* fix(utils): validate precise_sleep spin/margin args
Negative spin_threshold/sleep_margin make remaining arithmetic wrong
and can overshoot. Reject them early; cover the no-op path.
* test: drop flaky wall-clock assertion in no-op test
Per review: the 50ms wall-clock check can exceed its bound on a preempted
CI worker even when precise_sleep returns immediately. The direct calls
already exercise the non-positive no-op path, so the assertion is redundant.
* chore(tests): remove precise_sleep test negative values
---------
Co-authored-by: Bartok9 <danielrpike9@gmail.com>
* fix(utils): allow any JSON payload in write_json
The dict-only type stub blocked lists/scalars callers already dump.
Accept Any, set utf-8 encoding, and cover list roundtrip.
* fix(utils): json type
---------
Co-authored-by: Bartok9 <danielrpike9@gmail.com>
* fix(utils): mark Transition.complementary_info NotRequired
TypedDict class-body ``= None`` does not make a key optional and confuses
type checkers. Use ``NotRequired[...]`` so transitions without metadata
are valid.
* refactor(utils): complete NotRequired
---------
Co-authored-by: Bartok9 <danielrpike9@gmail.com>
* fix(utils): raise ValueError from get_safe_torch_device
Bare asserts vanish under python -O and look like programmer bugs.
Convert unavailable CUDA/MPS/XPU requests into clear ValueErrors.
* style: combine nested with in device util tests (ruff)
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(utils): reject zero-norm / invalid quaternions in Rotation
Zero or non-finite inputs previously slipped through and produced NaN
rotation matrices on later convert/apply. Validate shape and scept for
norm > 0 before normalizing.
* fix(teleop): degrade phone AR quat parse like missing pose
Address review on #3988: Rotation.from_quat now rejects zero/NaN
quaternions. Wrap HEBI iOS ARKit permission in ValueError and return the
existing (False, None, None, None) path so teleop does not die mid-session
before tracking is ready.
* style: ruff format long ValueError in rotation.py
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* Connect, test and disconnected camera instances sequentially
* Add warmup-s cli argument to lerobot-find-cameras script
* Reduce default record time from 6 to 2 seconds in find_cameras
* Annotate return value of save_image function
* Initialize logging configuration in find_cameras
* fix(robot): type FK-to-EE action features as ACTION not STATE
ForwardKinematicsJointsToEEAction.transform_features declared its
end-effector action features (ee.x/y/z/wx/wy/wz/gripper_pos) with
FeatureType.STATE, copied verbatim from the sibling
ForwardKinematicsJointsToEEObservation (where STATE is correct for
OBSERVATION features). Every other action-producing step in this file
(EEReferenceAndDelta, InverseKinematicsEEToJoints, InverseKinematicsRLStep)
types its ACTION-bucket features as FeatureType.ACTION.
The mismatch mis-classifies the converted EE actions as state, which
propagates a wrong feature schema to downstream consumers keyed on
FeatureType (e.g. normalization norm_map, policy input/output feature
classification).
* test(robot): FK-to-EE step feature-type contract (action vs observation)
Asserts ForwardKinematicsJointsToEEAction emits EE features in the ACTION
bucket typed FeatureType.ACTION, and ForwardKinematicsJointsToEEObservation
emits them in the OBSERVATION bucket typed FeatureType.STATE.
* chore: delete user file
* chore(processor): reduce verbosity
---------
Co-authored-by: Jaagat-P <jaagatp05@gmail.com>
* fix(evo1): re-pad normalizer stats when loading from checkpoint
reconcile_evo1_processors did not re-pad the (un)normalizer stats to
max_state_dim/max_action_dim on the checkpoint-load path. When
lerobot-train loads a checkpoint (e.g. stage2 from a stage1 checkpoint)
it injects the raw dataset stats via processor overrides, so LIBERO's
8-dim state stats normalized a 24-dim padded state and crashed with
"size of tensor a (24) must match tensor b (8)".
Restore _refresh_evo1_normalization_steps (removed in the "remove legacy
codepaths" refactor) and call it from reconcile_evo1_processors so the
loaded stats/features are re-padded to EVO1's fixed widths. Padding is a
no-op when stats are already at the target width.
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(evo1): cover reconcile re-padding of overridden normalizer stats
Regression test for the stage2-from-checkpoint crash: reloading a
checkpoint with raw (unpadded) dataset stats injected via processor
overrides must be re-padded to max_state_dim/max_action_dim by
reconcile_evo1_processors, otherwise normalizing the padded state
raises a shape mismatch.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Martino Russi <martino@huggingface.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(robots): retry SO follower/leader bus reads on transient Feetech errors
SO-100/SO-101 teleoperation aborts when a sync_read of Present_Position
returns a corrupted status packet ("Incorrect status packet!"), which the
Feetech bus emits intermittently under load. The read path already supports
a num_retry argument but the SO follower and leader never used it, so a single
transient failure crashed the control loop.
Add a max_read_retry config option (default 3) to SOFollowerConfig and
SOLeaderConfig and forward it to every Present_Position sync_read. Retries are
immediate and only happen on failure, so the steady-state read cost is
unchanged; set max_read_retry=0 to restore the previous behavior.
Fixes#3131
* chore(robots): change defaults
---------
Co-authored-by: isaka1022 <isaka1022@gmail.com>
* perf(docker): split dep install from src copy for CI layer caching
Install third-party deps (torch + all extras) in a layer keyed only on
pyproject.toml + uv.lock via --no-install-project, then copy src and install
the local package. Editing src/ no longer busts the heavy dependency layer,
so BuildKit layer cache hits across CI builds.
Applied to both Dockerfile.user and Dockerfile.internal.
* chore(ci): less verbose comments + copy all files
---------
Co-authored-by: dongmao.zhang <dongmao.zhang@bytedance.com>
* fix(datasets): bound memory of augment_dataset_quantile_stats by sampling frames
Per-episode stats previously materialized every frame (and decoded up to 16
episodes in parallel), so peak memory scaled with episode length and OOM'd on
large datasets (#2889). Numeric features are now read in full from the table
(exact), while only image/video frames are sub-sampled per episode using the
existing sample_indices heuristic. Worker count is configurable via
LEROBOT_STATS_MAX_WORKERS; --no-sampling restores exact behavior.
* Update tests/datasets/test_augment_quantile_stats.py
Co-authored-by: Haoming Song <1847575517@qq.com>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
---------
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Haoming Song <1847575517@qq.com>
* fix(eval): align seed list length with num_envs when unseeded
eval_policy appended a single None per batch to all_seeds on the unseeded path while the reward and success lists grew by num_envs. The per-episode zip(..., strict=True) then raised ValueError for num_envs > 1. Extend all_seeds by num_envs so the lists stay aligned.
* chore(tests): delete lerobot_eval test
---------
Co-authored-by: Devin Lai <markauto75@gmail.com>
* fix: handle missing/unresponsive TTS on Linux
spd-say may be installed but hang indefinitely when speech-dispatcher
is not running. Add a 5s timeout and catch TimeoutExpired alongside
FileNotFoundError so recording continues without audio.
* chore(utils): add log warning for say
---------
Co-authored-by: Jiwen Cai <jiwenc@nvidia.com>
* refactor(logging): replace print with logger in lerobot_info
* refactor(logging): replace print with logger in convert_dataset_v21_to_v30
* refactor(logging): replace print with logger in lerobot_annotate
* refactor(logging): replace print with logger in lerobot_dataset_viz
* refactor(logging): replace print with logger in lerobot_eval
* refactor(logging): replace print with logger in lerobot_find_cameras
* refactor(logging): replace print with logger in lerobot_find_joint_limits
* refactor(logging): replace print with logger in lerobot_find_port
* refactor(logging): replace print with logger in lerobot_imgtransform_viz
* refactor(logging): replace print with logger in lerobot_setup_can
* refactor(logging): replace print with logger in lerobot_teleoperate
* refactor(logging): replace print with logger in lerobot_train_tokenizer
* fix(logging): preserve CLI output semantics
---------
Co-authored-by: ailisilob <2248345706@qq.com>
Register three X-series Dynamixel models so they can be driven by
DynamixelMotorsBus: XH540-W150 (model 1110), XC330-T288 (1220) and
XC330-T181 (1210). All are standard Protocol 2.0 X-series motors that
share the existing X_SERIES control/baudrate/encoding tables and 4096
resolution; only the model number and operating-mode list are
model-specific. Values verified against the ROBOTIS e-manual.
These motors are used by the ROBOTIS OMY-L100 arm, among others.
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* feat(record): add `--dataset.no_stamp` to opt out of repo_id timestamping
stamp_repo_id() unconditionally appended a date-time tag to repo_id for every new (non-resume) dataset, so users managing their own versioned repo names (e.g. for a later lerobot-edit-dataset merge) could not opt out. Add a no_stamp field to DatasetRecordConfig and make stamp_repo_id() a no-op when it is set. The flag covers both lerobot-record and lerobot-rollout since they share this config, and no call-site changes are needed. Fixes#3722.
* chore(tests): delete dataset config test
---------
Co-authored-by: Philipp Sinitsin <ph.sinitsin@gmail.com>
* feat(camera): add manual exposure, gain, and white balance options for RealSense cameras
The RealSense camera integration lacked sensor-level controls, causing
issues like unstable lighting from auto-exposure hunting. This adds
optional `exposure`, `gain`, and `white_balance` fields to
RealSenseCameraConfig that disable the corresponding auto modes and
apply fixed values when set.
* fix: support D405 stereo module for sensor options
D405 exposes color stream via "Stereo Module", not "RGB Camera".
Fall back to Stereo Module when RGB Camera is not found.
* test(camera): add unit tests + range-aware errors for RealSense sensor options
Address PR #3220 review:
- Wrap set_option calls; re-raise ValueError with option name, value,
and sensor.get_option_range() diagnostics on out-of-range values.
- Add unit tests for _get_color_sensor (RGB Camera, D405 Stereo Module
fallback, diagnostic error) and _configure_sensor_options (no-op,
all values, unsupported warns, partial config, out-of-range raise).
* fix(realsense): validate manual color controls
* refactor(camera): apply feedback
---------
Co-authored-by: Lev Kozlov <kozlov.l.a10@gmail.com>
* feat(lekiwi): support LeKiwi in the rollout/eval CLI
Register the lekiwi robot in lerobot_rollout.py so policies can be rolled out
on a LeKiwi, and keep base-velocity (.vel) features in build_rollout_context.
LeKiwi's observation.state and action are 9-dim (6 arm .pos + x/y/theta.vel)
and the policy is normalized on all 9. The old filter kept only .pos features,
so it fed a 6-dim vector into a 9-dim normalizer (RuntimeError, size 6 vs 9) and
silently dropped the base velocities from the action, leaving the base unable to
move. Keeping both .pos and .vel fixes both. Pure-arm robots have no .vel keys,
so this is a no-op for them.
* style: format LeKiwi rollout action features
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* Add dataloader_multiprocessing_context, default to spawn
Make the DataLoader multiprocessing start method configurable on
TrainPipelineConfig and default it to 'spawn'.
The previous default (fork on Linux) is unsafe with libraries that hold
non-fork-safe state in the parent process — common ones in this codebase
are PyAV, torchcodec, and the ffmpeg shared libs they wrap. Symptoms
reported in #2488, #2209, and observed locally include:
- multiprocessing.context.AuthenticationError: digest received was wrong
- RuntimeError: Pin memory thread exited unexpectedly
- RuntimeError: DataLoader worker exited unexpectedly
- Random SIGSEGV inside worker processes during video decode
Switching to spawn re-imports modules cleanly in each worker and
eliminates these failure modes. Added the setting as a config field
rather than hard-coding so users on platforms where fork is preferred
can opt back in via --dataloader-multiprocessing-context=fork.
* Address review: shorten config comment, note spawn startup tradeoff
Per @jashshah999, mention that spawn workers re-import modules and so
add some startup time vs fork. Also trim the failure-mode dump from
the inline comment — the linked issue covers the symptoms in detail.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore(scripts): add multiprocessing_context safeguards
* chore(config): add libs note
---------
Co-authored-by: 0o8o0-blip <0o8o0-blip@users.noreply.github.com>
Register the `lekiwi` robot module in `lerobot_replay.py` so episodes can be
replayed on a LeKiwi via `--robot.type=lekiwi_client`. The module is already
registered in `lerobot_calibrate.py` and `lerobot_setup_motors.py`; this fills
the gap so the replay CLI recognizes the same robot.
Replayed actions are loaded from the dataset as torch tensors, which
`json.dumps` cannot serialize when `LeKiwiClient.send_action` ships them over
ZMQ. Coerce each action value to a plain float before sending. This is scoped
to the LeKiwi network client and does not affect any other robot.
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(record): connect teleoperator before robot to avoid watchdog jump
lerobot-record connected the robot before the teleoperator. A robot's
connect()/reset() can leave it holding a default pose under a firmware
watchdog (e.g. Unitree G1); if teleop.connect() (model loading, IK init,
network setup) then takes longer than that watchdog, the joints drop to
damping and the first send_action() makes the robot jump.
Swap the order so the teleoperator connects first, matching the ordering
already used in lerobot_teleoperate.py. Pure ordering fix, no API change.
Fixes#3684
* fix(record): trim comment and add connect-order regression test
Address review feedback on #3684:
- Trim the verbose ordering comment down to two lines.
- Add test_record_connects_teleop_before_robot to tests/test_control_robot.py,
asserting teleop.connect() runs before robot.connect() in record().
* chore(test): remove test
---------
Co-authored-by: Jaimin Patel <jpatel@tuvalabs.com>
Co-authored-by: Martino Russi <77496684+nepyope@users.noreply.github.com>
* fix(scripts/eval): restore policy training mode after eval_policy()
`eval_policy` calls `policy.eval()` before the rollout but never restores
the prior mode on return. When called from the training loop
(`lerobot_train.py`'s `eval_policy_all -> run_one -> eval_one ->
eval_policy` chain), the policy is left in eval mode for every subsequent
training step, which silently:
* disables Dropout (no regularisation),
* freezes BatchNorm running stats (no further EMA updates).
Under DDP only `is_main_process` runs eval (lerobot_train.py:527), so the
main rank ends up in eval mode while workers stay in train mode — the
all-reduced gradients then combine forward passes computed with different
dropout masks and different BN behaviour, a real DDP-correctness issue.
Scope of impact:
* Affects every policy with Dropout in its forward path. In-tree, that
includes the default ACT (6 Dropout layers at p=0.1), Diffusion (vision
backbone), VQ-BeT, Multi-Task DiT, X-VLA, plus all VLA policies that
inherit Dropout from their pretrained HF backbone (PI0/PI0.5/PI0-FAST,
SmolVLA, GR00T-N1.5, EO1, Wall-X).
* Triggers from the first eval onward. On the default config
(steps=100k, eval_freq=20k) that's 80% of training; on the LIBERO /
RoboCasa / VLABench example commands in docs/ (eval_freq=1k–5k)
it's 95–99% of training.
* Policies using only LayerNorm/GroupNorm and no Dropout (TDMPC, RTC)
are unaffected. Policies using `FrozenBatchNorm2d` (ACT's ResNet
backbone) are immune to the BN-stat half; the Dropout half still bites.
Fix:
* Snapshot `policy.training` on entry to `eval_policy`.
* Restore it on normal return.
* Save-and-restore is a strict no-op for callers that pass an
already-eval-mode policy (e.g. the standalone `lerobot-eval` script
loading a frozen checkpoint).
* Restoration is placed before the normal return only, not in a
try/finally — exception paths leave the policy in eval mode, same as
today. A try/finally upgrade would require re-indenting ~165 lines and
can land as a separate cleanup if desired.
Tests (tests/scripts/test_eval.py, 7 tests total, ~1.6s):
* Regression gates on the lerobot_eval fix itself: training-mode
preservation, eval-mode preservation, dropout-active behavioural
check, non-crash for both entry modes.
* Quantitative mechanism demonstration
(`test_missing_mode_restoration_hurts_generalisation`): trains a tiny
Dropout+BatchNorm MLP under both the bug pattern and the fix pattern
on identical data and seed, then asserts the buggy variant generalises
at least 5% worse on a held-out val set. In repeated runs we see
10-25% deltas on this toy problem; real policies (more layers, more
Dropout, longer training) generally see larger gaps. Lives alongside
the regression tests so the empirical proof is reproducible from the
repo without adding a separate benchmarks/ directory.
* fix(scripts): keep policy train/eval
---------
Co-authored-by: ModeEric <ericjm4@illinois.edu>
* (depth image processing): excluding depth frames from the RGB to BGR image processing
* test(update): updating tests to include RGB/BGR conversion checks
* fix(deps): bump diffusers cap to <0.39.0 (security)
Diffusers 0.35.x is affected by GHSA-98h9-4798-4q5v (HIGH, CVSS 8.8):
'trust_remote_code bypass via custom_pipeline and local custom components'.
Fixed in diffusers 0.38.0.
Current cap 'diffusers<0.36.0' blocks downstream consumers (e.g.
strands-labs/robots) from picking up the security fix.
The lerobot diffusers surface area is narrow and stable across 0.36-0.38:
- diffusers.schedulers.scheduling_ddim.DDIMScheduler
- diffusers.schedulers.scheduling_ddpm.DDPMScheduler
- diffusers.optimization.get_scheduler
- diffusers.ConfigMixin / ModelMixin / register_to_config
- diffusers.models.attention.{Attention,FeedForward}
- diffusers.models.embeddings.*
None of these were removed, renamed, or had breaking changes in 0.36, 0.37,
or 0.38 release notes. Bumping the cap to <0.39.0 unblocks the security
fix while keeping a major-version safety bound.
* chore(dependecies): bump diffusers
* chore(deps): update uv.lock
---------
Co-authored-by: Cagatay Cali <cagataycali@users.noreply.github.com>
* Make SO follower P coefficient configurable
* chore(test): minimize tests
* feat(robots): expose PID coeff in SO arms
---------
Co-authored-by: taivu1998 <46636857+taivu1998@users.noreply.github.com>
CarolinePascal