* extend index.mdx
* bring community section up + bigger logo
* fix formatting
* fix logo size
* text fix
* improved explanation
* fix emoji and formatting
* fix link
- Quickstart on LIBERO: finetune lerobot/pi05_libero_base on lerobot/libero
with a complete, copy-pasteable command; feature table mapping the dataset
keys to how pi05 consumes them; gated PaliGemma tokenizer tip.
- Explain --policy.path vs --policy.pretrained_path (weights+config vs
weights-only) and why n_action_steps/empty_cameras must be passed
explicitly with pretrained_path.
- Quantile statistics section: the exact error message, the
lerobot-edit-dataset recompute_stats fix (replacing the removed
augment_dataset_quantile_stats.py reference), where the result lands, and
the MEAN_STD alternative.
- Update stale link lerobot/pi05_libero -> lerobot/pi05_libero_base; add
PyPI install variant.
Co-authored-by: Xingdong Zuo <18168681+zuoxingdong@users.noreply.github.com>
* feat(rollout): add smooth_handover flag to DAgger strategy config
The DAgger phase transitions run blocking smooth handovers: on pause the
leader is driven to the follower (~2 s), and on correction start the
follower is slid to the teleop pose (~1 s), both inside the record loop.
For clutch-style teleoperators (e.g. VR controllers) that re-reference
their command frame at the current robot pose on engage, the handover is
already continuous — the interpolation only delays the start of the
correction and eats its first frames.
Add --strategy.smooth_handover (default true, existing behavior
unchanged) to let such setups skip it, mirroring the episodic strategy's
smooth_leader_to_follower_handover flag.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix: pre-commit auto-fix (prettier markdown table formatting)
---------
Co-authored-by: griffinaddison <griffinnosidda@gmail.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* feat(rollout): add smooth_handover flag to episodic strategy config
Follow-up to #3985, which added the same flag to the DAgger strategy.
The episodic strategy's reset-phase handover had two gaps:
- Non-actuated teleops could not skip the blocking follower slide at all.
- Setting smooth_leader_to_follower_handover=false on an actuated teleop
swapped which arm moves instead of skipping the handover.
Add --strategy.smooth_handover (default true, existing behavior
unchanged) as a master switch that skips the interpolation entirely,
for clutch-style teleops that re-reference at the current robot pose
on engage.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* docs: fix table formatting via prettier
---------
Co-authored-by: griffinaddison <gaddison@seas.upenn.edu>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* fix(vla_jepa): use device-safe autocast instead of hardcoded bfloat16
VLA-JEPA hardcodes torch.autocast with dtype=torch.bfloat16, which
crashes on MPS (no AMP support) and silently misbehaves on pre-Ampere
CUDA GPUs (no bf16). Add a _get_autocast_context() helper that reuses
the existing is_amp_available() utility to pick a safe strategy per
device, matching the pattern used by pi05 and molmoact2.
Fixes#3744
* fix: use _get_autocast_context for fp32 action head (MPS compat)
---------
Co-authored-by: devangpratap <115096812+devangpratap@users.noreply.github.com>
* feat(transforms): add 8 robotics-relevant image augmentations
Add GaussianNoise, MotionBlur, JPEGCompression, GaussianPatchBrightness,
RandomShadow, CoarseDropout, GammaCorrection, and PlanckianJitter.
Each transform addresses a real-world failure mode not covered by the
existing 6 defaults (sensor noise, motion blur, compression artifacts,
uneven lighting, cast shadows, partial occlusion, exposure variation,
color temperature shift).
All transforms are pure PyTorch, follow the make_params/transform
pattern, and integrate with ImageTransformConfig via a registry.
* add augmentation showcase image for PR
* update showcase with better sample frame
* tune showcase to balanced augmentation intensity
* tune showcase: softer shadow, dropout, jitter intensity
* refactor(transforms): several updates
* update image
* chore(media): remove example
* chore: add link to example
---------
Co-authored-by: Yuxian LI <liyuxian1358@gmail.com>
* Fix add_features for multi-dimensional per-frame features
* Use generic names in multidimensional add_features test
* tests(all shapes): enhancing tests to cover all possible features shapes
* chore(format): formatting code
---------
Co-authored-by: felixmin <felix.minze@live.de>
* Support task replacement mappings in modify_tasks
Part of #2326.
Signed-off-by: 陈伟 <woshei0a0a0a@qq.com>
* feat(task modification precedence): Improving task modification precedence so that all modes can be used in a single run. Adapting tests accordinginly.
* chore(fromat): formatting code
* docs(docstrings): updating docstrings
* docs(update): updating docs with the task modification features
---------
Signed-off-by: 陈伟 <woshei0a0a0a@qq.com>
Co-authored-by: vvezre <93599357+vvezre@users.noreply.github.com>
* docs: fix broken and placeholder links
Fix the two links called out in #4094 plus one more found while scanning
all docs: the NOTE:addlinktoLOC placeholder in async.mdx now points at
_aggregate_action_queues in robot_client.py; the dead
#mapping-videoencoderconfig--ffmpeg-options anchor in
video_encoding_parameters.mdx now points at get_codec_options in
video.py; and the stale ./cameras#setup-cameras fragment in il_robots.mdx
is dropped to link the cameras guide page.
Closes#4094.
* docs: apply prettier formatting to the video-encoding parameter table
Re-align the Markdown table columns with prettier (v3.6.2, --prose-wrap=preserve)
so the pre-commit 'Format Markdown with Prettier' hook passes. Whitespace only.
is_saving_step was the only step-frequency check without a `> 0` guard,
so save_freq=0 raised ZeroDivisionError from `step % 0` on the first
step. Route the decision through a should_save_checkpoint helper that
treats a non-positive save_freq as "save only the final checkpoint",
matching how log_freq/eval_freq handle non-positive values.
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
The discard-path fix (#3683) deletes staging for all camera_keys in
clear_episode_buffer(). The post-save cleanup in save_episode() must keep
iterating image_keys only: with batch_encoding_size > 1 the video staging
frames of already-saved episodes stay on disk until the batch encoder
consumes and deletes them. Add a regression test pinning that behavior,
plus a comment explaining why the two paths differ.
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Observations were sent as base64-in-JSON, which inflates every camera
frame 33% purely to fit binary JPEG inside a text protocol (~11 Mbps at
3 cameras x 30 Hz). Send a ZMQ multipart message instead: a JSON header
frame (state + camera order) followed by one raw JPEG frame per camera.
Benchmarked on hardware: -25% wire size (invariant across contention),
lower and tighter latency, and no dropped frames under load where the
base64 format stalled.
ZMQ_CONFLATE does not support multipart, so the observation sockets use
2-deep high-water marks; the client's existing drain-to-latest loop
preserves the keep-newest behavior.
Breaking wire change: host and client must run the same version.
Co-authored-by: Xingdong Zuo <18168681+zuoxingdong@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Without an explicit fourcc, OpenCV's V4L2 auto-negotiation selects
uncompressed YUYV when the camera offers it: ~147 Mbps per camera at
640x480@30 on the Pi's shared USB 2.0 bus, versus ~9 Mbps as MJPG for
identical frames. Two default cameras already exceed the bus, so frame
rates sag silently.
Pinning fourcc="MJPG" is backward compatible: it is an existing
OpenCVCameraConfig field, frames still arrive as BGR ndarrays, user
configs that set their own fourcc are unaffected, and a camera without
MJPG fails loudly at validation rather than degrading silently.
Co-authored-by: Xingdong Zuo <18168681+zuoxingdong@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(datasets): stop frame errors being treated as shard exhaustion in StreamingLeRobotDataset
StreamingLeRobotDataset.__iter__ caught every RuntimeError and treated all as exhausted shard. Real errors like video decode failure made each shard get dropped on the first frame, so iteration ended while yeilding zero frames with no errors.
Shard exahustion is StopIteration raised from make_frame generator, which python converts to RuntimeError with StopIteration as __cause__. Added check to tell StopIteration from everything else, consuming real shard exhaustion while re-raising everything else.
Added a test that injects a decode failure and asserts iteration raises instead of returning on empty stream.
Fixes#4066
* refactor(datasets): exception streaming
---------
Co-authored-by: Mohit Yadav <mohitydv09@gmail.com>
`can_peek_back(steps)` is documented to return whether `peek_back(steps)`
can be called "without raising an IndexError", but it guarded with `<=`:
return steps <= len(self._back_buf) + self._cursor
`peek_back(n)` needs n+1 buffered slots — it raises when
`n + 1 > len(self._back_buf) + self._cursor` and reads
`self._back_buf[self._cursor - (n + 1)]`. So at
`steps == len(self._back_buf) + self._cursor`, `can_peek_back` returns True
while `peek_back` raises LookBackError, contradicting the docstring.
Two siblings confirm the intended bound:
- `prev()` (one step back) requires `len(self._back_buf) + self._cursor > 1`.
- The forward twin is already consistent: `can_peek_ahead(n)` buffers n items
and `peek_ahead(n)` reads `_ahead_buf[n - 1]` (needs n).
Use `<` so `can_peek_back` matches `peek_back`'s guard exactly.
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix: implement bimanual SO leader DAgger handover support
- Add feedback_features property: enables DAgger's teleop_supports_feedback() check
- Implement enable_torque()/disable_torque(): synchronized torque control for both arms
- Implement send_feedback(): routes bimanual feedback to left/right arms with prefix stripping
This fixes DAgger smooth handover for bimanual SO follower + SO leader setups:
when pausing from policy to human intervention, both leader arms now move smoothly
to the follower's current pose, avoiding discontinuities at the human takeover point.
* Update hil_data_collection.mdx
Signed-off-by: Baptiste Lubrano Lavadera <45080391+Mr-C4T@users.noreply.github.com>
* Update bi_so_leader.py
Signed-off-by: Baptiste Lubrano Lavadera <45080391+Mr-C4T@users.noreply.github.com>
---------
Signed-off-by: Baptiste Lubrano Lavadera <45080391+Mr-C4T@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* fix(smolvla): freeze the intended VLM layers when train_expert_only=False
The partial-freeze patterns in set_requires_grad() used a
'text_model.model.' prefix that does not exist in SmolVLM parameter
names ('SmolVLMModel.text_model' is a bare LlamaModel, with no nested
'.model'). As a result the last VLM layer and the final norm were
silently left trainable, defeating the freeze that was added to avoid
unused-parameter errors with DDP; only lm_head was frozen by substring
luck.
Use the real flat names, and raise if any freeze pattern stops matching
so a future transformers renaming cannot silently reintroduce the bug.
Add a CPU regression test covering both last_layers branches.
Fixes#4018
* test(smolvla): drop regression test per review
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
serialize_torch_rng_state/deserialize_torch_rng_state only handled CPU
and CUDA generators. On MPS, resumed training was not bit-exact for any
stochastic op (dropout, ACT's CVAE noise) since the MPS generator's state
was never saved or restored. Mirrors the existing CUDA branch using
torch.mps.get_rng_state/set_rng_state (available since torch 2.11).
Note: get_rng_state()/set_rng_state() (used by seeded_context()) have the
same gap but are out of scope here — happy to follow up separately if
useful.
Co-authored-by: Sunny Dave <sunnydave@Sunnys-Mac-Studio.local>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* docs: fix code fences in env processor guide
The "Flexibility and Reusability" section wrapped a duplicated example
in a four-backtick fence and left a following block unclosed, so the
stray closing fence matched a later block. Everything in between
rendered as one code block that swallowed the surrounding prose.
Remove the duplicated block, add the missing closing fence after the
first example, and normalize the four-backtick fences to three so all
fences pair correctly.
* docs(pi0fast): fix typo 40kk -> 40k steps
* docs(integrate-hardware): fix so101 follower source link
* docs(hope_jr): fix dataset example link
The "example" link in the Record section pointed at the dataset's
`/settings` page, which returns HTTP 403 for readers. Drop the
`/settings` suffix so it links to the public dataset page the
sentence describes.
* docs(lekiwi): render emoji shortcodes as unicode
MDX does not expand `🤗` / `🤖` shortcodes, so they showed as
literal text in the rendered install step. Replace them with the 🤗 and
🤖 unicode characters, matching how the other robot pages write emoji.
* docs(smolvla): anchor record link to its section
The "Record a dataset" link dropped readers at the top of the
il_robots page instead of the relevant section. Point it at the
`#record-a-dataset` anchor (the `## Record a dataset` heading in
il_robots.mdx) so the link lands on the step it names.
* 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>
Nikodem Bartnik