomme): nest `pixels` as a dict so preprocess_observation picks it up `_convert_obs` was returning flat keys (`pixels/image`, `pixels/wrist_image`). `preprocess_observation()` in envs/utils.py keys off the top-level `"pixels"` entry and, not finding it, silently dropped every image from the batch. The policy then saw zero image features and raised ValueError: All image features are missing from the batch. Match the LIBERO layout: return `{"pixels": {"image": ..., "wrist_image": ...}, "agent_pos": ...}` and declare the same shape in `observation_space`. Made-with: Cursor * fix(robomme): align docs and tests with nested pixels obs layout Addresses PR #3311 review feedback: - Docs: correct observation keys to `pixels/image` / `pixels/wrist_image` (mapped to `observation.images.image` / `observation.images.wrist_image`) and drop the now-obsolete column-rename snippet. - Tests: assert `result["pixels"]["image"]` instead of flat `pixels/image`, matching the nested layout required by `preprocess_observation()`. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(envs): preserve AsyncVectorEnv metadata/unwrapped in lazy eval envs Port of #3416 onto this branch. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * ci: gate Docker Hub login on secret availability Fork PRs cannot access `secrets.DOCKERHUB_LEROBOT_{USERNAME,PASSWORD}`, which made every benchmark job fail at the login step. Gate the login on the env-var expansion of the username so the step is skipped (not failed) when secrets are absent. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(robomme): address review feedback --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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Pepijn
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0f1c9b0851 |
feat(envs): add RoboTwin 2.0 benchmark (#3315)
* feat(envs): add RoboTwin 2.0 benchmark integration
- RoboTwinEnvConfig with 4-camera setup (head/front/left_wrist/right_wrist)
- Docker image with SAPIEN, mplib, CuRobo, pytorch3d (Python 3.12)
- CI workflow: 1-episode smoke eval with pepijn223/smolvla_robotwin
- RoboTwinProcessorStep for state float32 casting
- Camera rename_map: head_camera/front_camera/left_wrist -> camera1/2/3
* fix(robotwin): re-enable autograd for CuRobo planner warmup and take_action
lerobot_eval wraps the full rollout in torch.no_grad() (lerobot_eval.py:566),
but RoboTwin's setup_demo → load_robot → CuroboPlanner(...) runs
motion_gen.warmup(), which invokes Newton's-method trajectory optimization.
That optimizer calls cost.backward() internally, which raises
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
when autograd is disabled. take_action() hits the same planner path at every
step. Wrap both setup_demo and take_action in torch.enable_grad() so CuRobo's
optimizer can build its computation graph. Policy inference is unaffected —
rollout()'s inner torch.inference_mode() block around select_action() is
untouched, so we still don't allocate grad buffers during policy forward.
* fix(robotwin): read nested get_obs() output and use aloha-agilex camera names
RoboTwin's base_task.get_obs() returns a nested dict:
{"observation": {cam: {"rgb": ..., "intrinsic_matrix": ...}},
"joint_action": {"left_arm": ..., "left_gripper": ...,
"right_arm": ..., "right_gripper": ...,
"vector": np.ndarray},
"endpose": {...}}
Our _get_obs was reading raw["{cam}_rgb"] / raw["{cam}"] and raw["joint_action"]
as if they were flat, so np.asarray(raw["joint_action"], dtype=float64) tripped
on a dict and raised
TypeError: float() argument must be a string or a real number, not 'dict'
Fix:
- Pull images from raw["observation"][cam]["rgb"]
- Pull joint state from raw["joint_action"]["vector"] (the flat array)
- Update the default camera tuple to (head_camera, left_camera, right_camera)
to match RoboTwin's actual wrist-camera names (envs/camera/camera.py:135-151)
* refactor(robotwin): drop defensive dict guards, cache black fallback frame
_get_obs was guarding every dict access with isinstance(..., dict) in case
RoboTwin's get_obs returned something else — but the API contract
(envs/_base_task.py:437) always returns a dict, so the guards were silently
masking real failures behind plausible-looking zero observations. Drop them.
Also:
- Cache a single black fallback frame in __init__ instead of allocating
a fresh np.zeros((H, W, 3), uint8) for every missing camera on every
step — the "camera not exposed" set is static per env.
- Only allocate the zero joint_state on the fallback path (not unconditionally
before the real value overwrites it).
- Replace .flatten() with .ravel() (no copy when already 1-D).
- Fold the nested-dict schema comment and two identical torch.enable_grad()
rationales into a single Autograd section in the class docstring.
- Fix stale `left_wrist` camera name in the observation docstring.
* fix(robotwin): align observation_space dims with D435 camera output
lerobot_eval crashed in gym.vector's SyncVectorEnv.reset with:
ValueError: Output array is the wrong shape
because RoboTwinEnvConfig declared observation_space = (480, 640, 3) but
task_config/demo_clean.yml specifies head_camera_type=D435, which renders
(240, 320, 3). gym.vector.concatenate pre-allocates a buffer from the
declared space, so the first np.stack raises on shape mismatch.
Changes:
- Config defaults now 240×320 (the D435 dims in _camera_config.yml), with
a comment pointing at the source of truth.
- RoboTwinEnv.__init__ accepts observation_height/width as Optional and
falls back to setup_kwargs["head_camera_h/w"] so the env is self-consistent
even if the config is not in sync.
- Config camera_names / features_map use the actual aloha-agilex camera
names (head_camera, left_camera, right_camera). Drops the stale
"front_camera" and "left_wrist"/"right_wrist" entries that never matched
anything RoboTwin exposes.
- CI workflow's rename_map updated to match the new camera names.
* fix(robotwin): expose _max_episode_steps for lerobot_eval.rollout
rollout() does `env.call("_max_episode_steps")` (lerobot_eval.py:157) to
know when to stop stepping. LiberoEnv and MetaworldEnv set this attribute;
RoboTwinEnv was tracking the limit under `episode_length` only, so the call
raised AttributeError once CuRobo finished warming up.
* fix(robotwin): install av-dep so lerobot_eval can write rollout MP4s
write_video (utils/io_utils.py:53) lazily imports PyAV via require_package
and raises silently inside the video-writing thread when the extra is not
installed — so the eval itself succeeds with pc_success=100 but no MP4
ever lands in videos/, and the artifact upload reports "No files were
found". Add av-dep to the install line (same pattern as the RoboMME image).
* feat(robotwin): eval 5 diverse tasks per CI run with NL descriptions
Widen the smoke eval from a single task (beat_block_hammer) to five:
click_bell, handover_block, open_laptop, stack_blocks_two on top of the
original. Each gets its own rollout video in videos/<task>_0/ so the
dashboard can surface visually distinct behaviours.
extract_task_descriptions.py now has a RoboTwin branch that reads
`description/task_instruction/<task>.json` (already shipped in the clone
at /opt/robotwin) and pulls the `full_description` field. CI cds into
the clone before invoking the script so the relative path resolves.
parse_eval_metrics.py is invoked with the same 5-task list so the
metrics.json embeds one entry per task.
* ci: point benchmark eval checkpoints at the lerobot/ org mirrors
pepijn223/smolvla_* → lerobot/smolvla_* across every benchmark job in
this branch (libero, metaworld, and the per-branch benchmark). The
checkpoints were mirrored into the lerobot/ org and that's the canonical
location going forward.
* refactor(robotwin): rebase docker image on huggingface/lerobot-gpu
Mirror the libero/metaworld/libero_plus/robomme pattern: start from the
nightly GPU image (apt deps, python, uv, venv, lerobot[all] already
there) and layer on only what RoboTwin 2.0 uniquely needs —
cuda-nvcc + cuda-cudart-dev (CuRobo builds from source), Vulkan libs +
NVIDIA ICD (SAPIEN renderer), sapien/mplib/open3d/pytorch3d/curobo
installs, the mplib + sapien upstream patches, and the TianxingChen
asset download.
Drops ~90 lines of duplicated base setup (CUDA FROM, apt python, uv
install, user creation, venv init, base lerobot install). 199 → 110.
Also repoint the docs + env docstring dataset link from
hxma/RoboTwin-LeRobot-v3.0 to the canonical lerobot/robotwin_unified.
* docs(robotwin): add robotwin to _toctree.yml under Benchmarks
doc-builder's TOC integrity check was rejecting the branch because
docs/source/robotwin.mdx existed but wasn't listed in _toctree.yml.
* fix(robotwin): defer YAML lookup and realign tests with current API
__init__ was eagerly calling _load_robotwin_setup_kwargs just to read
head_camera_h/w from the YAML. That import (`from envs import CONFIGS_PATH`)
required a real RoboTwin install, so constructing the env — and thus every
test in tests/envs/test_robotwin.py — blew up with ModuleNotFoundError
on fast-tests where RoboTwin isn't installed.
Replace the eager lookup with DEFAULT_CAMERA_H/W constants (240×320, the
D435 dims baked into task_config/demo_clean.yml). reset() still resolves
the full setup_kwargs lazily — that's fine because reset() is only
called inside the benchmark Docker image where RoboTwin is present.
Also resync the test file with the current env API:
- mock get_obs() as the real nested {"observation": {cam: {"rgb": …}},
"joint_action": {"vector": …}} shape
- patch both _load_robotwin_task and _load_robotwin_setup_kwargs
(_patch_load → _patch_runtime)
- drop `front_camera` / `left_wrist` from assertions — aloha-agilex
exposes head_camera + left_camera + right_camera, not those
- black-frame test now uses left_camera as the missing camera
- setup_demo call check loosened to the caller-provided seed/is_test
bits (full kwargs include the YAML-derived blob)
* fix: integrate PR #3315 review feedback
- ci: add Docker Hub login step, add HF_USER_TOKEN guard on eval step
- docker: tie patches to pinned versions with removal guidance, remove
unnecessary HF_TOKEN for public dataset, fix hadolint warnings
- docs: fix paper link to arxiv, add teaser image, fix camera names
(4→3 cameras), fix observation dims (480x640→240x320)
* fix(docs): correct RoboTwin 2.0 paper arxiv link
* fix(docs): use correct RoboTwin 2.0 teaser image URL
* fix(docs): use plain markdown image to fix MDX build
* ci(robotwin): smoke-eval 10 tasks instead of 5
Broader coverage on the RoboTwin 2.0 benchmark CI job: bump the smoke
eval from 5 tasks to 10 (one episode each). Added tasks are all drawn
from ROBOTWIN_TASKS and mirror the shape/complexity of the existing
set (simple single-object or single-fixture manipulations).
Tasks now run: beat_block_hammer, click_bell, handover_block,
open_laptop, stack_blocks_two, click_alarmclock, close_laptop,
close_microwave, open_microwave, place_block.
`parse_eval_metrics.py` reads `overall` for multi-task runs so no
parser change is needed. Bumped the step name and the metrics label
to reflect the 10-task layout.
* fix(ci): swap 4 broken RoboTwin tasks in smoke eval
The smoke eval hit two upstream issues:
- `open_laptop`: bug in OpenMOSS/RoboTwin main — `check_success()` uses
`self.arm_tag`, but that attribute is only set inside `play_once()`
(the scripted-expert path). During eval `take_action()` calls
`check_success()` directly, hitting `AttributeError: 'open_laptop'
object has no attribute 'arm_tag'`.
- `close_laptop`, `close_microwave`, `place_block`: not present in
upstream RoboTwin `envs/` at all — our ROBOTWIN_TASKS tuple drifted
from upstream and these names leaked into CI.
Replace the four broken tasks with upstream-confirmed equivalents
that exist both in ROBOTWIN_TASKS and in RoboTwin's `envs/`:
`adjust_bottle`, `lift_pot`, `stamp_seal`, `turn_switch`.
New 10-task smoke set: beat_block_hammer, click_bell, handover_block,
stack_blocks_two, click_alarmclock, open_microwave, adjust_bottle,
lift_pot, stamp_seal, turn_switch.
* fix(robotwin): sync ROBOTWIN_TASKS + doc with upstream (50 tasks)
The local ROBOTWIN_TASKS tuple drifted from upstream
RoboTwin-Platform/RoboTwin. Users passing names like `close_laptop`,
`close_microwave`, `dump_bin`, `place_block`, `pour_water`,
`fold_cloth`, etc. got past our validator (the names were in the
tuple) but then crashed inside robosuite with a confusing error,
because those tasks don't exist in upstream `envs/`.
- Replace ROBOTWIN_TASKS with a verbatim mirror of upstream's
`envs/` directory: 50 tasks as of main (was 60 with many
stale entries). Added a `gh api`-based one-liner comment so
future bumps are mechanical.
- Update the `60 tasks` claims in robotwin.mdx and
RoboTwinEnvConfig's docstring to `50`.
- Replace the stale example-task table in robotwin.mdx with ten
upstream-confirmed examples, and flag `open_laptop` as
temporarily broken (its `check_success()` uses `self.arm_tag`
which is only set inside `play_once()`; eval-mode callers hit
AttributeError).
- Rebuild the "Full benchmark" command with the actual 50-task
list, omitting `open_laptop`.
* test(robotwin): lower task-count floor from 60 to 50
ROBOTWIN_TASKS was trimmed to 50 tasks (see comment in
`src/lerobot/envs/robotwin.py:48`), but the assertion still
required ≥60, causing CI failures. Align the test with the
current upstream task count.
* fix(envs): preserve AsyncVectorEnv metadata/unwrapped in lazy eval envs
Port of #3416 onto this branch.
* ci: gate Docker Hub login on secret availability
* fix: integrate PR #3315 review feedback
- envs(robotwin): default `observation_height/width` in
`create_robotwin_envs` to `DEFAULT_CAMERA_H/W` (240/320) so they
match the D435 dims baked into `task_config/demo_clean.yml`.
- envs(robotwin): resolve `task_config/demo_clean.yml` via
`CONFIGS_PATH` instead of a cwd-relative path; works regardless
of where `lerobot-eval` is invoked.
- envs(robotwin): replace `print()` calls in `create_robotwin_envs`
with `logger.info(...)` (module-level `logger = logging.getLogger`).
- envs(robotwin): use `_LazyAsyncVectorEnv` for the async path so
async workers start lazily (matches LIBERO / RoboCasa / VLABench).
- envs(robotwin): cast `agent_pos` space + joint-state output to
float32 end-to-end (was mixed float64/float32).
- envs(configs): use the existing `_make_vec_env_cls(use_async,
n_envs)` helper in `RoboTwinEnvConfig.create_envs`; drop the
`get_env_processors` override so RoboTwin uses the identity
processor inherited from `EnvConfig`.
- processor: delete `RoboTwinProcessorStep` — the float32 cast now
happens in the wrapper itself, so the processor is redundant.
- tests: drop the `TestRoboTwinProcessorStep` suite; update the
mock obs fixture to use float32 `joint_action.vector`.
- ci: hoist `ROBOTWIN_POLICY` and `ROBOTWIN_TASKS` to job-level
env vars so the task list and policy aren't duplicated across
eval / extract / parse steps.
- docker: pin RoboTwin + CuRobo upstream clones to commit SHAs
(`RoboTwin@0aeea2d6`, `curobo@ca941586`) for reproducibility.
|
||
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whats2000
|
52f508c51c | fix(dataset): cleanup_interrupted_episode wipes image temp dirs (#3405) | ||
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Steven Palma
|
a8b72d9615 |
feat(dataset): 2x faster dataloader via parallel decode, uint8 transport, and persistent workers (#3406)
* feat(dataset): 2xfaster dataloader * fix(dataset): streaming return uint8 decode * fix(tests): adjust normalization step comparison * fix(dataset): with threadexecutor + False default * chore(dataset): make it a config * fix(test): account for uint8 in training path testing |
||
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Steven Palma
|
9021d2d240 |
refactor(imports): enforce guard pattern (#3382)
* refactor(imports): enforce guard pattern * fix(tests): skip reachy2 if not installed * Address review feedback |
||
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Khalil Meftah
|
f5c801fd34 | fix(test): add missing device placement in multi-task DiT tests (#3349) | ||
|
Maxime Ellerbach
|
a656a982af | fix(feetech): motor position readings overflow (#3373) | ||
|
Steven Palma
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df0763a2bc | feat(dependencies): minimal default tag install (#3362) | ||
|
Caroline Pascal
|
d762f4bfe8 |
fix(dataset): adding metadata loading when reading from a dataset after writing (#3305)
* fix(one shot load): adding metadata loading when reading from a dataset after writing * refactor(one shot load): move metadata reload to ensure_readable() on LeRobotDatasetMetadata Move the metadata reload from DatasetReader.load_and_activate() to a new public ensure_readable() method on LeRobotDatasetMetadata, called from LeRobotDataset._ensure_reader(). This places lifecycle management in the right layer: metadata owns its readiness check, the dataset orchestrates the write-to-read transition, and the reader stays clean. Also adds a regression test using delta_timestamps to exercise the meta.episodes access path in the create -> write -> finalize -> read flow. Co-authored-by: Steven Palma <imstevenpmwork@users.noreply.github.com> --------- Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@users.noreply.github.com> |
||
|
Pepijn
|
919184d6f8 |
feat(envs): lazy env init + AsyncVectorEnv as default for n_envs > 1 (#3274)
* docs(benchmarks): add benchmark integration guide and standardize benchmark docs Add a comprehensive guide for adding new benchmarks to LeRobot, and refactor the existing LIBERO and Meta-World docs to follow the new standardized template. Made-with: Cursor * refactor(envs): move dispatch logic from factory into EnvConfig subclasses Replace hardcoded if/elif chains in factory.py with create_envs() and get_env_processors() methods on EnvConfig. New benchmarks now only need to register a config subclass — no factory.py edits required. Net -23 lines: factory.py shrinks from ~200 to ~70 lines of logic. Made-with: Cursor * docs(benchmarks): clean up adding-benchmarks guide for clarity Rewrite for simpler language, better structure, and easier navigation. Move quick-reference table to the top, fold eval explanation into architecture section, condense the doc template to a bulleted outline. Made-with: Cursor * fix link * fix task count * fix: enable SmolVLA eval on LIBERO with custom camera mappings - Thread camera_name_mapping from LiberoEnv config through to gym envs - Sync features_map with camera_name_mapping in LiberoEnv.__post_init__ - Fix render() to use first available camera instead of hardcoded "image" - Handle non-dict final_info in rollout by falling back to info["is_success"] - Add use_peft legacy field to SmolVLAConfig for checkpoint compat - Add defaults to GR00TN15Config init=False fields for transformers 5.3 Made-with: Cursor * fix: use direct AutoresetMode import for gymnasium compat Made-with: Cursor * fix: handle gymnasium < 1.0 without AutoresetMode Made-with: Cursor * refactor: revert policy changes, keep env-only camera mapping fixes - Revert GR00T N1.5 default_factory/default changes (transformers compat) - Revert SmolVLA use_peft legacy field - Apply ruff formatting fixes - camera_name_mapping stays entirely in env/eval layer (no policy changes) Made-with: Cursor * Update docs/source/env_processor.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * feat(envs): lazy env init + AsyncVectorEnv as default for n_envs > 1 LiberoEnv and MetaworldEnv previously allocated GPU resources (EGL context, OpenGL framebuffer) in __init__, before AsyncVectorEnv's fork(). Worker processes inherited stale GPU handles, causing EGL_BAD_CONTEXT crashes on first render. Fix: defer OffScreenRenderEnv / MT1 construction to _ensure_env(), called on first reset() or step() inside the worker subprocess. Each worker creates its own clean context after fork(). Also fixes lerobot_eval.py:170 (add_envs_task TODO): replace with env.call("task") which works with both SyncVectorEnv and AsyncVectorEnv. AsyncVectorEnv is now the default for n_envs > 1; auto-downgraded to SyncVectorEnv when n_envs=1 (no benefit, less overhead). Expected speedup: ~15-20x for LIBERO Spatial with batch_size=50. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: close envs between tasks to prevent worker process accumulation eval_policy_all never closed environments after each task completed, causing AsyncVectorEnv worker processes to accumulate (N_tasks × n_envs). This led to OOM, BrokenPipeError and EOFError on multi-task benchmarks. Also fixes: - AsyncVectorEnv compat in envs/utils.py (use get_attr/call instead of .envs) - Tuple task handling in tokenizer_processor and lerobot_eval - _LazyAsyncVectorEnv for deferred worker spawning in LIBERO Made-with: Cursor * fix(eval): use task_description instead of task for language conditioning env.call("task") returns the LIBERO task name with underscores (e.g. "pick_up_the_black_bowl_...") instead of the natural language description ("pick up the black bowl ..."). The VLM tokenizes these completely differently, causing 0.0 reward across all episodes. Made-with: Cursor * docs: update adding_benchmarks for async env changes - Replace add_envs_task reference with env.call("task_description") - Update use_async_envs default to True - Add note about lazy GPU init for AsyncVectorEnv compatibility Made-with: Cursor * feat(eval): batch_size=auto + faster env loading - batch_size=0 (default) auto-tunes based on CPU cores, capped by n_episodes and 64. Removes the need for users to guess the right value. The old batch_size > n_episodes error is replaced by silently clamping to n_episodes. - _LazyAsyncVectorEnv accepts pre-computed spaces so only one temp env is created per suite (not per task). For libero_spatial (10 tasks) this avoids 9 redundant LiberoEnv instantiations during env setup. Made-with: Cursor * docs: add evaluation guide and update benchmarks doc - New docs/source/evaluation.mdx covering lerobot-eval usage, batch_size auto-tuning, AsyncVectorEnv performance, tuning tips, output format, multi-task evaluation, and programmatic usage. - Add evaluation page to _toctree.yml under Benchmarks section. - Update adding_benchmarks.mdx to reference batch_size auto default and link to the evaluation guide. Made-with: Cursor * docs(evaluation): remove benchmark table, rename section header Made-with: Cursor * perf(eval): shared memory, observation passthrough, task prefetch - AsyncVectorEnv now uses shared_memory=True for zero-copy observation transfer - LiberoEnvConfig.gym_kwargs passes observation_height/width to the env - eval_policy_all prefetches next task's workers while current task runs Made-with: Cursor * style: ruff format Made-with: Cursor * chore: revert env_processor.mdx changes (not part of this PR) Made-with: Cursor * ci(benchmarks): add isolated integration tests for libero and metaworld Each benchmark gets its own Docker image (lerobot[libero] / lerobot[metaworld] only) so incompatible dep trees cannot collide. A 1-episode smoke eval runs per benchmark on GPU runners. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * ci(benchmarks): pin action hashes and use uv sync --locked Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * ci(benchmarks): trigger only on envs/ or lerobot_eval.py changes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): set LIBERO_DATA_FOLDER to bypass interactive stdin prompt libero/__init__.py calls input() to ask about a custom dataset path, which raises EOFError when stdin is closed inside Docker. Setting LIBERO_DATA_FOLDER skips the prompt entirely. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(benchmarks): add CI smoke test step to adding_benchmarks guide Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): pre-create libero config in Dockerfile to bypass stdin prompt libero/__init__.py calls input() when ~/.libero/config.yaml is missing. We write the config at image build time (without importing libero) so the prompt never fires at runtime. Also trigger CI on pyproject.toml changes. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): use shell to create libero config instead of multiline python -c The multiline RUN python -c "..." was being parsed as Dockerfile instructions. Use printf to write ~/.libero/config.yaml directly. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): point libero config to bundled package init_files The config was pointing to /tmp/libero_init which doesn't exist. Use importlib.util.find_spec to locate the hf-libero package directory and write paths to the actual bundled bddl_files/init_files/assets. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): add smolvla extra to benchmark Dockerfiles num2words (required by SmolVLM processor) is declared in lerobot[smolvla], not lerobot[libero/metaworld]. Install both extras together. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(eval): render_frame covers _LazyAsyncVectorEnv isinstance(env, AsyncVectorEnv) silently skipped _LazyAsyncVectorEnv, causing video rendering to produce no frames on the default async path. Switch to hasattr(env, "call") so any async-compatible env (including _LazyAsyncVectorEnv) hits the call("render") branch. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): remove unused _get_sub_env_attr helper _get_sub_env_attr was defined but never called anywhere in the codebase. _sub_env_has_attr (its sibling) is kept — it is actively used in utils.py. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * chore: apply prettier formatting to docs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(env_processor): remove deprecated add_envs_task from pipeline example add_envs_task is replaced by env.call("task_description") in this PR. Remove it from the pipeline walkthrough and renumber the steps (8→7). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): remove __del__ from _LazyAsyncVectorEnv __del__ is unreliable as a cleanup mechanism. close() is already called explicitly in the eval loop's finally block, so the finalizer is redundant. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(eval): prefetch next task's workers after close to avoid GPU memory overlap Previously, next task's AsyncVectorEnv workers were spawned while the current task was still running, causing both tasks' GPU contexts to coexist. Moving the prefetch start into the finally block (after env.close()) ensures workers for task N+1 only spin up once task N has released GPU memory. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): move _LazyAsyncVectorEnv to utils and apply to metaworld _LazyAsyncVectorEnv lived in libero.py but metaworld had the same OOM problem: all tasks' AsyncVectorEnv workers were spawned eagerly, wasting GPU memory for tasks not yet running. Move the class to envs/utils.py so both environments share it, then apply the same is_async + lazy wrapping pattern in create_metaworld_envs. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * chore: remove out-of-scope benchmark/CI/docs files from PR Benchmark CI workflow, Dockerfiles, benchmark docs, evaluation smoke-test doc, and dispatch tests belong in a separate PR. Scope this PR to the async env init changes only. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * chore: restore adding_benchmarks + test_dispatch, drop env_processor changes - Restore docs/source/adding_benchmarks.mdx (belongs in this PR) - Restore tests/envs/test_dispatch.py (belongs in this PR) - Revert docs/source/env_processor.mdx to main (out of scope for this PR) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(adding_benchmarks): remove CI smoke test step (coming in separate PR) Step 7 (Dockerfile + benchmark_tests.yml CI job) and its table rows are out of scope for this PR. The CI infrastructure will be added on top in a follow-up PR. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): remove unused add_envs_task Replaced by env.call("task_description") in lerobot_eval.py. No callers remain in the codebase. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * style: fix prettier formatting in env_processor.mdx Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(eval): catch AttributeError and NotImplementedError explicitly for task description Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(envs): use forkserver context and close envs in test to prevent deadlock AsyncVectorEnv with default fork context leaks worker processes between test_policy parametrized cases; subsequent env creation deadlocks because new forked workers inherit stale pipe FDs from previous test's leaked workers. - configs.py: pass context="forkserver" to AsyncVectorEnv (matches _LazyAsyncVectorEnv) - test_policies.py: call close_envs(envs) at end of test_policy to clean up workers Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(envs): default use_async_envs=False in create_envs and make_env Tests that call make_env(n_envs=2) without passing use_async_envs were getting AsyncVectorEnv, whose forked workers can't resolve gym namespaces registered at runtime. Default to False (sync) so existing tests pass. lerobot_eval.py explicitly passes cfg.eval.use_async_envs, so the CLI async behaviour (controlled by EvalConfig.use_async_envs) is unchanged. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> |
||
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Pepijn
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5de7aa5a4f |
refactor(envs): move benchmark dispatch into EnvConfig subclasses (#3272)
* docs(benchmarks): add benchmark integration guide and standardize benchmark docs Add a comprehensive guide for adding new benchmarks to LeRobot, and refactor the existing LIBERO and Meta-World docs to follow the new standardized template. * refactor(envs): move dispatch logic from factory into EnvConfig subclasses Replace hardcoded if/elif chains in factory.py with create_envs() and get_env_processors() methods on EnvConfig. New benchmarks now only need to register a config subclass — no factory.py edits required. Net -23 lines: factory.py shrinks from ~200 to ~70 lines of logic. * docs(benchmarks): clean up adding-benchmarks guide for clarity Rewrite for simpler language, better structure, and easier navigation. Move quick-reference table to the top, fold eval explanation into architecture section, condense the doc template to a bulleted outline. * fix link * fix task count * fix(tests): fix 3 failing dispatch tests - test_registry_all_types: skip non-EnvConfig stubs (e.g. TestPluginConfig) - test_processors_delegation: use None instead of abstract PreTrainedConfig - test_custom_get_env_processors_override: use DataProcessorPipeline for isinstance check (PolicyProcessorPipeline is a subscripted generic) * fix: enable SmolVLA eval on LIBERO with custom camera mappings - Thread camera_name_mapping from LiberoEnv config through to gym envs - Sync features_map with camera_name_mapping in LiberoEnv.__post_init__ - Fix render() to use first available camera instead of hardcoded "image" - Handle non-dict final_info in rollout by falling back to info["is_success"] - Add use_peft legacy field to SmolVLAConfig for checkpoint compat - Add defaults to GR00TN15Config init=False fields for transformers 5.3 Made-with: Cursor * fix: use direct AutoresetMode import for gymnasium compat Made-with: Cursor * fix: handle gymnasium < 1.0 without AutoresetMode Made-with: Cursor * refactor: revert policy changes, keep env-only camera mapping fixes - Revert GR00T N1.5 default_factory/default changes (transformers compat) - Revert SmolVLA use_peft legacy field - Apply ruff formatting fixes - camera_name_mapping stays entirely in env/eval layer (no policy changes) Made-with: Cursor * Update docs/source/env_processor.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update docs/source/env_processor.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update docs/source/env_processor.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(eval): raise RuntimeError for unsupported final_info format (Gymnasium < 1.0) Made-with: Cursor * style: fix markdown code fences in env_processor.mdx Made-with: Cursor * docs: remove duplicate code blocks in env_processor.mdx Made-with: Cursor * style: revert quadruple backticks to triple (prettier compat) * docs(env_processor): add EnvConfig subclass step and policy_cfg examples - Add missing '### 2. Update Your EnvConfig Subclass' section with get_env_processors() snippet - Update factory usage example to show policy_cfg parameter and keyword-argument style for both SmolVLA and ACT cases * docs(env_processor): rename step 2 and fix policy_cfg examples - Rename '### 2. Update the Factory' → '### 2. Update Your EnvConfig Subclass' - Update factory usage examples to use keyword-argument style with policy_cfg parameter for both SmolVLA and ACT cases --------- Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> |
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Francesco Capuano
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7c032f19fc |
feat(dataset): registering torchvision transforms (#3153)
* add: a flexible transformation registry * fix: image transforms can be set both at init and after * add: tests * fix: take in review * feat(datasets): add image transform setters * fix: pre-commit * fix: CI --------- Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com> |
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Pepijn
|
818892a38b |
feat(dagger): Add HIL/Dagger/HG-Dagger/RaC style data collection (#2833)
* feat: HIL data collection, RTC interpolator, and action queue improvements - Add Human-in-the-Loop (HIL) data collection examples (sync + RTC) - Add HIL data collection documentation - Add ActionInterpolator for smoother policy control at higher rates - Integrate interpolator into lerobot-record and eval_with_real_robot - Add action queue clear() and get_processed_left_over() methods - Add rtc/__init__.py for cleaner imports * docs: expand Related Work section with paper summaries * fix: only record dataset frames at original fps, not at interpolated rate The interpolator speeds up robot control (e.g. 2x) but dataset frames should still be recorded at the original fps. Interpolated-only iterations now only send actions to the robot without writing to the dataset. * refactor: merge HIL sync and RTC scripts into single file with --rtc.enabled toggle Combines hil_data_collection.py and hil_data_collection_rtc.py into one script. RTC is toggled via --rtc.enabled=true (defaults to off for sync inference). Deletes the separate hil_data_collection_rtc.py and updates docs to reflect the single-script usage. * test: add ActionInterpolator test suite (29 tests) Covers constructor validation, passthrough (multiplier=1), 2x and 3x interpolation with exact value checks, reset/episode boundaries, control interval calculation, multi-dim actions, and simulated control loop integration. * test: add ActionQueue + ActionInterpolator integration tests Verifies the interpolator doesn't interfere with RTC's leftover chunk tracking: queue consumption rate matches base fps regardless of multiplier, get_left_over/get_processed_left_over only change on queue.get(), merge preserves smooth interpolation across chunks, and interpolator reset is independent of queue state. * feat: register SO follower/leader configs in HIL script Adds SOFollowerRobotConfig and SOLeaderTeleopConfig imports so SO100/SO101 robots can be used via --robot.type=so_follower and --teleop.type=so_leader. Updates docs accordingly. Made-with: Cursor * docs: remove em dashes from HIL documentation Made-with: Cursor * refactor: rename examples/rac to examples/hil Updates directory name and all references in docs and script docstrings. Made-with: Cursor * fix: encorperate pr feedback comments * refactor(tests): enhance ActionInterpolator test structure and add detailed docstrings * feedback pr and test fix * fix(test): pass correct real_delay in interpolator delay test The test was passing real_delay=0 and relying on _check_delays to silently override it with the index-based diff. Now passes real_delay=3 to match the 3 actions consumed during the simulated inference period. * fix pr feedback * ordering * update hil script * fix * default name * fix(bi_openarm): use kw_only=True to fix dataclass field ordering BiOpenArmFollowerConfig overrides `id` with a default, making it positional in the child — non-default `left_arm_config` then follows a default field, which Python dataclasses forbid. Adding kw_only=True (matching the parent RobotConfig) removes positional constraints. Made-with: Cursor * style: format long line in hil_data_collection.py Made-with: Cursor * pr feedback --------- Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> |
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Pepijn
|
15934d8d08 |
feat(policies): add relative action support for pi0, pi0.5, and pi0_fast (#2970)
* Add option for pi family models to train with relative actions (relative to state) * formatting * add recomputation of stats and option to compute delta stats * normalzie after delta conversion * only recompute state for stats * calulate chunk based stats * sample 100k * load from parquet * sample 1m * stats per chunck * fix * use quantiles * stats for entire dataset * fix * max 1m frames * compute before dist * fix multi gpu processor bug * Fix RTC with delta actions and OpenArms motor_type wiring * feat: align pi0_fast delta actions with pi0/pi05 and add RTC integration tests - Add delta_exclude_joints and action_feature_names to PI0FastConfig - Move to_absolute_actions from modeling to processor pipeline for pi0_fast - Add delta action detection and logging to eval_with_real_robot.py - Add delta actions documentation to pi0 and pi05 READMEs - Fix ruff lint issues in test_delta_actions.py - Add test_rtc_delta_actions.py (24 tests) covering: - ActionQueue with delta vs absolute actions - RTC denoise step with delta leftovers - Full pipeline roundtrip (delta → RTC → absolute) - State rebasing approximation bounds - Non-delta policy compatibility - Multi-chunk consistency * chore: clean up test comments, add OpenPI attribution, remove debug logging - Replace decorative comment separators in test files with plain section headers - Add attribution comments for 1e-6 epsilon in normalize_processor.py (from OpenPI) - Remove debug logging blocks from lerobot_train.py * refactor: extract compute_delta_action_stats into compute_stats.py Move the ~70-line inline delta action stats block from lerobot_train.py into a dedicated function in compute_stats.py, where all other stats computation already lives. The training script now calls it in 6 lines. * refactor: remove unused get_processed_left_over from ActionQueue This method was never called outside of tests. Leftover actions for RTC guidance are always retrieved via get_left_over() (delta/original space). * revert: remove logging-only changes from eval_with_real_robot.py The delta actions detection helper and log message added no functional value — the script already handles delta policies correctly via the processor pipeline. * refactor: use ACTION/OBS_STATE constants instead of hardcoded strings Replace hardcoded "action" and "observation.state" with ACTION and OBS_STATE from utils.constants in compute_stats.py, dataset_tools.py, and lerobot_train.py. * style: remove stray blank lines in training loop * refactor: move delta action stats to preprocessing step, remove on-the-fly computation - Remove on-the-fly compute_delta_action_stats from lerobot_train.py - Rewrite recompute_stats to delegate action stats to compute_delta_action_stats (chunk-based sampling matching what the model sees during training) - Add chunk_size parameter to recompute_stats for delta action computation - Add delta actions documentation to pi0.mdx and pi05.mdx * feat: add recompute_stats CLI operation to lerobot-edit-dataset * fix(tests): relax quantile normalization test tolerance for 1e-6 epsilon * chore: remove agents_memory/pr_details.md from repo * refactor: rename delta actions to relative actions throughout What OpenPI calls "DeltaActions" is actually UMI's "relative trajectory" representation: each action in the chunk is an offset from the current state, not from the previous action. This avoids error accumulation. Renamed across all source, tests, docs, and CLI: - DeltaActionsProcessorStep → RelativeActionsProcessorStep - to_delta_actions → to_relative_actions - use_delta_actions → use_relative_actions - delta_exclude_joints → relative_exclude_joints - compute_delta_action_stats → compute_relative_action_stats - delta_action_processor.py → relative_action_processor.py - test_delta_actions.py → test_relative_actions.py Kept as-is: AbsoluteActionsProcessorStep (converts TO absolute), registry ID "delta_actions_processor" (backward compat), and unrelated delta references (IK pipeline, Robosuite, RA-BC metrics, gym envs). * docs: add Action Representations guide Dedicated page explaining absolute, relative, and delta actions with numerical examples, joint vs EE space, and how to use kinematics pipelines and the relative action processor. References UMI paper (Chi et al., 2024) for the terminology. * docs: remove redundant OpenPI naming note from action representations * docs: remove opinionated OpenPI reference from delta actions section * docs: replace ASCII diagram with UMI paper figure * docs: remove OpenPI reference from action representations * docs: use HF-hosted image instead of local asset * docs: clarify figure attribution * revert: restore original normalization epsilon behavior The 1e-6 unconditional epsilon change perturbed all normalized values, breaking backward compatibility tests. The original approach (1e-8 eps for MEAN_STD, conditional torch.where for QUANTILES) already handles division by zero correctly without affecting non-degenerate cases. * fix: restore delta_action_processor.py used by phone/RL teleop The rename commit incorrectly deleted delta_action_processor.py and duplicated its classes into relative_action_processor.py. Restore the original file and import from it instead. * fix(processor): address PR #2970 review comments - Remove shebang from relative_action_processor.py (library module, not script) - Add device alignment in to_relative_actions/to_absolute_actions so _last_state on CPU doesn't cause cross-device errors when actions are on CUDA - Rename delta_step → relative_step in AbsoluteActionsProcessorStep for naming consistency; update factory.py, all processor files, and tests - Expand _reconnect_relative_absolute_steps docstring to explain why post-hoc rewiring is needed after deserialization - Fix off-by-one in compute_stats.py: sample_upper_bound = total_frames - chunk_size + 1 so last valid start index is included and total_frames == chunk_size is not rejected - Remove redundant NOTE comment in processor_pi05.py (duplicated two lines below) - Fix pi0_fast processor ordering: move relative_step before NormalizerProcessorStep so normalizer sees delta actions (matching pi0/pi05); flip postprocessor to unnormalize → absolute accordingly. Relative stats are now required for all pi models - Revert use_relative_joint_actions_aloha → use_delta_joint_actions_aloha in configuration_smolvla.py (preserve existing public API) - Update action_representations.mdx: add missing joint to 6-DOF example, fix 'based on a figure', clarify pi family ordering, add RTC compatibility section * update rtc link * feat: compute relative action stats over full dataset with optional parallelism Remove the 100k sample cap from compute_relative_action_stats and process all valid chunks. Vectorize with numpy (pre-load actions/states, fancy indexing + broadcasting) for a large speedup over the per-index HF dataset loop. Add num_workers param for thread-based parallelism (numpy releases the GIL). Update docs to show --push_to_hub for recompute_stats. * style: apply ruff formatting to compute_stats.py * testing on real robot * style: fix ruff format and remove redundant .keys() calls |
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Bryson Jones
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2e069b1c47 |
Feature/add multitask diffusion transformer policy implementation (#2545)
* Add multitask diffusion transformer policy Add multitask diffusion transformer policy * expand the observation encoder to support differnt size encoders for vision and text * add RoPE attention module as this is shown to help training dynamics and generation quality for DiTs * update readme and citations for multitask dit policy * remove dino vision encoder and simplify text and vision encoders by removing inheritance structure * adjust factory comment * update docstring for multitask dit policy processor file * simplify config for multitask dit by merging and flattening everything, then adding comments to denote where some parameters are only used for specific objectives * add references to the modeling file comments * merge all modules files into the main modeling file * add torch.no_grad decorators * split up select action return statement * remove redundant asserts * add tutorial to training with multi_task_dit * fix bugs when testing on hardware * remove environment state conditioning * update typo in test instruction comment * add processor tests to multitask dit tests * move policy to top of file * use constants for indexing into batches and remove env state references * remove the base classes since we don't need to be able to extend * fix nit formatting in generate actions fcn * reformat and clean up tutorial for multitask dit policy * add more descriptions and depth to multitask dit tutorial * note origins of each training objective * rename config param for multiple vision encoders * refactor code to perform task tokenization in the processor instead of in the modeling code for multitask dit * add multitask dit to toc for docs * add conditional transformers import to match all other policies that use transformers lib * add test handling for multitask dit when transformers isnt available * skip tests without transformers * remove cropping of images smaller than the crop size * add kwargs arg to multitask dit constructor * add wallx dep conflict management for multitask dit policy * use hyphens for cleanliness in pyproject.toml * add conflict management to pyproject toml for pi conflict for mtdp as well * update tests script to not use unnecessary uv sync call which resolves dependencies that do not need to run. This drastically reduces CI run time * revert fast tests edits * update docs and readme files, fixing some typos and adding multitask dit to readme * chore(dependencies): upgrade transformers + hggingface-hub + peft + scipy * chore(dependencies): bump pi0 family to transformers v5 * chore(dependencies): bump wall x to transformers v5 * chore(dependencies): bump gr00t to transformers v5 * chore(style): fix pre-commit * fix(policy): xvla forced_bos_token missing * test(rl): skip ci tests for resnet10 * Fix: full pi models support for transformer v5 (#2967) * fix(pi): remove loss truncation * fix(pi): remove state padding before tokenization * fix(pi): fix image padding value * fix from_pretrain * add transformer v5 changes * remove reference * more fixes * make it work * add support for rest of pi family * add pifast work * more changes * more changes * more cleanup * fix torch params * dtype fix * torch compile * embed mismatch fix * revert groot * more nit fixes * remove unused classes * more fixes * revert * nit * torch dtype warning fix * but back dynamic renaming * add tie embedding --------- Co-authored-by: Yufei Sun <skieyfly@gmail.com> * chore: fix XVLA in transformers v5 (#3006) * test(policies): enable wall x CI testing * style(test): pre-commit check * style(test): pre-commit --------- Signed-off-by: Bryson Jones <63133702+brysonjones@users.noreply.github.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Jade Choghari <chogharijade@gmail.com> Co-authored-by: Yufei Sun <skieyfly@gmail.com> Co-authored-by: Steven Palma <steven.palma@huggingface.co> |
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Steven Palma
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4e45acca52 |
fix(dataset): use revision-safe Hub cache for downloaded datasets (#3233)
* refactor(dataset): enhance dataset root directory handling and introduce hub cache support - Updated DatasetConfig and LeRobotDatasetMetadata to clarify root directory behavior and introduce a dedicated hub cache for downloads. - Refactored LeRobotDataset and StreamingLeRobotDataset to utilize the new hub cache and improve directory management. - Added tests to ensure correct behavior when using the hub cache and handling different revisions without a specified root directory. * refactor(dataset): improve root directory handling in LeRobotDataset - Updated LeRobotDataset to store the requested root path separately from the actual root path. - Adjusted metadata loading to use the requested root, enhancing clarity and consistency in directory management. * refactor(dataset): minor improvements for hub cache support * chore(datasets): guard in resume + assertion test --------- Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com> Co-authored-by: mickaelChen <mickael.chen.levinson@gmail.com> |
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Steven Palma
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123495250b |
refactor(dataset): split LeRobotDataset into DatasetReader & DatasetWriter (+ API cleanup) (#3180)
* refactor(dataset): split reader and writer * chore(dataset): remove proxys * refactor(dataset): better reader & writer encapsulation * refactor(datasets): clean API + reduce leaky implementations * refactor(dataset): API cleaning for writer, reader and meta * refactor(dataset): expose writer & reader + other minor improvements * refactor(dataset): improve teardown routine * refactor(dataset): add hf_dataset property at the facade level * chore(dataset): add init for datasset module * docs(dataset): add docstrings for public API of the dataset classes * tests(dataset): add tests for new classes * fix(dataset): remove circular dependecy |
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|
Altman
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e64fa667c3 |
fix(vqbet): use in-place fill_ to avoid overwriting DDP GPU buffers with CPU tensors (#3128)
* 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>
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Steven Palma
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d90e4bcfd3 |
refactor(dataset): modular files (#3171)
* refactor(dataset): modular files * refactor(dataset): update imports across the codebase |
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Steven Palma
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9d3b62aa61 | chore(dataset): basic house-keeping (#3170) | ||
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Steven Palma
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7c2ec31793 | refactor(datasets): module cleanup (#3169) | ||
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Steven Palma
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a07b1d76f1 | chore(dependecies): untangle dependecies across internal modules (#3149) | ||
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Steven Palma
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db8547e35d | test(cameras): skip flaky async_read test (#3106) | ||
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Martino Russi
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4f2ef024d8 |
feat(robots): Unitree G1 WBC implementation (#2876)
* move locomotion from examples to robot, move controller to teleoperator class * modify teleoperate to send back actions to robot * whole body controller * add holosoma to locomotros * various updates * update joint zeroing etc * ensure safefail with locomotion * add unitree locomotion * launch camera from g1 server * publish at varying framerates * fix async read in camera * attempting to fix camera lag * test camera speedup * training * inference works * remove logging from pi0 * remove logging * push local changes * testing * final changes * revert control_utils * revert utils * revert * revert g1 * revert again: * revert utils * push recents * remove examples * remove junk * remove mjlog * revergt edit_dataset * Update lerobot_edit_dataset.py Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> * undo teleop changes * revert logging * remove loggings * remove loogs * revert dataset tools * Update dataset_tools.py Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> * move gravity to utils * revert changes * remove matplotlib viewer (rerun works fine) * factory revert * send policy action directly * recent changes * implement flexible action space * send empty command if arms are missing * rename locomotion to controller * add init * implement feedback * add feedback for teleoperator * fix ruff * fix ruff * use read_latest * fix zmq camera * revert exo_serial * simplify PR * revert exo_changes * revert camera_zmq * Update camera_zmq.py Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> * remove frame duplication from zmq server * revert channerfactoryinitialize * keep channelfactoryinitialize * remove zeroing out logic * fix typo * refactor teleop class * simplify teleop further * import armindex at the top * fix visualizer again * revert ik helper * push stuff * simplify image_server * update image_server * asd * add threading logic * simplify ik helper stuff * simplify holosoma * fix names * fix docs * revert leg override * clean connect * fix controller * fix ruff * clean teleoperator * set_from_wireless * avoid double initializations * refactor robot class * fix pre-commit * update docs * update docs format * add teleop instructions * unitree_g1 specific exception in record/teleoperate * add thumbnail to docs * add thumbnail to doc * refactor(unitree): multiple improvements (#3103) * refactor(unitree): multiple improvements * test(unitree): added tests + improved installation instructions * refactor(robots): minor changes unitree robot kinematic * chore(robots): rename g1 kinematics file --------- Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Steven Palma <steven.palma@huggingface.co> |
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Shun.Sasaki
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6139b133ca | fix(async_inference): restore robot module imports in robot_client.py (#3081) | ||
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Steven Palma
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85de893fa7 |
fix(ci): skip HF log in (and tests) in forks and community PRs (#3097)
* 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 |
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Steven Palma
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e489ba24fc |
feat(dependencies): require Python 3.12+ as minimum version (#3023)
* feat(dependecies): upgrade to python3.12 * fix(test): processor regex message * fix(test): processor regex message * fix(dependecies): resolve all tags in python 3.12 * fix(dependecies): add more hints to faster resolve * chore(dependecies): remove cli tag huggingface-hub dep * refactor(policy): update eagle for python3.12 * chore(docs): update policy creation for python 3.12 * chore(test): skip failing tests in macos |
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Steven Palma
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f0d2b37beb |
chore(dependencies): bump transformers v5 (#2964)
* chore(dependencies): upgrade transformers + hggingface-hub + peft + scipy * chore(dependencies): bump pi0 family to transformers v5 * chore(dependencies): bump wall x to transformers v5 * chore(dependencies): bump gr00t to transformers v5 * chore(style): fix pre-commit * fix(policy): xvla forced_bos_token missing * test(rl): skip ci tests for resnet10 * Fix: full pi models support for transformer v5 (#2967) * fix(pi): remove loss truncation * fix(pi): remove state padding before tokenization * fix(pi): fix image padding value * fix from_pretrain * add transformer v5 changes * remove reference * more fixes * make it work * add support for rest of pi family * add pifast work * more changes * more changes * more cleanup * fix torch params * dtype fix * torch compile * embed mismatch fix * revert groot * more nit fixes * remove unused classes * more fixes * revert * nit * torch dtype warning fix * but back dynamic renaming * add tie embedding --------- Co-authored-by: Yufei Sun <skieyfly@gmail.com> * chore: fix XVLA in transformers v5 (#3006) * test(policies): enable wall x CI testing * style(test): pre-commit check * style(test): pre-commit * fix wall x for transformer v5 (#3008) * tv5 fix * various wall x fixes * Delete tests/policies/pi0_pi05/print_pi05_output_logits.py Signed-off-by: Jade Choghari <chogharijade@gmail.com> * sync modeling_florence2.py with chore/bump_transformers_v5 * more * more fixes * more * remove comment * more --------- Signed-off-by: Jade Choghari <chogharijade@gmail.com> * chore(dependencies): adjust dependencies versioning after transformers v5 (#3034) * chore(dependecies): adjust dependecies versioning after transformers v5 * fix(policies): remove deprecated input_embeds * fix(policies): dict _tied_weights_keys * chore(depedencies): common qwen-vl-utils * chore(dependencies): bump transformers to 5.2 * Fix policy testing for tv5 (#3032) * fix ci logger * other fix * fix mypy * change logits to torch2.10 * skip wallx| * remove logging --------- Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> * feat(ci): log into HF to unblock some CI tests (#3007) * feat(ci): log into HF to unblock some CI tests * chore(ci): change hf call + secret name * fix(ci): temp fix for pi0 rtc test * test(policies): require_cuda for unblocked tests * test(policies): require_cuda wall_x * fic(tests): require_cuda outter most for pi0 * fix(test): return instead of yield --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * style(test): fix pre-commit * chore(deps): upgrade transformers (#3050) * chore(test): use lerobot model * fix(policies): change default action tokenizer for wall x * sample on cpu * Revert "Merge branch 'chore/bump_transformers_v5' of https://github.com/huggingface/lerobot into chore/bump_transformers_v5" This reverts commit |
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Caroline Pascal
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cbc8bfb2e6 |
chore(docstrings): updating v2.1-v3.0 conversion script docstrings to match the new task label (#3077)
* 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 |
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Caroline Pascal
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63dca86df8 |
fix(dataset edit tools): clarifying root argument usage + adding related features (#3049)
* 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 |
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Pepijn
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04de496547 | fix(logging): avoid double-counting samples across processes (#3045) | ||
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Khalil Meftah
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baf9b50365 |
Fix(diffusion): enforce no-crop behavior when crop_ratio=1.0 (#3046)
* refactor(diffusion): replace crop_shape with resize_shape and crop_ratio * fix(diffusion): address review feedback on resize/crop backward compat * test: regenerate diffusion artifacts for updated default config * fix: disable crop when resize path uses crop_ratio=1.0 --------- Co-authored-by: starlitxiling <1754165401@qq.com> |
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Steven Palma
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e96339a3b4 |
feat(dataset): add streaming video encoding + HW encoder support (#2974)
* feat(dataset): init stream encoding * feat(dataset): use threads to fix frame pickle latency * refactor(dataset): remove HW encoded related changes * add lp (#2977) * feat(dataset): add Hw encoding + log drop frames (#2978) * chore(docs): add streaming video encoding guide * fix(dataset): style docs + testing * chore(docs): simplify sttreaming video encoding guide * chore(dataset): add commands + streaming encoding default false + print note if false + queue default is now 30 * chore(docs): add verification note advice * chore(dataset): adjusting defaults & docs for streaming encoding * docs(scripts): improve docstrings * test(dataset): polish streaming encoding tests * chore(dataset): move FYI log related to streaming * chore(dataset): add arg vcodec to suggestions * refactor(dataset): better handling for auto and available vcodec * chore(dataset): change log level * docs(dataset): add note related to training performance vcodec * docs(dataset): add more notes to streaming encoding --------- Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> Co-authored-by: Pepijn <pepijn@huggingface.co> |
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Steven Palma
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bc38261321 |
feat(robots): use read_latest() camera (#2987)
* feat(robots): use read_latest() camera * fix(test): add read_latest reachy cam mock |
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Vladislav Sovrasov
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1c388c0002 |
(Chore) Bump upper bound for torch version (#2897)
* Bump upper torch version bound * Apply suggestion from @Copilot Signed-off-by: Vladislav Sovrasov <vladislav.sovrasov@intel.com> * Update ref state dicts for schedulers * Support older than 2.8 torch versions * Fix precommit --------- Signed-off-by: Vladislav Sovrasov <vladislav.sovrasov@intel.com> |
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masato-ka
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51d3822d75 |
feat(datasets): Add info operation to lerobot-edit-dataset command (#2917)
* Add New featrue to lerobot_edit_datset.py that show dataset information. * Fix to draccus error when happen give only --operation.type=info * Updating test and documents regarding lerobot-edit-dataset info function. * Updating documents regarding lerobot-edit-dataset extract function. option name in document is mistake. * feat(datasets): Update to align formatting with pre-commit.(#2917) Update to align formatting by pre-commit. --------- Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> |
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Caroline Pascal
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adebbcf090 |
fix(dataset tools draccus): fixing draccus parsing for dataset edit operation type specification (#2949)
* fix(edit dataset operation): fixing dataset tools CLI operation type specification * test(edit dataset operation): adding tests for dataset tools operation type specification * chore(format): running pre-commit * chore(backward compatibility): adding a type property in OperationConfig for backward compatibility Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com> |