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| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Caroline Pascal
|
293a8d9a77 |
feat(examples): add Isaac Teleop → SO-101 teleoperation and dataset recording example (#3927)
* Add Isaac Teleop SO-101 leader-arm teleoperator
Add the NVIDIA Isaac Teleop teleoperator scaffolding and its first device:
SO101LeaderArm, a back-drivable SO-101 leader arm on Isaac Teleop's generic
joint-space device path. It reads the leader's joints from the so101_leader
plugin via JointStateSource and emits follower-ready {joint}.pos (rad2deg arm,
gripper -> RANGE_0_100) for direct 1:1 joint drive.
- IsaacTeleopTeleoperator base + IsaacTeleopConfig (shared session/CloudXR config)
- SO101LeaderArm / SO101LeaderArmConfig and leader_joints_to_robot_action
- examples/isaac_teleop_to_so101/teleoperate_leader.py example
- pure-numpy conversion tests
- isaac-teleop optional extra + NVIDIA PyPI index in pyproject
* Add Isaac Teleop XR controller teleoperator for SO-101
Add end-to-end XR (VR) controller teleoperation of an SO-101 follower arm via
the NVIDIA Isaac Teleop stack, layered on the Isaac Teleop scaffolding.
Teleoperator (src/lerobot/teleoperators/isaac_teleop/):
- XRController / XRControllerConfig: connect to the CloudXR runtime, auto-launch
the Isaac Teleop session, and expose get_action() emitting the raw base-frame
grip pose, squeeze, and trigger.
- MapXRControllerActionToRobotAction: stateless per-frame mapper from the XR
action to the IK input contract (absolute ee.x/y/z, ee.gripper_pos, wrist_roll).
- OverwriteWristRollFromAngle: post-IK step writing the operator wrist-roll [rad]
onto wrist_roll.pos [deg], recovering the under-determined roll DOF.
Example (examples/isaac_teleop_to_so101/):
- teleoperate.py: thin absolute-pose IK pipeline with an in-loop clutch (engage
latch + 1:1 delta rebase of position and orientation), EEBoundsAndSafety, and
InverseKinematicsEEToJoints; slews to a recorded home on startup.
- record_reset_pose.py / download_assets.py / webxr.env / .gitignore.
Also:
- Extend robot_kinematic_processor.py with EEBoundsAndSafety and
InverseKinematicsEEToJoints.
- Add XRControllerConfig + base_T_anchor to the Isaac Teleop config.
- Add docs/source/isaac_teleop.mdx and the _toctree entry.
- Add unit tests for the CloudXR launcher and the XR controller processor.
* Unify Isaac Teleop SO-101 scripts behind a mandatory device selector
Merge teleoperate.py (XR controller: clutch + soft-orientation IK) and
teleoperate_leader.py (SO-101 leader arm: 1:1 joint mirror) into a single
teleoperate.py driven by a `lerobot-teleoperate`-style draccus CLI: a follower
`--robot.*` and an input `--teleop.*`, where `--teleop.type` (xr_controller |
so101_leader) selects the Isaac device.
Uses a "dispatch, don't merge" shape: per-device setup_xr/setup_leader build a
Device bundle (compute / startup / cleanup / command); a shared slew() takes a
per-step target callable (XR a fixed reset pose, leader a live re-read so the
1:1 handoff stays continuous); one device-branchless outer loop runs both, with
compute() -> None meaning "hold at the measured pose" (XR disengaged or leader
stale). The entrypoint is @parser.wrap()'d over a TeleoperateConfig dataclass and
dispatches on the parsed config type; device knobs ride on --teleop.* (the leader
serial port is --teleop.port, forwarded to the plugin) and loop/launch knobs are
top-level (--launch_plugin=<path> collapses the old --launch-plugin/--plugin-bin
pair; --reset_to_origin/--align/--dry_run).
To let the Isaac devices claim the natural --teleop.type names without colliding
with the serial so101_leader of lerobot-teleoperate, give IsaacTeleopConfig its
own draccus choice registry (own _choice_registry, decoupled from the global
TeleoperatorConfig one) and register XRControllerConfig as "xr_controller" and
SO101LeaderArmConfig as "so101_leader" there; the example types its teleop field
as IsaacTeleopConfig so the choices resolve against that scoped registry. These
devices drive the bespoke clutch/IK/align loop and are not routed through
make_teleoperator_from_config, so dropping them from the global registry is inert.
YAGNI sweep of the commit train: delete the orphaned OverwriteWristRollFromAngle
(wrist_roll_processor.py) plus its export and tests -- no producer emits
wrist_roll; the live XR path uses orientation-weight IK on the 5-DOF arm by
design. Kept the load-bearing knobs (orientation_weight, raise_on_jump,
base_T_anchor) and the optional reset-pose recorder. Updated isaac_teleop.mdx
for the unified entrypoint and excised the stale roll-retargeter prose.
Net LOC down (two scripts 714 lines -> one), in-loop device branches reduced to
zero. Planned and reviewed via a 6-persona multi-agent panel (3-round planning
convergence + 2-round review). Verification (isaacteleop/placo not installable
here, so the device classes cannot connect, but their config dataclasses and the
script import fine via deferred imports): the teleoperators test suite passes
(45 passed, 2 skipped), draccus parsing of both target command lines yields the
right config subclass with scoped --teleop.type, --help renders the scoped
choices, the serial so101_leader stays in the global registry, and ruff
check/format are green.
Signed-off-by: Jiwen Cai <jiwenc@nvidia.com>
* Add Isaac Teleop SO-101 dataset recording script
record.py records a LeRobot dataset while driving the SO-101 follower
with either Isaac Teleop device (--teleop.type=xr_controller |
so101_leader), mirroring teleoperate.py's device dispatch.
* Extract shared Isaac Teleop SO-101 example infra into common.py
teleoperate.py and record.py both built the per-device pipeline and ran the
same read -> compute -> hold-when-idle -> sleep loop, with record.py importing
internals from teleoperate.py. Move the shared device/loop infrastructure
(Device, slew, Clutch, setup_xr/setup_leader + leader helpers, reset infra and
constants) into a new common.py, and add build_device() + hold_action() to
collapse the connect/dispatch/startup and idle-hold glue duplicated in both
entry points. The setup functions now type their config against a LoopConfig
Protocol, so common.py is decoupled from either CLI; both import from it.
Also rename record_reset_pose.py -> override_reset_pose.py so it is not confused
with record.py, and update the doc references.
* Add stdin keyboard backend so recording shortcuts work over SSH/headless
lerobot's init_keyboard_listener() uses pynput, which hooks GLOBAL key events
from the display server. Over SSH, under Wayland, or on a headless box with only a
TTY, keystrokes go to the terminal's stdin instead, so the listener never fires and
the Right/Left/Esc recording shortcuts silently do nothing.
Add a stdin (termios) keyboard backend to the example's common.py and an
init_keyboard_listener() that prefers it whenever stdin is an interactive TTY
(works over SSH / Wayland / headless-with-tty), falling back to lerobot's
pynput/headless listener for GUI launches with no controlling terminal. Selectable
via LEROBOT_KEYBOARD_BACKEND={auto,stdin,pynput,none}. The backend keeps ISIG so
Ctrl-C still works and always restores the terminal (on stop() and via atexit).
record.py now sources init_keyboard_listener from common; the Right/Left/Esc -> flag
mapping and the (listener, events) contract are unchanged.
Also convert record.py's loop_kwargs to a dict literal (ruff C408).
* Wait for the XR headset to connect before driving the arm
On the xr_controller path the example connected CloudXR and immediately ran the
reset slew + control loop, even if no headset was connected — the arm moved before
the operator was in VR, and get_action() just returned zeros so the clutch never
engaged.
Add an is_tracking property to XRController (set from the controller stream's
optional group, mirroring SO101LeaderArm) and a _wait_for_xr_controller() helper in
common.py that prints connection instructions (CloudXR web client URL + this
workstation's candidate IPv4s, with loopback/link-local and virtual/bridge/USB-gadget
interfaces filtered out) and polls until the controllers stream (indefinite, 15s
reminder, Ctrl-C to abort). setup_xr.startup() now connects, waits for the headset,
THEN runs the reset slew and seeds the clutch — so the arm only moves once the
operator is connected and watching. Mirrors the leader path's _wait_for_leader; both
record.py and teleoperate.py inherit it via the shared setup_xr.
* Address review feedback on the Isaac Teleop -> SO-101 example
Review-response and CI fixes for the Isaac Teleop -> SO-101 example.
- Move the XR Clutch into src/lerobot/teleoperators/isaac_teleop/clutch.py
(pure numpy + Rotation, no isaacteleop import), export it, and add
tests/teleoperators/test_clutch.py.
- Drop the vendored stdin keyboard listener; record.py uses a small terminal-
first wrapper over upstream's TerminalKeyListener (works over SSH even with a
local X display), falling back to upstream init_keyboard_listener otherwise.
- record.py: pass rgb_encoder/depth_encoder to LeRobotDataset create()/resume()
(upstream removed camera_encoder), fixing the AttributeError at record time.
- build_device: derive motor names from robot.action_features instead of
robot.bus (supports non-bus robots), and disconnect the follower if any step
after connect() fails so a failed setup never leaks the connection.
- Read leader joints by the group's declared names (_joints_group_to_rad)
instead of positionally, so a layout mismatch can't silently mirror the wrong
DOF onto the follower; add tests including a reversed-layout group.
- base.py: hoist `from pathlib import Path` to module scope; only the
isaacteleop CloudXRLauncher import stays lazy (optional dep).
- Trim the common.py module docstring and point to docs/source/isaac_teleop.mdx.
- default.env: correct the NV_DEVICE_PROFILE comment (auto-webrtc is the default;
this file overrides to Quest3, which works for both Quest 3 and Pico 4).
- download_assets.py: correct the RAW_BASE comment (tracks main, not pinned) and
add `# nosec B310` next to the existing `# noqa: S310` for the bandit hook.
- uv.lock: add the isaac-teleop extra's deps so `uv sync --locked` matches
pyproject; regenerated with uv 0.8.0 to keep lockfile revision 2 (CI's uv).
- isaac_teleop.mdx: prettier formatting.
* fix(.gitignore): removing .gitignore and using lerobot cache folder instead to store local user files
* chore(docstrings): reducing docstrings in default.env
* feat(URDF): cleaning up and simplifying the URDF download procedure
* feat(robot guard): adding a guard in case an unsupported robot type is provided (so-arms only)
* fix(imports): enforcing a python module structure to simplify imports
* feat(safe read): extending the motor bus safe read rationale to reset pose setting
* chore(trim): trimming lenghty comments and docstrings
* fix(deps): use isaacteleop [retargeters-lite] extra to unblock aarch64 (DGX Spark) (#3933)
* fix(deps): drop isaacteleop [retargeters] extra to unblock aarch64
The [retargeters] extra pulls dex-retargeting (pins numpy<2.0, conflicting
with lerobot's numpy>=2.0) and nlopt>=2.8 (no aarch64 wheels), making
lerobot[isaac-teleop] unresolvable on ARM (DGX Spark, Jetson Thor, GH200)
and over-constrained on numpy everywhere else.
The LeRobot teleoperators only import isaacteleop.retargeting_engine,
isaacteleop.cloudxr and isaacteleop.teleop_session_manager, all shipped in
the base wheel (requires only numpy>=1.23), so the extra is unused.
Verified on DGX Spark (aarch64, Python 3.12): resolves and installs with
isaacteleop 1.3.131 + numpy 2.2.6; all imported symbols load.
* fix(deps): use isaacteleop [retargeters-lite] extra for aarch64 support
Pin to isaacteleop ~=1.3.131 (the release that added ARM64/aarch64 support)
and swap the full [retargeters] extra for the new [retargeters-lite] one
(scipy-only). The full extra drags in dex-retargeting (pins numpy<2,
conflicting with lerobot's numpy>=2.0) and nlopt>=2.8 (no aarch64 wheels),
making lerobot[isaac-teleop] unresolvable on ARM hosts (DGX Spark, Jetson
Thor, GH200) and over-constrained on numpy everywhere else.
The LeRobot teleoperators only import isaacteleop.retargeting_engine,
isaacteleop.cloudxr and isaacteleop.teleop_session_manager — all covered
by the base wheel + retargeters-lite.
Verified on DGX Spark (aarch64, Python 3.12/3.13): resolves and installs
with isaacteleop 1.3.131 + numpy 2.2.6 + scipy 1.18.
* feat(deps): re-add full [retargeters] extra gated to x86_64
Keep the dex-retargeting/nlopt-based retargeters available on x86_64 (where
their wheels exist) via an environment marker, while ARM hosts (DGX Spark,
Jetson Thor, GH200) resolve with base + [retargeters-lite] only.
Verified: uv lock resolves on both platforms; on aarch64 the compile
excludes nlopt/dex-retargeting, on x86_64 they are included.
---------
Co-authored-by: Johnny Nunez <22727137+johnnynunez@users.noreply.github.com>
* chore(docstrings): trimming latest docstrings
* chore(teleop): move isaac-teleop to examples + update docs + add readme with installation notes
* chore(deps): restore uv.lock
* fix(example: isaac teleop parsing config
* fix(examples): isaac atomic-gripper controller
* feat(Examples): isaac-teleop holdlatch
* chore(examples): some other minor improvements for isaac-teleop
* chore(examples): top-level imports isaac-teleop
* chore(Examples): address ai review isaac-teleop
---------
Signed-off-by: Jiwen Cai <jiwenc@nvidia.com>
Co-authored-by: Jiwen Cai <jiwenc@nvidia.com>
Co-authored-by: Johnny <johnnync13@gmail.com>
Co-authored-by: Johnny Nunez <22727137+johnnynunez@users.noreply.github.com>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
|
||
|
Steven Palma
|
698d2a0e77 |
feat(policies): add EVO1 policy (#3908)
* feat(policies): add EVO1 policy * fix(evo1): infer batch size after normalizing image dims `_collect_image_batches` read `batch_size = batch[camera_keys[0]].shape[0]` before normalizing per-camera tensors to `(B, C, H, W)`. For an unbatched `(C, H, W)` input (which the function tries to support via the `image.dim() == 3` branch), this picked up the channel count `C` instead of the real batch size, making the subsequent per-sample loop iterate `C` times and indexing go out of bounds. Normalize each camera tensor up-front, then read `batch_size` from the normalized batch dim. Adds `test_collect_image_batches_handles_unbatched_chw` covering the regression. Reported by Copilot review on huggingface/lerobot#3545. * chore(lock): regenerate uv.lock for evo1 extra Adds the `evo1` entry to `[package.metadata.requires-dist]` and the `provides-extras` list so that `uv sync --locked --extra test` (used by fast_tests.yml) no longer reports the lockfile as stale. Generated with `uv 0.8.0` (matching `UV_VERSION` in fast_tests.yml). The non-evo1 marker tweaks are produced by `uv lock` re-resolving the existing dep graph and are not introduced by this PR. * chore(evo1): align with policy contribution guide conventions - Add `src/lerobot/policies/evo1/README.md` symlink into `docs/source/evo1.mdx` to match the in-tree README convention (mirroring the EO-1 layout). - Convert `transformers` import in `internvl3_embedder.py` to the standard `TYPE_CHECKING + _transformers_available` two-step gating used by other optional-backbone policies (e.g. diffusion). The previous lazy-in-`__init__` import was functionally equivalent for runtime gating but didn't expose the real symbols to type checkers. - Add `lerobot[evo1]` to the `all` extra in `pyproject.toml` so `pip install 'lerobot[all]'` keeps installing every optional policy. Per the guidance in https://moon-ci-docs.huggingface.co/docs/lerobot/pr_3534/en/contributing_a_policy. * fix(evo1): finalize policy guide alignment * docs(evo1): format results table * Fix EVO1 LIBERO rollout processors * Fix EVO1 LIBERO eval action postprocessing * Fix eval action conversion for bf16 policies * fix(evo1): move LIBERO padding into policy processors * refactor(evo1): use native HF InternVL3-1B-hf, drop trust_remote_code - Switch from OpenGVLab/InternVL3-1B (requires trust_remote_code=True) to OpenGVLab/InternVL3-1B-hf (native transformers implementation). - Replace manual _extract_feature + _prepare_and_fuse_embeddings with a single model.forward() call — verified bit-for-bit identical output. - Remove ~170 lines of manual ViT/pixel-shuffle/projection logic. - Symlink README.md to docs/source/ following repo convention. Weights are byte-identical between both model variants; only the module naming differs. All 12 existing unit tests pass. Local training (10 steps) on maximellerbach/omx_pickandplace confirmed working. * refactor(policy): evo1 GPU-batched preprocessing + vectorized attention masking + remove dead code * fix(style): pre-commit oops * chore(evo1): delete added test + reduce diff * refactor(policies): use config for evo1 + local imports * refactor(policies): multiple improvements * chore: update docs + remove legacy codepaths * feat(policies): implement RTC to EVO1 --------- Co-authored-by: javadcc_mac <javadcc1@sjtu.edu.cn> Co-authored-by: Yiming Wang <145452074+JAVAdcc@users.noreply.github.com> Co-authored-by: Martino Russi <nopyeps@gmail.com> |
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Steven Palma
|
708fa1d189 |
feat(policies): add Gr00t N1.7 policy (#3922)
* Add GR00T N1.7 support
Add GR00T N1.7 policy configuration, checkpoint compatibility, processor parity, LIBERO documentation, and focused tests.
Co-authored-by: Ryan Halabi <ryhalabi@nvidia.com>
* Move Groot processor compatibility into Groot loader
* Restore GR00T Flash Attention install guidance
* Allow Groot fake RTC chunk prefetch
* Fix GR00T N1.7 RTC action decoding
* Trim GR00T N1.7 RTC chunks to valid horizon
* Ignore padded GR00T N1.7 RTC prefix rows
* removed n1.5 dependency
* removed remaining N1.5 traces
* groot: auto-enable LIBERO gripper action transform for libero_sim
GR00T N1.7 emits gripper in [0,1] but LIBERO expects [-1,1]. The decode
transform existed but was never auto-enabled for embodiment_tag=libero_sim,
so the policy scored 0% on LIBERO eval. Auto-set it in __post_init__ (still
overridable). LIBERO Spatial eval: 0% -> 98%.
* Reconnect GR00T relative action processors
* groot: remove dead N1.5 code (eagle2_hg_model, flow_matching_action_head, action_encoder)
N1.7 backbone is nvidia/Cosmos-Reason2-2B via Qwen3VLForConditionalGeneration,
not Eagle2 — eagle2_hg_model/ had zero refs outside its own dir.
GR00TN17ActionHead (groot_n1_7.py) re-implements MultiEmbodimentActionEncoder +
CategorySpecificLinear + swish + SinusoidalPositionalEncoding locally, so
flow_matching_action_head.py (N1.5 FlowmatchingActionHead) and its sole
dependency action_encoder.py are dead. Verified: no src/ or tests/ reference.
Removed (~2037 LOC):
- eagle2_hg_model/ (4 files, ~1575 LOC)
- action_head/flow_matching_action_head.py (408 LOC)
- action_head/action_encoder.py (54 LOC)
cross_attention_dit.py KEPT (DiT/AlternateVLDiT/SelfAttentionTransformer live in N1.7).
* groot: reuse lerobot get_device_from_parameters instead of inline lookup
modeling_groot.py duplicated next(self.parameters()).device twice. LeRobot
ships get_device_from_parameters in policies/utils.py (used by diffusion,
vqbet, tdmpc, gaussian_actor). Reuse it for consistency with the framework.
* groot: fix stale Eagle VLM docstring in processor (N1.7 uses Qwen3-VL backbone)
Addresses checker nit: processor_groot.py docstring still described the N1.5
Eagle VLM path with eagle_content/eagle_* keys that no longer exist in the code.
* test(groot): add N1.7 original-vs-LeRobot output parity test
Verifies the LeRobot GR00T N1.7 integration produces equivalent raw
action_pred to NVIDIA Isaac-GR00T for the same checkpoint, inputs, seed,
precision (fp32) and attention kernel (SDPA): max|diff|=8.9e-7 on the
libero_sim embodiment (GR00T-N1.7-LIBERO/libero_10).
The two impls pin incompatible transformers majors (orig 4.57.3 vs
LeRobot 5.x) and cannot share a process, so the original outputs + exact
collated inputs are produced out-of-process and loaded from an .npz. The
test skips on CI / when the checkpoint or artifact are absent.
* test(groot): parametrize N1.7 parity across all checkpoint embodiments
Generalize the original-vs-LeRobot N1.7 output-parity test from a single
libero_sim case to every embodiment tag in the checkpoint (libero_sim, oxe_droid,
real_g1, the real_r1_pro_sharpa family, and the xdof family). Inputs are built
generically from checkpoint metadata; the test discovers per-tag .npz artifacts
and runs one parametrized case each, loading the LeRobot model once via a fixture.
All 9 embodiments match the original to fp32 epsilon (max|diff| < 3e-6), confirming
the integration is correct across the model's full embodiment space and not overfit
to libero_sim.
* test(groot): self-contained parity test + in-repo producer + docs
- Rename test_groot_n1_7_vs_original.py -> test_groot_vs_original.py
- Make the test self-contained: producer script (dump_original_n1_7.py) now lives
next to the test; default artifact dir is repo-relative
(tests/policies/groot/artifacts/), overridable via GROOT_N1_7_PARITY_DIR. The
test only reads artifacts and skips if absent -- it never creates external dirs.
- Heavy .npz artifacts (~6-9MB each) are gitignored and regenerated by the producer;
never committed.
- Drop the verbose 'MULTIPLE EMBODIMENTS' docstring block (kept a one-line note).
- Document the parity procedure in the groot policy README (docs/source/policy_groot_README.md).
- Rename test fn test_groot_n1_7_get_action_parity -> test_groot_get_action_parity.
9/9 embodiments still pass (max|diff| < 3e-6, fp32 eps).
* docs(groot): drop WHY TWO ENVIRONMENTS block from parity test docstring
* test(groot): move parity producer into utils/ package
Mirror the tests/policies/pi0_pi05/utils convention: move dump_original_n1_7.py into
a tests/policies/groot/utils/ package (with __init__.py) and update all path
references in the test docstring/skip-message and the policy README.
* test(groot): adopt test_groot_lerobot for GR00T N1.7, drop N1.5
The test loaded MODEL_PATH='aractingi/bimanual-handover-groot-10k', an N1.5
checkpoint (config base_model_path=nvidia/GR00T-N1.5-3B, no model_version). On
load, model_version defaults to n1.7 while the base path infers n1.5, so the
version-consistency guard in GrootConfig.__post_init__ raised ValueError and both
test_lerobot_groot_inference and test_lerobot_groot_forward_pass failed. N1.5 is no
longer a supported model_version.
Adopt the test for N1.7:
- MODEL_PATH -> nvidia/GR00T-N1.7-3B (root-level sharded safetensors; loads via
GrootPolicy.from_pretrained as a base N1.7 model).
- Embodiment tag 'gr1' (N1.5) -> 'gr1_unified' (valid N1.7 tag from the checkpoint
embodiment_id.json), via a single EMBODIMENT_TAG constant.
- DUMMY_ACTION_HORIZON 16 -> 40 to match N1.7's native action-chunk size.
- Docstrings/labels updated to 'GR00T N1.7'.
Both tests run and pass on CUDA; full tests/policies/groot/ suite is
73 passed / 0 failed / 0 skipped.
* docs(groot): document the N1.5 removal and the N1.7 parity test
- groot.mdx: breaking-change warning and migration path (pin lerobot==0.5.1 to
keep N1.5, or move to N1.7); the dead `huggingface-cli download` is replaced
with `hf download`.
- policy_groot_README.md: N1.5 removal note, updated paper / model-card links,
and the two-comparison (model parity + preprocessor parity) description of
the original-vs-LeRobot test, including the raw-observation artifacts and
recorded seed.
* fix(groot): N1.7 backbone loading and DiT parameter-count logging
- select_layer default tracks the N1.7-3B checkpoint value (16); real
checkpoint loads still override it from config.json.
- get_backbone_cls recognizes Cosmos-Reason2 / Qwen3-VL backbones by name and
warns (instead of silently assuming) when an unrecognized backbone is loaded
only on the strength of backbone_model_type='qwen'.
- 'revision' pins the GR00T checkpoint repo only and is no longer forwarded
into the unrelated backbone repo load; pin the backbone via
transformers_loading_kwargs instead.
- DiT / SelfAttentionTransformer parameter counts go through logging.debug
instead of print().
* fix(groot): N1.7 config defaults, N1.5 rejection, and processor/model runtime fixes
Covers the GR00T N1.7 source trio (configuration, processor, model wrapper).
Config:
- GrootConfig defaults are the N1.7 values; explicitly passed legacy N1.5-era
values (chunk_size=50, max_state_dim=64, ...) are remapped with a warning
instead of silently.
- action_decode_transform gains an 'auto' sentinel so an explicit 'none'
opt-out wins over the libero_sim default and survives save/load round-trips.
- action_delta_indices is cached on the inputs that determine it.
- Legacy N1.5 checkpoints/configs (tokenizer_assets_repo, model_type/
architectures/eagle backbone markers) are rejected with a single clear
error pointing to lerobot==0.5.1.
Processor:
- GrootN17ActionDecodeStep handles the 2-D (B, D) actions delivered by sync
select_action (relative eef/non-eef decode in eval/record flows).
- Postprocessor falls back to dataset stats when a raw checkpoint lacks the
configured embodiment tag; raw-state cache is per-instance, not
process-global; caller overrides (device, rename_map) are honored on the
raw-checkpoint branch.
- Camera/modality-key mismatches warn (including the zero-match fallback);
deprecated Qwen2VLImageProcessorFast replaced with Qwen2VLImageProcessor;
removed N1.5 processor steps are stubbed to raise the removal guidance and
the action-unpack step is re-registered as _v2.
Model:
- Flash-attention probe is diagnostic-only; forward raises on a missing loss;
print() replaced with logging; N1.5 base-path mismatch includes the
removal guidance.
* fix(groot): skip normalization overrides for training
* fix(groot): GPU/tensor N1.7 image preprocessing + resize to trained resolution
GR00T training was dataloader-bound (0->100->0 GPU-utilization sawtooth).
GrootN17VLMEncodeStep ran the Qwen3-VL image processor per frame on PIL images
on the single CPU main-loop thread, and that cost is timed inside dataloading_s
(preprocessor(batch) runs in the main process, not the dataloader workers), so
adding workers cannot hide it.
- Feed the torchvision-backed Qwen3-VL processor (C,H,W) uint8 tensors instead
of a per-frame Image.fromarray PIL roundtrip, and run resize/normalize/patchify
on config.device (GPU) when available. Bit-identical on CPU when no resize is
configured; with a resize only the PIL->torchvision bicubic backend differs
(<2/255 per pixel). The use_albumentations path stays PIL/cv2; reload on a box
without the saved device falls back to CPU.
- Default image_target_size/crop to the N1.7 backbone's training geometry
(256x256 / 230x230) when a checkpoint ships no image sizing (checkpoint_assets
is None, e.g. finetuning nvidia/GR00T-N1.7-3B via repo-id with a new
embodiment). Previously image_target_size=None disabled the resize, so
full-resolution frames were patchified into ~4.7x more vision tokens than the
model was trained on -- inflating dataloading_s (patchify) and update_s (VLM
sequence) and skewing the input distribution. Checkpoints that pin their own
sizing are honored; the default constants are shared with GR00T_N1_7_DEFAULTS.
Net: preprocessing leaves the CPU critical path and the VLM sees the resolution
it was trained on -- faster training/inference and a correct train/serve
distribution. Affects inference too (shared preprocessor); existing checkpoints
still load (backward compatible) but must be retrained to gain the benefits.
* refactor(groot): N1.7 style cleanup (utils, imports, flash-attn, config)
Mechanical refactor of the GR00T N1.7 policy to match the repo's architecture and
style standards. No change to policy algorithm/numerics; only UX/CLI and packaging
changes. Tests are intentionally left untouched (out of scope) and need updating
for the removed `model_version` field.
Cleanup & consolidation:
- Add `groot/utils.py` holding the pure, side-effect-free helpers (JSON I/O, value
coercion, stat flattening, rot6d/SE3 math, language/batch prep) shared by the
config and processor layers.
- Remove dead code: the unused `resolve_groot_n1_7_backbone_model` cache-resolver
cluster, `GR00TN17Config.to_filtered_dict/json`, and the `_copy_default` wrapper.
Imports & execution guards:
- Hoist nested imports to module top; relative imports within the package, absolute
for external modules. The version-gated Qwen3-VL classes import under the single
`_transformers_available` guard (transformers is pinned >=5.4, which ships them).
- No import-time side effects: `_register_with_transformers()` now runs in
`GR00TN17.__init__` (idempotent via `register(exist_ok=True)`), and the N1.5 step
stubs register lazily before pipeline deserialization (idempotent via the
registry, no run-once globals).
- Gate optional deps at the point of use with `require_package(..., extra="groot")`.
Dependencies & docs:
- Drop `flash-attn` (and its build-only dep `ninja`) from the `groot` extra; default
to SDPA (numerically equivalent) with opt-in via `--policy.use_flash_attention`.
Un-comment `lerobot[groot]` in the `all` extra and regenerate `uv.lock`.
- Rewrite the `groot.mdx` install section: flash-attn is a purely optional,
user-managed optimization that LeRobot neither installs nor requires.
Config & CLI:
- Surface previously-frozen knobs on `GrootConfig` (plumbed into `GR00TN17Config`;
no-ops at their defaults): inference — `num_inference_timesteps`, `rtc_ramp_rate`,
`use_flash_attention`; fine-tuning — `tune_top_llm_layers` (partial-LLM tuning)
and `tune_vlln` (previously hardwired to True).
- Convert the single-valued `model_version` and `n1_7_backbone_model` fields to
internal constants.
- Keep `base_model_path`: it is NOT equivalent to `pretrained_path` (raw NVIDIA
checkpoints have no LeRobot `type` field and load only via `base_model_path`) and
is genuinely user-tunable.
- Keep the deprecated Isaac-GR00T/N1.5 fields (and the dead LoRA fields) as a
back-compat block so a v0.5.1 N1.5 `config.json` still parses under draccus and is
rejected with the friendly N1.5 removal message instead of an opaque decode error.
* Optimize GR00T N1.7 image preprocessing
* Remove PIL fallback from GR00T preprocessing
* Fix GROOT relative action training stats
* Address GROOT relative action review feedback
* Fix GROOT N1.7 relative action stats
* Fix GROOT relative action training stats
* Fix GROOT relative action padding and RTC leftovers
* Reset rollout state after robot episode end
* Revert "Reset rollout state after robot episode end"
This reverts commit
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Pepijn
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e275ea3960 |
LingBot-VA: video-action world model (#3731)
* feat(policies): add LingBot-VA autoregressive video-action world model Port the LingBot-VA policy (Wan2.2 dual-stream video+action world model) into LeRobot, following the EO-1 / VLA-JEPA conventions. Covers inference, checkpoint conversion, and predicted-video saving (training is deferred to a follow-up PR). - Vendored Wan transformer/attention/flex/VAE/scheduler modules (key names preserved for near-identity conversion); torch SDPA default, flashattn/flex lazy-guarded. - LingBotVAConfig (registered "lingbot_va") + processor with fixed-quantile action unnormalization; full dual-stream sampling loop with CFG, two flow-matching schedulers and KV cache, mapped onto select_action with observed-keyframe feedback. - convert_lingbot_va_checkpoints.py (libero/robotwin variants): bundles the ~5B transformer, lazy-pulls the frozen VAE+UMT5 from the source repo. - Predicted-video plumbing in lerobot_eval (predicted_frames_callback; opt-in via --policy.save_predicted_video) and ConstantWithWarmupSchedulerConfig. - pyproject: widen diffusers-dep to <0.37, add lingbot_va + imageio-dep extras, add lingbot_va and (missing) eo1 to `all`. - Factory + policies/__init__ wiring, docs page + toctree, and tests. Note: the LIBERO success-rate correctness gate must be validated on a CUDA GPU with the converted checkpoint. * feat(lingbot_va): RoboTwin eef-pose eval, single-file model, Hub checkpoints Make the LingBot-VA port runnable on both LIBERO and RoboTwin and clean up the package to LeRobot conventions. - Consolidate all vendored Wan2.2 model code (transformer, attention, VAE helpers, flow-matching scheduler, grid utils, flex-attention) into a single modeling_lingbot_va.py; remove the separate wan_*/schedulers modules. - Move the fixed action (un)normalization quantiles out of the config and into the post-processor (LIBERO 7-DoF + RoboTwin 16-d eef); remove the conversion script in favour of ready-to-use LeRobot-format checkpoints on the Hub. - Fixes found via on-sim validation: undo LIBERO's 180-degree image flip (image_hflip), encode obs as a multi-frame streaming-VAE clip, reset the streaming VAE cache between episodes, run the transformer in config.dtype, lazy-load frozen VAE/UMT5 by subfolder with the text encoder on CPU. - RoboTwin: add an end-effector-pose action mode to RoboTwinEnv (16-d per-arm xyz+quat+gripper deltas composed onto the initial eef pose, executed via CuRobo IK) and the robotwin_tshape latent layout (full-res head + half-res wrists via a second streaming VAE) with the upstream RoboTwin action quantiles + camera mapping. - Predicted-video saving works for both benchmarks; docs + tests updated. * feat(lingbot_va): implement training / fine-tuning (flow-matching loss) - Implement LingBotVAPolicy.forward(): dual-stream flow-matching training loss (latent + action, timestep-weighted, action-masked) ported from upstream train.py; VAE-encodes camera clips, UMT5-encodes the task, noises both streams, runs the block-causal flex-attention training pass (forward_train). - training_loss_from_streams() core + _build_training_streams() data prep (action scatter into the 30-d space, multi-frame VAE encode incl. robotwin_tshape). - get_optim_params returns only trainable transformer params (LoRA/PEFT friendly); VAE/UMT5 stay frozen. Training needs attn_mode='flex'. - Add a tiny-config single-training-step test (forward->loss->backward->AdamW) and a Training/fine-tuning section in the docs. * fix(lingbot_va): CI quality gate + fast-test collection - Add tests/policies/lingbot_va/__init__.py so the test files don't clash by basename with tests/policies/vla_jepa/* under pytest's default import mode (fast-test collection error). - Fix vendored typos flagged by the typos hook (pach_scale->patch_scale, total_tolen-> total_token_len, stablized->stabilized) and a mypy union-attr in RoboTwinEnv._read_eef_pose. - Apply Prettier formatting to docs/source/lingbot_va.mdx. * docs(lingbot_va): document EEF action-channel schema + camera order * Update lingbot_va.mdx Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update pyproject.toml Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update pyproject.toml Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * refactor(lingbot_va): drop hardcoded action quantiles; source from checkpoint The LIBERO/RoboTwin action (un)normalization quantiles were hardcoded as module constants in processor_lingbot_va.py. They are already serialized into each checkpoint's policy_postprocessor.json (via LingBotVAActionUnnormalizeStep.get_config) and restored on load by PolicyProcessorPipeline.from_pretrained, so the constants are dead at eval/load time for the released checkpoints (verified: libero_long/robotwin/base all carry their quantiles on the Hub). - Remove LIBERO_ACTION_Q01/Q99, ROBOTWIN_ACTION_Q01/Q99 and _default_action_quantiles. - make_lingbot_va_pre_post_processors now defaults a fresh (unconverted) build to a neutral [-1, 1] mapping (identity rescale); real per-benchmark stats come from the saved checkpoint (or postprocessor_overrides), analogous to dataset-stats normalization. - Update the config doc comment to point at the checkpoint as the source of truth. - Tests: replace the LIBERO-default assertion with a neutral-default check, and add a save_pretrained/from_pretrained round-trip guard for the quantile serialization. * docs(lingbot_va): trim verbose comments - configuration_lingbot_va.py: condense multi-line field comments to one-liners (keep the ── section headers). - processor_lingbot_va.py: shorten the action-quantile explanation block. - modeling_lingbot_va.py: drop the bare "# ----" separator rules, keeping the one-line section headers. No code changes. * docs(lingbot_va): trim provenance comments; default wan path to base repo - configuration_lingbot_va.py: drop the "──" decorations and the "(from transformer/config.json)" note; default wan_pretrained_path to robbyant/lingbot-va-base (has the frozen vae/text_encoder/tokenizer subfolders). - modeling_lingbot_va.py: remove the vendored-code banner and the "(upstream wan_va/...)" section-header provenance/dash decorations; condense the transformer-dtype comment to one line. No code changes. * refactor(lingbot_va): use built-in UnnormalizerProcessorStep for actions Replace the bespoke LingBotVAActionUnnormalizeStep with the standard UnnormalizerProcessorStep in QUANTILES mode, which computes the identical (action + 1) / 2 * (q99 - q01) + q01 mapping. The per-channel q01/q99 are stored as the step's saved state (a safetensors file) and restored on load; a fresh build has no action stats so the step is an identity passthrough. The 3 Hub checkpoints (lerobot/lingbot_va_{libero_long,robotwin,base}) have been re-uploaded with the new post-processor (policy_postprocessor.json + *_unnormalizer_processor.safetensors); reloading from the Hub round-trips q01/q99. - processor_lingbot_va.py: drop the custom step + registry; build the post-processor with UnnormalizerProcessorStep (explicit ACTION->QUANTILES norm_map so the preprocessor / training path is unchanged). - tests: assert the built-in step is used, identity-when-no-stats, correct quantile unnormalization, and a save_pretrained/from_pretrained stats round-trip. * docs(lingbot_va): point checkpoint paths at the lerobot org The LeRobot-format checkpoints moved from pepijn223/* to lerobot/* (libero_long, robotwin, base). Update the eval/train --policy.path examples accordingly. * docs(lingbot_va): condense processor normalization comments * fix(lingbot-va): align RoboTwin evaluation (#3784) Thank you for the RoboTwin fix, and alignment! * applying fixes * updating uv lock and linting * adjusting test to match expected values * cleaning up deps * cleaning up top level imports, styling, and deps guards * cleanup * moving wan utils and loading utils to `utils.py` * removing ftfy by replicating the prompt_clean function without it (we don't expect to have weird chars given in the prompt anyway) * removing unused function * guarding for scipy dep, renaming test to avoid collision * adding back accelerate for peak memory usage optim + justifying robotwin description dep --------- Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: pepijn223 <pepijn223@hf.co> Co-authored-by: Gangwei XU <gwxu@hust.edu.cn> Co-authored-by: Maxime Ellerbach <maxime.ellerbach@huggingface.co> |
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Maxime Ellerbach
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141c353206 |
feat(policies): Add FastWAM Policy (#3834)
* Add FastWAM policy * Add FastWAM policy review updates * big refactor to use models from diffusers and transformers * changing reproducable results * preparing for training adding some temporary debug code aswell to visualize model output * re-parenting of some layers to enable proper zero-3 FSDP * linting * small fix for the preprocessor and padded images * removing some preprocessors * removing temporary debug code * cleaning up * updating uv lock after rebasing * adding lazy imports * linting * fixing stale assertion * make tokenizer/text-encoder model ids configurable + some nits * moving and renaming files to have a cleaner file tree * removed asserts from the model, added guard instead and completely removed useless asserts * cleaning up imports * removing is_main_process and custom logging logic * removing unused / stale attention path, removing some of the stale forwards within wan/models --------- Co-authored-by: ZibinDong <zibindong@outlook.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> |
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Pepijn
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cec8ee0be6 |
feat: language annotation pipeline (#3471)
Steerable annotation pipeline (lerobot-annotate) that populates the language_persistent and language_events columns introduced in PR 1 (#3467) directly into data/chunk-*/file-*.parquet. This is PR 2 of the three-PR plan: PR 1 (Add extensive language support #3467): schema + DSL + rendering, base of this PR PR 2 (this PR): annotation pipeline writing into PR 1's columns PR 3: model with language prediction and runtime A VLM (Qwen-VL family, served on vLLM) watches each episode's video and emits grounded language annotations: subtasks, plans, memory, task rephrasings, interjections + speech, and per-camera VQA. The pipeline is built for production annotation at scale — single-camera grounding, embedded-frame inputs, a describe-then-segment grounding flow, and a deterministic full-episode coverage guarantee — informed by Scale's dense-captioning findings (representation > sampling, rules > reasoning, model capacity is the biggest lever, two-pass systems compound errors) |
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Maxime Ellerbach
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2e9cd87bbd |
feat(policies): add VLA-JEPA (#3568)
* first commit * feat(policies): add VLA-JEPA * feat(policies): add VLA-JEPA * support vla_jepa * (feat)policies: add VLA-JEPA * linting * adding deps to pyproject.toml * updating uv lock * adding guards to avoid needing transformers and diffusers for type checking and basic tests * fixing action and state dim * fix warnings with qwen processor kwargs * fixing wm_loss not propagating * adjusting obs steps, tublets size to match original implementation * some more fixes to be closer to the original implem * adding more tests to ensure good coverage * align VLA-JEPA architecture with original checkpoint - Remove stale `action_num_heads` / `action_attention_head_dim` config fields; DiT head dimensions are now always derived from the preset (DiT-B/L/test). - Add `num_target_vision_tokens` and `action_max_seq_len` config fields required by the action head's future-token embedding and positional embedding tables. - Fix default `qwen_model_name` to 2B (matches all released checkpoints). - Rename `ActionEncoder` attrs w1/w2/w3 → layer1/layer2/layer3 to match checkpoint key names; replace `nn.Sequential` decoder/state-encoder with `_MLP2` (layer1/layer2 naming). - Fix `VLAJEPAActionHead` to size ActionEncoder and StateEncoder at `inner_dim` (DiT input width) rather than `action_hidden_size` (DiT output width). - Rename `DiT.blocks` → `transformer_blocks` and `attn` → `attn1` to match checkpoint; add alternating cross/self attention (even blocks cross-attend to Qwen context, odd blocks self-attend). - Add `DiT-test` preset for unit tests. - Rewrite `ActionConditionedVideoPredictor` with explicit ViT-style blocks (`_PredictorBlock` with fused qkv) to match checkpoint structure; rename `encoder`/`norm`/`proj` → `predictor_blocks`/`predictor_norm`/`predictor_proj`. * propagate action_is_pad masking through VLA-JEPA policy pipeline Pass the `action_is_pad` tensor from the batch through to the action head so padded timesteps are excluded from the flow-matching loss. * update VLA-JEPA tests for arch changes and action_is_pad - Switch conftest to use `action_model_type="DiT-test"` now that `action_num_heads` / `action_attention_head_dim` have been removed. - Add action_head tests covering fully-padded loss (zero) and equivalence of action_is_pad=None vs all-zeros mask. - Remove obsolete `test_native_to_lerobot_wm_only` test. * add VLA-JEPA documentation Covers architecture overview, pretrained checkpoints, config reference, training/eval commands for LIBERO-10, and guidance on fine-tuning for single-camera datasets. * add one-shot script to convert ginwind/VLA-JEPA checkpoints to safetensors (will remove once migrated) * make default params more aligned with paper and pretrained models - adding possibility of freezing qwen backbone and world model - added tests for weight loading * trying out to re-init the action head to avoid pretraining dimension mismatch * allow different state dim and action dim * removing missleading future_action_window_size to just use chunk_size * lots of changes to make existing weights work, need to massively refactor the pre and post processing * refactoring into using pre and post processor * pre-commit cleanup * fixing doc defaults args Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> * adressing dtype zeros issue * adding guard for diffusers * fixing training and exal examples * trying to close success rate gap * fix qwen norm layer output libero eval is now as expected * adding instructions for different embodiement + fixing some tests * smol fix to avoid having default CPU device when training * fixing misconception about multiview / singleview handling * removing conversion script * adding licences * adding .mdx docs and shortening polivy_vla_jepa_README.md * removing useless pre-processor * cleanup * removing swish in favor of silu * adding configuration gripper index and threshold * fixing simlink --------- Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> Co-authored-by: ginwind <ginwind@mail.ustc.edu.cn> |
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Nikodem Bartnik
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741c2d0a39 |
Docs/add lelab (#3707)
* first text draft (no images) * simplified docs * fix formatting * add youtube video * add a tip about compatibility * fix broken link |
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Khalil Meftah
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b8ad81bf39 |
feat(rewards): add ROBOMETER reward model (#3627)
* feat/add ROBOMETER reward model * feat(rewards): add Robometer offline progress labeling script * fix(rewards/robometer): add missing input keys mm_token_type_ids * chore(rewards/robometer): default to lerobot/Robometer-4b model * doc(rewards/robometer): update citation and original github link * feat(rewards/robometer): add image key argument to compute Robometer progress |
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Haoquan Fang
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24017e960c |
Add MolmoAct2 policy (#3604)
* add molmoact2 policy * add apache headers to molmoact2 files * simplify molmoact2 package imports * align molmoact2 feature validation with eo pattern * remove molmoact2 processor override from factory * guard molmoact2 transformers imports * guard molmoact2 processor transformers import * add scipy dependency to molmoact2 extra * use a single molmoact2 action queue * move molmoact2 config logic into config * fix molmoact2 hf image key resolution * load molmoact2 without remote code * lazy import molmoact2 scipy * format molmoact2 files * skip molmoact2 tests without optional deps * fix molmoact2 pre-commit checks * validate molmoact2 gripper range |
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Khalil Meftah
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e86f5af5bf |
feat(rewards): add TOPReward reward model (#3629)
* feat(rewards): add TOPReward reward model * refactor(rewards): clean up TOPReward processor/model * fix(rewards/topreward): add missing input keys mm_token_type_ids * fix(rewards/topreward): fix pyproject extra typo and simplify processor (#3653) Add lerobot[topreward] extra to all in pyproject.toml, drop the redundant labels arg in scoring, and collapse the dead-branch shape check in the encoder processor. * optmize topreward input processing (#3660) --------- Co-authored-by: Cole <91766445+jcoleharrison@users.noreply.github.com> Co-authored-by: Haoming Song <haomingsong24@gmail.com> |
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Pepijn
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7ab4936b1b |
Add extensive language support (#3467)
* Add extensive language support * Address review: split persistent/event schemas, drop event timestamps - recipe.py: derive _VALID_ROLES/_VALID_STREAMS from MessageRole/MessageStream Literals - dataset_metadata.py: keep CODEBASE_VERSION at v3.0 - language.py: remove RESERVED_STYLES; split arrow/feature schemas into persistent (with timestamp) and event (without timestamp); add docstrings - language_render.py: events use frame-row timestamp implicitly; no per-event timestamp filtering or sorting - converters.py: drop unused subtask_key passthrough - add docstrings to new public APIs (recipe, render_messages_processor, collate) - update tests for split schemas; revert uv.lock Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * Add docstrings to all new helpers; revert uv.lock Covers private helpers in recipe.py, language.py, language_render.py, and render_messages_processor.py. Also reverts uv.lock to main (it was re-generated by `uv run` during local checks). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(language): add motion (persistent) and trace (event-only) styles Promote the previously-reserved motion/trace styles to first-class core styles. motion routes to language_persistent (it tracks robot state over time); trace routes to language_events (single-moment annotations). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(language): per-camera tagging on view-dependent styles Adds a nullable `camera` field to the language row struct (both persistent and event variants) so view-dependent styles like `vqa` can carry which `observation.images.*` view they were grounded against. Without this, multi-camera datasets ended up with multiple `(vqa, role)` rows at the same timestamp that the resolver could not disambiguate. - `language.py`: add `camera` to PERSISTENT_ROW_FIELDS / EVENT_ROW_FIELDS, to both Arrow struct types and the HF datasets feature mappings; introduce VIEW_DEPENDENT_STYLES = {vqa, motion, trace} plus `is_view_dependent_style` and `validate_camera_field` helpers (camera required iff style is view-dependent). - `language_render.py`: thread an optional `camera=` kwarg through every resolver (`active_at`, `emitted_at`, `nth_prev`, `nth_next`) and through `_matching_rows` / `_select_*`, so recipes can disambiguate per-camera VQA with `emitted_at(t, style=vqa, role=assistant, camera=...)`. Without a `camera` filter, multi-row matches keep raising the existing ambiguity error — which is the desired behaviour on multi-camera data. - `recipes/pi05_hirobot.yaml`: replace the single `ask_vqa` branch with `ask_vqa_top` and `ask_vqa_wrist` per-camera sub-recipes (each carrying the matching image block), keeping the original 0.20 budget and documenting the customization point for datasets with different cameras. - Tests: schema test asserts the new field order; new tests cover `is_view_dependent_style`, `validate_camera_field` (both required and forbidden directions), per-camera `emitted_at` filtering, and the ambiguity error when two cameras emit `(vqa, assistant)` at the same timestamp without a `camera=` filter. RenderMessagesStep + dataset passthrough fixtures updated to include the new field. - `docs/source/language_and_recipes.mdx`: document the `camera` field, the per-camera resolver pattern, and the canonical recipe convention. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(language): drop motion from VIEW_DEPENDENT_STYLES Motion primitives are described in robot-frame (joint / Cartesian) terms, not pixel space, so they are camera-agnostic. Only `vqa` (event) and `trace` (event, pixel-trajectory) are view-dependent. The `camera` field stays on PERSISTENT_ROW_FIELDS for schema symmetry — the validator, resolver, and HF feature mapping behave identically across the two columns regardless of which styles populate `camera` today — but persistent rows now always have `camera=None` in practice. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(language): task_aug style + automatic ${task} rephrasing rotation Adds task-prompt diversity (Xiao 2022 / CAST) without touching ``meta/tasks.parquet`` or forcing recipes to opt in. The plan reserved ``task_aug`` as a future style; this lands it now. - ``language.py``: add ``task_aug`` to ``CORE_STYLES`` and ``PERSISTENT_STYLES``. ``column_for_style("task_aug")`` returns ``language_persistent`` so PR 2 writers route it correctly. - ``language_render.py``: ``_resolve_task`` now consults the persistent slice for rows of ``style="task_aug", role="user"``. When any exist it picks one deterministically by ``sample_idx`` (blake2b-keyed, not Python's randomized hash) so an epoch sees every rephrasing of every episode while the same sample still resolves identically across reruns. Falls back to the canonical ``meta/tasks.parquet`` task when no rephrasings are present, so existing datasets and unannotated runs keep their behaviour. Explicit ``task=`` overrides still win. - Tests: rephrasing coverage across samples, determinism on repeat ``sample_idx``, fallback when persistent has no ``task_aug`` rows, and explicit override priority. Recipes get this for free: any ``${task}`` placeholder rotates through the available rephrasings. Recipes that want the literal canonical task can override the binding. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(language): tool catalog in meta/info.json + LeRobotDatasetMetadata.tools Stores OpenAI-style function schemas at ``meta/info.json["tools"]`` so datasets can declare which tools are available (today: just ``say``; tomorrow: per-dataset extensions). The ``DEFAULT_TOOLS`` constant fills in for unannotated datasets so chat-template consumers don't have to special-case anything. Three pieces: - ``language.py``: ``SAY_TOOL_SCHEMA`` and ``DEFAULT_TOOLS`` constants. Single source of truth — PR 2's writer and PR 3's runtime tool registry will both import from here instead of duplicating the dict. - ``dataset_metadata.py``: ``LeRobotDatasetMetadata.tools`` property reads ``info.json["tools"]`` and falls back to ``DEFAULT_TOOLS``. Returns deep-copied dicts so callers can mutate the result safely. - ``docs/source/tools.mdx``: spec page covering the catalog, per-row invocations, and the three-step "how to add a new tool" workflow (declare schema, implement, register). Linked from the docs toctree under the Datasets section. This lays the groundwork for PR 2's pipeline writing the catalog out during annotation, and PR 3's ``src/lerobot/tools/`` package shipping runnable implementations (one file per tool — first up: ``say.py`` wrapping Kyutai's pocket-tts). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * Apply ruff and prettier formatting after merge Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * refactor(language): unify resolver dispatch and prune redundant test scaffolding * Drop the unused `events` kwarg from `active_at`/`nth_prev`/`nth_next`; only `emitted_at` actually consults events. The dispatcher in `_resolve_spec` now passes events conditionally. * Replace the dual `_persistent_sort_key`/`_event_sort_key` pair with a single `_row_sort_key` and drop the `sort_key` parameter from `_select_one`. Event rows lack `timestamp` (it is implicit in the frame) and now default to `0.0` for sort purposes — the `(style, role)` tiebreaker is unchanged. * Inline `_select_latest` into `active_at` (its only caller). * Collapse `emitted_at`'s dual-branch into one `_select_one` call. * Tighten `_validate_persistent_resolver` to a single `column_for_style(style) != LANGUAGE_PERSISTENT` check. * Parameterize `test_per_camera_blend_renders_both_views` over the two cameras and factor the sub-recipe builder into `_vqa_subrecipe` so the test no longer hand-rolls two near-identical recipe blocks. Net -98 LOC; behavior, public resolver names, and test expectations unchanged. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(language): always raise on ambiguous resolver matches `_select_one` previously skipped its ambiguity check whenever any of `role`/`tool_name`/`camera` was set, on the assumption that the caller had already pinned down a unique row. That left a real ambiguity hole for VQA: with two cameras emitting `(vqa, assistant)` at the same frame, `emitted_at(..., role="assistant")` silently picked the first sorted row instead of telling the recipe to add `camera=...`. The existing `test_emitted_at_raises_on_ambiguous_per_camera_vqa` test already encoded the desired behavior. Tighten the check: any time `len(rows) > 1` we now raise with the selectors echoed back, so users see exactly which fields they passed and that more is needed to disambiguate. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore: fix CI — collapse short ValueError to one line, refresh uv.lock * `ruff format` on CI (newer version) wants the short `camera=None` ValueError on a single line. * `uv.lock` was stale relative to `pyproject.toml`'s `datasets>=4.7.0` pin (and picked up upstream `s390x` marker fixes for cuda packages). CI runs `uv sync --locked` which rejected the divergence. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(language): keep base install green — drop processor re-export, gate dataset-extra tests `lerobot.processor` re-exported `RenderMessagesStep` at the package level, so importing anything from `lerobot.processor` pulled in `lerobot.datasets.language` → `lerobot.datasets/__init__.py` → `require_package("datasets")`, which fails in the Tier 1 base install that intentionally omits the `[dataset]` extra. The chain bricked collection for unrelated suites (`tests/policies/pi0_pi05/...`, `tests/envs/...`, etc.). * Stop re-exporting `RenderMessagesStep` from `lerobot.processor`. The only consumer (the test) already imports from the submodule. Document the deliberate omission in the module docstring. * Add `pytest.importorskip("datasets", ...)` (and `pandas` where needed) at the top of the four PR-added tests that exercise the language stack: - tests/datasets/test_language.py - tests/datasets/test_language_render.py - tests/processor/test_render_messages_processor.py - tests/utils/test_collate.py Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(language): address review — tools accessor, motion docs, conditional collate * **`meta.tools` actually reads `info.json["tools"]`.** `DatasetInfo` had no `tools` field, so `from_dict` silently dropped the key (it warned about unknown fields then discarded them) and the property always returned `DEFAULT_TOOLS`. Added `tools: list[dict] | None` to the dataclass; `to_dict()` drops it when unset so existing datasets keep a clean `info.json`. Fixed the accessor to read `self.info.tools` (the previous `.get(...)` would have raised AttributeError on the dataclass anyway). Added regression tests: fallback when absent, round-trip from disk, and round-trip through `DatasetInfo.from_dict` / `to_dict`. * **`motion` is not view-dependent — fix the docs.** The mdx claimed rows of style `motion` must carry `camera`, but `VIEW_DEPENDENT_STYLES = {"vqa", "trace"}` and the validator agrees: motion primitives are joint/Cartesian-frame, not pixel-space. Updated both call-out paragraphs in `language_and_recipes.mdx`. * **Conditional `collate_fn` swap.** Added `meta.has_language_columns` and gate the `lerobot_collate_fn` swap in `lerobot_train.py` on it, so non-language datasets keep PyTorch's `default_collate`. Also added a pass-through test in `test_collate.py` that asserts on a plain tensor batch the custom collate matches `default_collate` key-for-key, plus a test for the `None`-sample drop path. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * review: dedupe regex, centralize column names, harden collate, more tests * **#2 — dedupe `_PLACEHOLDER_RE`.** The same regex was compiled in `recipe.py` and `language_render.py`. Promote to module-level `PLACEHOLDER_RE` in `recipe.py` (its primary owner — declares template syntax) and import from `language_render.py`. * **#3 — centralize language column names.** `io_utils.py` had hardcoded `{"language_persistent", "language_events"}` literals at two sites. Replace with `LANGUAGE_COLUMNS` import so a future column rename can't silently desync. * **#4 — defensive collate preserved-keys.** `lerobot_collate_fn` silently filtered language fields from samples that didn't have them, which would hand downstream consumers a preserved list shorter than the tensor batch. Now: if any sample carries a key, every sample in the batch must carry it; otherwise raise a `ValueError` so the upstream rendering bug surfaces at the boundary. * **#5 — `_scalar` rejects non-singleton lists.** Previously a zero- or multi-element list fell through and triggered confusing `float([])` errors downstream. Now raises `ValueError` with the actual length. * **#6 — refactor `_extract_complementary_data`.** Replace 11 lines of `key = {... if ... else {}}` plus an 11-line splat dict with a single `_COMPLEMENTARY_KEYS` tuple iterated once. * **#7 — document `EXTENDED_STYLES`.** Was an empty `set()` with no comment. Add a docstring explaining it's an intentional extension point: downstream modules append project-local styles before `column_for_style` is called. * **#9 — `tools.mdx` notes the runtime layer is future work.** The page referenced `src/lerobot/tools/`, `registry.py`, and `get_tools(meta)` — none exist in this PR. Added a callout at the start of "How to add your own tool" plus a note on the implementations paragraph. * **#10 — tests for YAML round-trip, malformed rows, blend validation.** `test_recipe.py` grew from 1 case to 12 covering: blend-or-messages exclusivity, target-turn requirement, blend emptiness, weight presence/positivity, nested-blend rejection, `from_dict` with nested blends, `from_yaml` / `load_recipe` agreement, top-level non-mapping rejection. Added a malformed-row test for `_normalize_rows` that asserts non-dict entries raise `TypeError`. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * review: emitted_at uses 0.1s tolerance; MessageTurn requires stream at construction * **Float tolerance in `emitted_at` for persistent styles.** The ``_timestamp(row) == t`` exact-equality check silently missed any caller that derived ``t`` arithmetically (e.g. ``frame_idx / fps``) even though the parquet timestamp would only differ by ULPs. Added ``EMITTED_AT_TOLERANCE_S = 0.1`` and check ``abs(...) <= tolerance`` instead, with a docstring explaining why exact equality wasn't enough and why 0.1 s is safe at typical 30–100 Hz control rates. Test asserts the new behavior at half-window (matches) and double-window (no match) using the constant so it stays in sync. * **`MessageTurn.stream` is required at construction.** It was typed ``MessageStream | None = None`` so YAML could omit ``stream:`` and pass the dataclass invariant — but ``_validate_rendered`` rejected ``None`` streams later, surfacing the error at the first sample instead of at recipe load. Now ``__post_init__`` raises ``ValueError`` if ``stream`` is ``None``, with the list of valid streams in the message. The redundant late-stage check in ``_validate_rendered`` is replaced with a one-line comment that cites the upstream invariant. Test pins the new construction-time rejection. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs(tools): drop follow-up-PR references Reword the two callouts in `tools.mdx` to describe the runtime layer in present tense ("not part of the catalog layer shipped today", "those modules don't yet exist in the tree") instead of pointing at a specific follow-up PR. Keeps the doc honest about what works now without coupling it to a particular release order. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * review: address CarolinePascal feedback - language timestamps: float64 -> float32 to match LeRobotDataset frame timestamps (Arrow struct + HF feature) - dataset_metadata: hoist `.language` imports to module top — language.py has no lerobot imports, so there is no circular-import risk - dataset_metadata: add a `meta.tools` setter that persists the catalog to info.json and reloads `meta.info` - feature_utils: validate the `language` dtype instead of returning "" — warn (non-fatal) when a non-empty value is written at record time - centralize the scalar-unwrap helper as `lerobot.utils.utils.unwrap_scalar`, shared by render_messages_processor and language_render - docs: move `## Layer 2 — recipe anatomy` ahead of the resolver sections, which describe recipe bindings rather than dataset layout - language_render: note in EMITTED_AT_TOLERANCE_S that persistent rows change on a human-action timescale, not the camera frame rate Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
|
3c15fd8537 |
feat(robots): natively integrate Seeed Studio reBot B601-DM arm (#3624)
* feat(robots): natively integrate Seeed Studio reBot B601-DM arm Add first-class LeRobot support for the Seeed Studio reBot arm, replacing the out-of-tree `lerobot-robot-seeed-b601` / `lerobot-teleoperator-rebot-arm-102` plugin packages. New devices: - robot `rebot_b601_follower` — single-arm B601-DM follower (6-DOF + gripper, Damiao CAN motors via `motorbridge`) - robot `bi_rebot_b601_follower` — bimanual follower composing two single arms - teleoperator `rebot_102_leader` — single-arm StarArm102 / reBot Arm 102 leader (FashionStar UART servos via `motorbridge-smart-servo`) - teleoperator `bi_rebot_102_leader` — bimanual leader composing two single arms The bimanual variants reuse the single-arm classes and namespace each arm's observation/action keys with `left_` / `right_` prefixes, so a bimanual StarArm102 leader can teleoperate a bimanual reBot B601 follower. Optional SDK imports are guarded; a `rebot` extra installs `motorbridge` and `motorbridge-smart-servo`. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: add reBot B601-DM calibration & dual-arm teleoperation guide Add docs/source/rebot_b601.mdx covering single-arm and bimanual calibration and teleoperation for the reBot B601-DM follower and reBot Arm 102 leader, with zero-position reference images from the Seeed Studio wiki. Register the page in the docs toctree. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: fix reBot B601 MDX build (move JSON example out of <Tip>) The doc-builder parses `{...}` inside MDX component children as a Svelte expression, so the joint_directions JSON example broke the build. Move it into a top-level fenced code block. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: apply prettier formatting to reBot B601 page Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: remove duplicate colocated reBot B601 page docs/source/rebot_b601.mdx is the canonical, toctree-registered page; the colocated rebot_b601.md was a redundant thinner copy. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: clarify 6-DOF leader fallback comment in reBot B601 follower Explain that holding wrist_yaw at zero is what lets a 6-DOF leader (e.g. so100_leader / so101_leader) teleoperate the 7-DOF follower. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * refactor: address Caroline's PR review on reBot B601 integration - leader: remove _validate_config (no other lerobot device validates its config; a key mismatch now surfaces as a plain KeyError) - leader: simplify _round_to_valid_range to direct modular arithmetic instead of a bidirectional search loop - leader: inline the single-use _clamp helper - follower & leader: write MotorCalibration range_min/range_max from the configured joint_limits / joint_ranges instead of a fixed [-90, 90] - docs: add a "Find the USB ports" section (lerobot-find-port) and move the brltty/permissions tip there; link the OpenArm page for SocketCAN adapter configuration Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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|
Caroline Pascal
|
bd9619dfc3 |
feat(encoding parameters): adding support for user provided video encoding parameters (#3455)
* chore(video backend): renaming codec into video_backend in get_safe_default_video_backend() * feat(pyav utils): adding suport for PyAV encoding parameters validation * feat(VideoEncoderConfig): creating a VideoEncoderConfig to encapsulate encoding parameters * feat(VideoEncoderConfig): propagating the VideoEncoderConfig in the codebase * chore(docs): updating the docs * feat(metadata): adding encoding parameters in dataset metadata * fix(concatenation compatibility): adding compatibility check when concatenating video files * feat(VideoEncoderConfig init): making VideoEncoderConfig more robust and adaptable to multiple backends * feat(pyav checks): making pyav parameters checks more robust * chore(duplicate): removing duplicate get_codec_options definition * test(existing): adapting existing tests * test(new): adding new tests for encoding related features * chore(format): fixing formatting issues * chore(PyAV): cleaning up PyAV utils and encoding parameters checks to stick to the minimun required tooling. * chore(format): formatting code * chore(doctrings): updating docstrings * fix(camera_encoder_config): Removing camera_encoder_config from LeRobotDataset, as it's only required in LeRobotDatasetWriter. * feat(default values): applying a consistent naming convention for default RGB cameras video encoder parameters * fix(rollout): propagating VideoEncoderConfig to the latest recording modes * chore(format): formatting code, fixing error messages and variable names * fix(arguments order): reverting changes in arguments order in StreamingVideoEncoder * chore(relative imports): switching to relative local imports within lerobot.datasets * test(artifacts): cleaning up artifacts for the video encoding tests * chore(docs): updating docs * chore(fromat): formatting code * fix(imports): refactoring the file architecture to avoid circular imports. VideoEncoderConfig is now defined in lerobot.configs and lazily imports av at runtime. * fix(typos): fixing typos and small mistakes * test(factories): updating factories * feat(aggregate): updating dataset aggregation procedure. Encoding tuning paramters (crf, g,...) are ignored for validation and changed to None in the aggregated dataset if incompatible. * docs(typos): fixing typos * fix(deletion): reverting unwanted deletion * fix(typos): fixing multiple typos * feat(codec options): passing codec options to lerobot_edit_dataset episode deletion tool * typo(typo): typo * fix(typos): fixing remaining typos * chore(rename): renaming camera_encoder_config to camera_encoder * docs(clean): cleaning and formating docs * docs(dataset): addind details about datasets * chore(format): formatting code * docs(warning): adding warning regarding encoding parameters modification * fix(re-encoding): removing inconsistent re-encoding option in lerobot_edit_dataset * typos(typos): typos * chore(format): resolving prettier issues * fix(h264_nvenc): fixing crf handling for h264_nvenc * docs(clean): removing too technical parts of the docs * fix(imports): fixing imports at the __init__ level * fix(imports): fixing not very pretty imports in video config file |
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Nikodem Bartnik
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0a4a7c40ad |
docs(cheat sheet): create cheat sheet (#3602)
* add comprehensive CLI cheat sheet for quick reference |
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Steven Palma
|
b607c8458e |
docs: add policy & compute guide (#3534)
* docs(policy): contributing a policy guide * docs(training): HW compute guide * chore(docs): add to readme and index * Apply suggestions from code review Co-authored-by: Haoming Song <1847575517@qq.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * chore(docs): slight improvements * refactor(docs): consolidate add policy docs * chore(style): fix pre-commit --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Haoming Song <1847575517@qq.com> |
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Haoming Song
|
e99c55af4b |
feat(policies): add EO-1 model (#3403)
* feat(policies): add EO-1 model * chore(eo1): adjust policy_eo1_README.md to to avoid duplicate with eo1.mdx * chore(eo1): remove policy_eo1_README.md, link eo1.mdx in policy folder --------- Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> |
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Steven Palma
|
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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Pepijn
|
5adad11128 |
feat(sim): VLABench benchmark integration (#3396)
feat(sim): add VLABench benchmark integration Add VLABench as a new simulation benchmark in LeRobot, following the existing LIBERO and MetaWorld patterns. This PR wires VLABench end-to-end across environment integration, Docker setup, CI smoke evaluation, and documentation. It also fixes a number of upstream packaging and runtime issues required to make VLABench usable and reproducible in CI. What’s included Benchmark integration Add VLABench as a new simulation benchmark. Expose supported VLABench tasks through the LeRobot env interface. Follow the established LIBERO / MetaWorld factory patterns. Preserve lazy async-env metadata so env.unwrapped.metadata["render_fps"] continues to work. CI smoke evaluation Add a VLABench smoke-eval job using lerobot/smolvla_vlabench. Use the correct rename_map for the 3-camera dataset layout. Expand smoke coverage from 1 to 10 primitive tasks. Extract task descriptions after eval so metrics artifacts include per-task labels. Skip Docker Hub login when secrets are unavailable (e.g. fork PRs). Docker / install fixes Install VLABench from GitHub rather than PyPI. Use uv pip, not pip, in the base image. Fail loudly on install errors instead of masking them. Clone VLABench into the non-root user’s home directory. Use shallow editable installs for VLABench and rrt-algorithms to work around missing __init__.py issues. Pin upstream clones to exact commit SHAs for reproducibility. Add undeclared runtime dependencies required by VLABench (open3d, colorlog, scikit-learn, openai). Unpin open3d so Python 3.12 wheels resolve. Assets Support downloading VLABench assets from a Hugging Face Hub mirror via VLABENCH_ASSETS_REPO. Keep Google Drive download support as fallback. Install huggingface_hub[hf_xet] so Xet-backed assets download correctly. Validate required mesh/XML asset subtrees at build time. Patch VLABench constants to tolerate missing asset directories at import time. Runtime / env correctness Import VLABench robots and tasks explicitly so decorator-based registry population happens. Resize and normalize camera observations so they always match the declared (H, W, 3) uint8 observation space. Reinstall LeRobot editably inside the image so the new env code is actually used. Coerce agent_pos / ee_state to the expected shape. Pad actions when needed to match data.ctrl. Replace zero-padding fallback with proper dm_control IK for 7D end-effector actions. Refetch dm_control physics on each step instead of caching weakrefs. Retry unstable resets with reseeding and handle PhysicsError gracefully at step time. Dataset / policy alignment Align VLABench observations and actions with Hugging Face dataset conventions used by lerobot/vlabench_unified: convert EE position between world frame and robot-base frame at the env boundary, expose / consume Euler XYZ instead of raw quaternion layout, align gripper semantics with dataset convention (1 = open, 0 = closed). This fixes policy/env mismatches that previously caused incorrect IK targets and unstable behavior at evaluation time. Docs Add a full docs/source/vlabench.mdx page aligned with the standard benchmark template. Document task selection forms (single task, comma list, suite shortcut). Document installation, evaluation, training, and result reproduction. Point examples at lerobot/smolvla_vlabench. Add a benchmark banner image. Remove outdated / misleading references to upstream evaluation tracks. Document manual install flow instead of a broken vlabench extra. Packaging cleanup Remove the unresolvable vlabench extra from pyproject.toml. Remove the no-op VLABench processor step. Remove the obsolete env unit test that only covered the dropped gripper remap helper. Apply formatting / logging / style cleanup from review feedback. Why this is needed VLABench is not currently consumable as a normal Python dependency and requires several upstream workarounds: no PyPI release, missing package declarations, undeclared runtime deps, SSH-only submodule references, asset downloads outside normal package install flow, registry population that depends on import side effects, env outputs that do not always match declared observation shapes, task resets that can diverge under some random layouts. This PR makes the benchmark usable in LeRobot despite those constraints, and ensures CI runs are reproducible and informative. If you want a much shorter squash commit message, I’d use this: feat(sim): integrate VLABench benchmark with CI, Docker, and docs Add VLABench as a new LeRobot simulation benchmark, following the existing LIBERO / MetaWorld patterns. This includes: LeRobot env integration and task exposure, CI smoke eval with lerobot/smolvla_vlabench, Docker install and asset-download fixes, runtime fixes for registry loading, assets, camera obs, action handling, dm_control IK, and PhysicsError recovery, alignment of obs/action semantics with HF VLABench datasets, docs and packaging cleanup. The PR also incorporates review feedback, improves reproducibility by pinning upstream commits, and makes VLABench usable in CI despite upstream packaging and asset-management issues. |
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Pepijn
|
a07f22e22c |
feat(envs): add LIBERO-plus robustness benchmark (#3313)
* feat(envs): add LIBERO-plus robustness benchmark integration
- LiberoPlusEnv config (subclass of LiberoEnv, same gym interface)
- Docker image installing LIBERO-plus fork via PYTHONPATH
- CI workflow: 1-episode smoke eval with pepijn223/smolvla_libero_plus
- pyproject.toml: libero_plus extra
* fix(libero): use suite's perturbation-aware init_states loader
LIBERO-plus's Benchmark class exposes a `get_task_init_states(i)` method that
strips perturbation suffixes (`_table_N`, `_tb_N`, `_view_`, `_language_`,
`_light_`, `_add_`, `_level`) and loads the underlying base `.pruned_init`
file — the on-disk name for a perturbation variant doesn't exist as a file,
only the base does. lerobot's loader was bypassing that logic and trying to
read the suffix-bearing filename directly, which failed for every non-zero
task id and killed the eval before any rollout video could be written.
Delegate to the suite's method when it exists; fall back to the path-based
loader for vanilla LIBERO (which does not provide the method).
Also drop the hf-libero install + init_files copy from the LIBERO-plus
Dockerfile — the LIBERO-plus clone already ships both `bddl_files/` and
`init_files/` for all five suites, so the copy was unnecessary and the
`cp -r` into an existing dir produced a confusing nested layout.
* fix(libero): resolve LIBERO-plus perturbation init_states path ourselves
Delegating to `task_suite.get_task_init_states(i)` works for path resolution
but LIBERO-plus's method calls `torch.load(path)` without `weights_only=False`,
which fails on PyTorch 2.6+ because the pickled init_states contains numpy
objects not in the default allowlist:
_pickle.UnpicklingError: Weights only load failed.
WeightsUnpickler error: Unsupported global:
GLOBAL numpy.core.multiarray._reconstruct was not an allowed global.
Mirror LIBERO-plus's suffix-stripping logic (`_table_N`, `_tb_N`, `_view_`,
`_language_`, `_light_`, `_add_`, `_level`) in our own helper so we can pass
`weights_only=False` ourselves. Vanilla LIBERO task names don't contain any
of these patterns except for `_table_` when followed by the word `center`
(e.g. `pick_up_the_black_bowl_from_table_center_...`), and the regex
requires `_table_\\d+` so semantic uses are preserved.
* fix(libero-plus): download perturbation assets from Sylvest/LIBERO-plus
LIBERO-plus's bddl_base_domain.py resolves scene XMLs with
`os.path.join(DIR_PATH, "../assets")`, so the `assets` key in config.yaml
has no effect on scene lookup — MuJoCo always opens
`<clone>/libero/libero/assets/scenes/...`. With no such directory present,
every perturbation task fails on:
FileNotFoundError: No such file or directory:
.../libero-plus/libero/libero/assets/scenes/tabletop_table_Cobblestone01_GLOSS_6K.xml
These textures, views, and extra objects ship only in the 6.4 GB `assets.zip`
published at `Sylvest/LIBERO-plus` (the LIBERO-plus README explicitly says
to download and unzip it into the package dir). Fetch it via `hf_hub_download`,
unzip into `${LIBERO_PLUS_ROOT}/`, install `unzip`, and point config.yaml at
the extracted dir so everything stays consistent. The download lives in its
own Docker layer so subsequent rebuilds reuse the cached assets.
Drops the lerobot/libero-assets snapshot_download — that mirror only has
vanilla LIBERO textures and is ignored for scene loading anyway.
* fix(libero-plus): flatten deep path prefix from Sylvest/LIBERO-plus assets.zip
The 6.4 GB zip ships with every entry prefixed by
`inspire/hdd/project/embodied-multimodality/public/syfei/libero_new/release/dataset/LIBERO-plus-0/assets/...`
(the author's internal filesystem layout, not the layout the LIBERO-plus
README promises), so the previous `unzip -d ${LIBERO_PLUS_ROOT}/` created
`${LIBERO_PLUS_ROOT}/inspire/.../assets/` — robosuite still opened
`${LIBERO_PLUS_ROOT}/assets/scenes/tabletop_table_Cobblestone01_GLOSS_6K.xml`
and hit the same FileNotFoundError.
Extract to a scratch dir, then `mv` the nested `assets/` subtree to the
expected location. Verified the target file exists in the zip central
directory under that exact prefix.
* refactor(libero): inline init_states resolver behind single regex
Collapse the three-style suffix stripper (split/re.sub/in) into one
compiled regex, drop the (Path, bool) tuple return, and move the
`_add_`/`_level` reshape branch into the caller so each branch loads
its own file and returns directly. Net: -11 lines, one fewer helper.
* refactor(libero-plus): rebase docker image on huggingface/lerobot-gpu
Mirror the libero/metaworld/robomme pattern: start from the nightly GPU
image (apt deps, python, uv, venv, lerobot[all] already there) and only
layer on what LIBERO-plus uniquely needs — its wand/ImageMagick build
deps, the non-extra runtime pips (robosuite==1.4.1, bddl, …), the
PYTHONPATH-shadowed fork, and the 6.4 GB assets.zip.
Drops ~50 lines of duplicated base setup (CUDA FROM, apt python, uv
install, user creation, venv init) the nightly already provides.
123 → 73 lines.
Also:
- Add libero_plus to docs/source/_toctree.yml under Benchmarks so
doc-builder's TOC integrity check stops failing.
- Repoint the docs dataset link from pepijn223/libero_plus_lerobot to
the canonical lerobot/libero_plus.
- Revert the stray uv.lock churn (revision/marker diff that crept in
from an unrelated resolve — unrelated to LIBERO-plus).
* fix(libero-plus): stop touching pyproject + uv.lock
The fast-tests job was rejecting the branch because pyproject.toml had a
[libero_plus] extra whose git dep wasn't represented in uv.lock.
The Docker image no longer needs the extra — it clones LIBERO-plus
directly and PYTHONPATH-shadows hf-libero. Drop [libero_plus] from
pyproject and restore pyproject.toml + uv.lock to exactly what's on
origin/main, so `uv sync --locked --extra test` is a no-op for this PR.
Also repoint the doc/CI/env comments that still mentioned the extra at
the Docker install path.
* fix(libero-plus): strip perturbation metadata from task descriptions
LIBERO-plus builds task.language by space-joining the perturbation-variant
filename, so every non-_language_ variant inherits a trailing blob like
"view 0 0 100 0 0 initstate 0 noise 45" or "add 16". That shows up in the
dashboard video labels and no longer matches the base instruction stored
in the training dataset.
Strip those tokens in extract_task_descriptions.py with an end-anchored
regex over the {view,initstate,noise,add,tb,table,light,level}(+digits)
vocabulary. The anchor preserves mid-sentence literal uses of those words
(e.g. "from table center and place it on the plate") — only the trailing
metadata chain is removed. _language_ variants carry real BDDL-sourced
text and are left untouched.
* 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 #3313 review feedback
- docs: fix paper link to arxiv, add benchmark image, add suite descriptions,
add LIBERO-plus replacement warning, restructure eval section to match
LIBERO doc style, fix policy I/O section, remove false try/except claim
- docker: fix shell grouping for hf-libero uninstall, replace hardcoded
asset path with dynamic find
- ci: add Docker Hub login step, add HF_USER_TOKEN guard on eval step
- envs: add is_libero_plus param to get_task_init_states so vanilla LIBERO
always takes the simple path
* fix(docs): use correct LIBERO-plus teaser image URL
* ci(libero-plus): drop redundant hf auth login step
The standalone login step ran `hf auth login` in a throwaway
`docker run --rm` container, so no credentials persisted. Auth is
already performed inside the eval step's container. Removing the
redundant step per PR #3313 review feedback.
* fix(envs): preserve AsyncVectorEnv metadata/unwrapped in lazy eval envs
Port of #3416 onto this branch. Without these attributes eval crashes
when calling `env.unwrapped.metadata["render_fps"]` with async vector
envs. Adds `metadata` / `unwrapped` to `_LazyAsyncVectorEnv` and
caches the metadata alongside obs/action spaces in the LIBERO and
MetaWorld factories.
* 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 before any of
the actual build/eval work could run. Gate the login on the env-var
expansion of the username so the step is skipped (not failed) when
secrets are absent. Mirrors the existing pattern in the VLABench job.
* Update .github/workflows/benchmark_tests.yml
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* Update scripts/ci/extract_task_descriptions.py
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* Update .github/workflows/benchmark_tests.yml
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* Update docker/Dockerfile.benchmark.libero_plus
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* Update .github/workflows/benchmark_tests.yml
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* fix(libero-plus): address review feedback
* ci(libero-plus): fix YAML indentation in upload-artifact steps
The `uses:` key on two upload-artifact steps was at column 0 instead
of nested under the step, causing `pre-commit run check-yaml` to fail
with "expected <block end>, but found '<block mapping start>'".
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
|
||
|
Pepijn
|
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
|
a147fa4439 |
feat(envs): add RoboCerebra long-horizon manipulation benchmark (#3314)
* feat(ci): add RoboCerebra benchmark eval job - Docker image with robosuite/libero deps for RoboCerebra eval - CI workflow: 1-episode eval with pepijn223/smolvla_robocerebra - Reuses libero env with rename_map + empty_cameras=3 * docs(robocerebra): add benchmark page and toctree entry Add a dedicated docs page for RoboCerebra that points at the canonical dataset lerobot/robocerebra_unified and shows how to run eval + fine-tune against it. Wire it into the Benchmarks section of the toctree so doc-builder picks it up. * 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. * fix(robocerebra): drop alias extra + simplify docker image Two problems rolled up: 1. `uv sync --locked --extra test` was failing because pyproject.toml added a `robocerebra = ["lerobot[libero]"]` alias extra but uv.lock wasn't regenerated. Drop the alias — nothing in CI actually needs the extra name (the Dockerfile just installs what it needs directly), so this restores pyproject.toml and uv.lock to byte-exact origin/main. 2. Rebase docker/Dockerfile.benchmark.robocerebra on huggingface/lerobot-gpu:latest (same pattern as libero/metaworld/…). The nightly image already ships lerobot[all] which includes [libero], so the RoboCerebra image is essentially identical to the LIBERO one: fetch libero-assets, write ~/.libero/config.yaml, overlay source. 92 → 43 lines. Also repoint the CI workflow comment that referenced the removed extra. * ci: use dedicated lerobot/smolvla_robocerebra checkpoint for smoke eval Replace the generic pepijn223/smolvla_libero placeholder with the purpose-trained lerobot/smolvla_robocerebra model in the RoboCerebra CI smoke test. * fix(ci): align RoboCerebra eval with training pipeline Fixes 5 mismatches that caused 0% success rate: - env.type: robocerebra (unregistered) → libero - resolution: 360x360 (default) → 256x256 (matches dataset) - camera_name_mapping: map eye_in_hand → wrist_image (not image2) - empty_cameras: 3 → 1 (matches training) - add HF_USER_TOKEN guard on eval step * fix(ci): set env.fps=20 and explicit obs_type for RoboCerebra eval Match the dataset's 20 FPS (LiberoEnv defaults to 30) and make obs_type=pixels_agent_pos explicit for safety against future changes. * docs(robocerebra): align page with adding_benchmarks template Rework docs/source/robocerebra.mdx to follow the standard benchmark doc structure: intro + links + available tasks + installation + eval + recommended episodes + policy I/O + training + reproducing results. - Point everything at lerobot/smolvla_robocerebra (the released checkpoint), not the personal pepijn223 mirror. - Add the --env.fps=20 and --env.obs_type=pixels_agent_pos flags that CI actually uses, so copy-paste eval reproduces CI. - Split the "Training" block out of the recipe section into its own section with the feature table. - Add an explicit "Reproducing published results" section pointing at the CI smoke eval. * fix: integrate PR #3314 review feedback - ci(robocerebra): drop redundant hf auth login step (auth is already performed inside the eval step's container). - ci(robocerebra): add Docker Hub login before the image build to pick up the authenticated rate limit. - docs(robocerebra): align eval snippet with the CI command (observation size, camera_name_mapping, use_async_envs, device, empty_cameras=1). * fix(envs): preserve AsyncVectorEnv metadata/unwrapped in lazy eval envs Port of #3416 onto this branch. * ci: gate Docker Hub login on secret availability * Update .github/workflows/benchmark_tests.yml Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update .github/workflows/benchmark_tests.yml Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> |
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|
Pepijn
|
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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|
Pepijn
|
e699e52388 |
feat(envs): add RoboCasa365 benchmark integration (#3375)
* feat(envs): add RoboCasa365 benchmark integration Add RoboCasa365 (arXiv:2603.04356) as a new simulation benchmark with 365 everyday kitchen manipulation tasks across 2,500 diverse environments. New files: - src/lerobot/envs/robocasa.py: gym.Env wrapper with deferred env creation, flat 12D action / 16D state vectors, 3-camera support - docs/source/robocasa.mdx: user-facing documentation - docker/Dockerfile.benchmark.robocasa: CI benchmark image Modified files: - src/lerobot/envs/configs.py: RoboCasaEnv config (--env.type=robocasa) - pyproject.toml: robocasa optional dependency group - docs/source/_toctree.yml: sidebar entry - .github/workflows/benchmark_tests.yml: integration test job Refs: https://arxiv.org/abs/2603.04356, https://robocasa.ai Related: huggingface/lerobot#321 * fix(docker): use uv pip to install robocasa in benchmark image The huggingface/lerobot-gpu base image uses `uv` with a venv at /lerobot/.venv — `pip` is not on PATH, so `pip install` fails with "pip: not found". Switch to `uv pip install` which installs into the existing venv. Also drop the @v1.0.0 tag pin from the robocasa git URL since the upstream repo may not have that tag; use default branch instead. * fix(robocasa): editable install + switch to lerobot/smolvla_robocasa - pip install from git omits data files like box_links_assets.json (not declared in package_data). Clone and install editable so the source tree is used at runtime. - Download only tex + fixtures_lw asset types (smoke test doesn't need objaverse/aigen objects). Pipe 'y' to auto-accept download prompt. - Switch CI policy from pepijn223/smolvla_robocasa to lerobot/smolvla_robocasa. * fix(docker): re-install lerobot editably after COPY The nightly huggingface/lerobot-gpu image predates the RoboCasaEnv registration — so `lerobot-eval --env.type=robocasa` fails at argparse with "invalid choice" even after COPY . . overlays the new source. Force an editable reinstall so the venv picks up the current configs.py. * fix(ci): add rename_map for robocasa eval (image* -> camera*) Policy lerobot/smolvla_robocasa expects observation.images.camera1/2/3, but RoboCasaEnv produces observation.images.image/image2/image3. * fix(robocasa): override RoboCasaGymEnv default split (test -> all) RoboCasaGymEnv defaults split="test", but create_env only accepts {None, "all", "pretrain", "target"}, so the out-of-the-box default crashes with ValueError. Always pass "all" when split is None. * fix(docker): also download objs_lw (lightwheel objects) for robocasa Kitchen tasks (e.g. CloseFridge) reference lightwheel object meshes like Stool022/model.xml. fixtures_lw alone isn't enough — we also need objs_lw. Still skipping objaverse/aigen to keep image size down. Made-with: Cursor * feat(robocasa): raw camera names + benchmark-group task shortcuts Align the LeRobot env with RoboCasa's native conventions so policies trained on the upstream datasets don't need a --rename_map at eval time, and expose the standard task groups as first-class --env.task values. - Preserve raw RoboCasa camera names (e.g. robot0_agentview_left) as observation.images.<name> end-to-end. Drops camera_name_mapping and DEFAULT_CAMERA_NAME_MAPPING; features/features_map are now built dynamically from the parsed camera list. - Accept benchmark-group names as --env.task: atomic_seen, composite_seen, composite_unseen, pretrain50/100/200/300. Expanded lazily via robocasa.utils.dataset_registry and auto-sets the split ("target" | "pretrain"). - Update CI smoke-eval rename_map to map raw cam names to the camera1/2/3 keys expected by lerobot/smolvla_robocasa. * docs(robocasa): single-task smolvla train+eval recipe on pepijn223/robocasa_CloseFridge - Rewrite observation section to use raw RoboCasa camera keys (observation.images.robot0_agentview_{left,right}, observation.images.robot0_eye_in_hand). - Add a "Training on a single task" section with a full smolvla training command on pepijn223/robocasa_CloseFridge, plus matching single-task eval command. - Document benchmark-group task shortcuts (atomic_seen, composite_seen, composite_unseen, pretrain50/100/200/300) as valid --env.task values. * fix(robocasa): restrict obj_registries to lightwheel by default CloseFridge (and most kitchen tasks) crashed at reset with `ValueError: Probabilities contain NaN` coming out of `sample_kitchen_object_helper`. RoboCasa's upstream default `obj_registries=("objaverse", "lightwheel")` normalizes per-registry candidate counts as probabilities; when a sampled category has zero mjcf paths in every configured registry (because the objaverse asset pack isn't on disk — ~30GB, skipped by our Docker build), the 0/0 divide yields NaNs and `rng.choice` raises. - Add `obj_registries: list[str] = ["lightwheel"]` to `RoboCasaEnv` config; thread it through `create_robocasa_envs`, `_make_env_fns`, and the gym.Env wrapper to the underlying `RoboCasaGymEnv` (which forwards to `create_env` → `robosuite.make` → kitchen env). - Default matches what `download_kitchen_assets --type objs_lw` actually ships, so the env works out of the box without a 30GB objaverse download. - Document the override (`--env.obj_registries='[objaverse,lightwheel]'`) for users who have downloaded the full asset set. * fix(docker): also download tex_generative for robocasa benchmark RoboCasa's lightwheel kitchen fixtures embed references to `generative_textures/wall/tex*.png` directly in their MuJoCo XML, so `MjModel.from_xml_string` errors out at reset time with "No such file or directory" even when the env is constructed with `generative_textures=None`. The generative textures live under a separate asset registry key (`tex_generative`) in `download_kitchen_assets`, distinct from the base `tex` pack we were already fetching. - Add `tex_generative` to the download list so the fixture XMLs resolve. - Document the remaining omissions (objaverse/aigen, ~30GB) and how the runtime side pairs this with obj_registries=["lightwheel"] to avoid sampling from categories whose assets aren't on disk. * ci(robocasa): smoke-eval 10 atomic tasks instead of 1 Broader coverage in the benchmark CI job: evaluate SmolVLA on ten fixture-centric atomic RoboCasa tasks (one episode each) instead of just CloseFridge. The tasks are all drawn from TARGET_TASKS.atomic_seen and selected to avoid object-manipulation categories that would require the objaverse/aigen asset packs (we only ship objs_lw in the Docker image, paired with obj_registries=["lightwheel"] on the runtime side). Tasks: CloseFridge, OpenCabinet, OpenDrawer, TurnOnMicrowave, TurnOffStove, CloseToasterOvenDoor, SlideDishwasherRack, TurnOnSinkFaucet, NavigateKitchen, TurnOnElectricKettle. `scripts/ci/parse_eval_metrics.py` already handles multi-task output via the `overall` key, so no parser changes needed. Bumped the metrics artifact's task label to `atomic_smoke_10` to reflect the grouping. * fix(pyproject): drop unresolvable robocasa extra robocasa's upstream setup.py hardcodes `lerobot==0.3.3` in install_requires. Exposing it as the `lerobot[robocasa]` extra made uv's dep resolver cycle: `lerobot[robocasa]` -> robocasa -> lerobot (a different version) -> unsolvable. This broke every `uv sync` — even invocations with an unrelated extra like `--extra test` — because uv validates the whole lockfile graph. - Remove the `robocasa` extra from pyproject.toml. Installation instructions in docs/source/robocasa.mdx now walk users through the manual `git clone` + `pip install --no-deps` flow, which matches what the Docker image already does and sidesteps the cyclic dep entirely. - Dockerfile: `uv pip install -e ~/robocasa --no-deps` so the shadowed lerobot==0.3.3 never lands in the image; install robocasa's actual runtime deps (numpy, numba, scipy, mujoco, tianshou, etc.) explicitly. * docs(robocasa): align page with adding_benchmarks template Rework docs/source/robocasa.mdx to follow the standard benchmark doc structure: intro + links + available tasks (with family breakdown and first-class benchmark-group shortcuts) + installation + eval + recommended episodes + policy I/O + training + reproducing results. - Fix the paper link (was pointing at a non-existent arxiv ID). - Surface lerobot/smolvla_robocasa and pepijn223/robocasa_CloseFridge in the top-of-page links so they're findable without reading the training section. - Add an explicit "Object registries" subsection explaining the `--env.obj_registries=[objaverse,lightwheel]` override path. - Add an explicit "Reproducing published results" section pointing at the CI smoke eval. * fix: integrate PR #3375 review feedback - envs(robocasa): hoist the duplicated `_parse_camera_names` helper out of `libero.py` and `robocasa.py` into `envs/utils.py` as the public `parse_camera_names`; call sites updated. - envs(robocasa): give each factory a distinct `episode_index` (`0..n_envs-1`) and derive a per-worker seed series in `reset()` so n_envs workers don't all roll the same scene under a shared outer seed. - envs(robocasa): drop the unused `**kwargs` on `_make_env`; declare `visualization_height` / `visualization_width` on both the wrapper and the `RoboCasaEnv` config + propagate via `gym_kwargs`. - envs(robocasa): emit `info["final_info"]` on termination (matching MetaWorld) so downstream vector-env auto-reset keeps the terminal task/success flags. - docs(robocasa): add `--rename_map` (robot0_agentview_left/ eye_in_hand/agentview_right → camera1/2/3) plus CI-parity flags to all three eval snippets. - docker(robocasa): pin robocasa + robosuite git SHAs and the pip dep versions (pygame, Pillow, opencv-python, pyyaml, pynput, tqdm, termcolor, imageio, h5py, lxml, hidapi, gymnasium) for reproducible benchmark images. - ci(robocasa): update the workflow comment — there is no `lerobot[robocasa]` extra; robocasa/robosuite are installed manually because upstream's `lerobot==0.3.3` pin shadows ours. * docs(robocasa): add benchmark banner image * fix(envs): preserve AsyncVectorEnv metadata/unwrapped in lazy eval envs Port of #3416 onto this branch. Also threads the cached metadata through the RoboCasa factory so async eval on `--env.type=robocasa` keeps the same improvement. * fix: integrate PR #3375 review feedback (round 2) - envs(robocasa): when the caller passes `seed=None` to `reset()`, fall back to `self.episode_index` for the inner env seed so each worker still samples a distinct trajectory instead of all workers inheriting the same global RNG state. - envs(robocasa): replace the two module-level `print()` calls in `create_robocasa_envs` with `logger.info(...)` via a module-level `logger = logging.getLogger(__name__)`. - ci(robocasa): run `scripts/ci/extract_task_descriptions.py` after the eval so `metrics.json` carries per-task natural-language labels, matching LIBERO / MetaWorld / VLABench jobs. Added a `_robocasa_descriptions()` extractor that splits CamelCase task names into word-level labels keyed by `<task>_0`. |
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Pepijn
|
4dbbcca496 |
docs(benchmarks): add benchmark integration guide and standardize benchmark docs (#3270)
* 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 * 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 * Update docs/source/adding_benchmarks.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update docs/source/metaworld.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update docs/source/adding_benchmarks.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update docs/source/adding_benchmarks.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update docs/source/adding_benchmarks.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * docs(benchmarks): add verification checklist to adding-benchmarks guide Made-with: Cursor --------- Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> |
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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
|
66fef25ded |
docs(toctree): add Benchmarks section for LIBERO and Meta-World (#3268)
* docs(toctree): add Benchmarks section for LIBERO and Meta-World Move LIBERO and Meta-World pages out of the Simulation section into a dedicated Benchmarks section so benchmark-specific docs are easier to find and the Simulation section stays focused on environment hubs. Made-with: Cursor * docs(toctree): move IsaacLab Arena into Benchmarks section Include NVIDIA IsaacLab Arena Environments alongside LIBERO and Meta-World in the Benchmarks section. Made-with: Cursor |
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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
|
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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Jade Choghari
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017ff73fbf |
chore(docs): add rename map and empty cam guide (#3065)
* add blog/guide * add to tree * chore(docs): rephrase rename_map docs for clarity and simplicity --------- Co-authored-by: Steven Palma <steven.palma@huggingface.co> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> |
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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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Jade Choghari
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b18cef2e26 |
feat(dataset): add subtask support (#2860)
* add subtask * remove folder * add docs * update doc * add testing * update test * update constant naming + doc * more docs |
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Caroline Pascal
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55c0471db9 |
docs(cameras): revising and improving docs on cameras (#2878)
* docs(cameras): revising and improving docs on cameras * resolving copilot comments |
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Steven Palma
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bf337e716d |
feat(robots): add OpenArm robot & teleoperator (#2795)
* fix(motors): cleanup imports + fix signatures * feat(motors): add damiao canbus + multiple fixes * fix(motors): address comments -> last_state + different gains + sleep * refactor(motors): reduce duplicated code + adressed some comments in the PR * chore(motors): better timeouts * tests(motors): damiao test and imports * chore(deps): fix space * feat(robot): add openarm leader Co-authored-by: Pepijn <pepijn@huggingface.co> * feat(robot): add openarm follower Co-authored-by: Pepijn <pepijn@huggingface.co> * refactor(robot): remove mechanical compensations and double arm assumption + rename * chore(robots): remove left arm references * refactor(teleop): multiple improvements to leader * refactor(teleop): multiple improvements to leader * feat(robots): add open arm to util CLI * chore(robot): add alias openarm * Apply suggestions from code review Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * chore(motors): remove normalization tables damiao * fix(motors): imports and signatures * feat(motors): add motor_type_str + recv_id to motor class and _get_motor_recv_id raises if no motor_obj.recv_id * chore(motors): remove normalize from base motor class and damaio * tests(motors): remove bad tests (to be replaced) * chore(motors): updated import check * fix(robots): open arm mirrored config for joint limits * chore(motors): update position_kd gain values * chore(robots): set to 0 if openarm is calibrated at connect time * chore(robots): remove macos in open arm as can doesn't support it * chore(robots): update for motor_type_str in Motor class * chore(robots): no default value for can port in open arms * use constant for kp and kd range and check responses in mit_control_batch() * Add docs on setting up canbus and use damiao otor bus, also add lerobot_setup_can.py and log if there is not response from a write command * precommit format * supress bandit as these are intentional cli commands * fix setup-can * add test * skip test in ci * nit precommit * update doc example * dont import can for tests * remove comment * Add openarms docs * format * update purchase link * can to none if nit availabl;e * add canfd option in bus * make handshake logic similar to lerobot-can * type hint * type check * add temp teleop test * remove script * mock class * ignore linter --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Pepijn <pepijn@huggingface.co> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> |
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Steven Palma
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9cfb5ce546 |
feat(motors): add damiao motors & can bus (#2788)
* fix(motors): cleanup imports + fix signatures * feat(motors): add damiao canbus + multiple fixes * fix(motors): address comments -> last_state + different gains + sleep * refactor(motors): reduce duplicated code + adressed some comments in the PR * chore(motors): better timeouts * tests(motors): damiao test and imports * chore(deps): fix space * Apply suggestions from code review Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * chore(motors): remove normalization tables damiao * fix(motors): imports and signatures * feat(motors): add motor_type_str + recv_id to motor class and _get_motor_recv_id raises if no motor_obj.recv_id * chore(motors): remove normalize from base motor class and damaio * tests(motors): remove bad tests (to be replaced) * chore(motors): updated import check * use constant for kp and kd range and check responses in mit_control_batch() * Add docs on setting up canbus and use damiao otor bus, also add lerobot_setup_can.py and log if there is not response from a write command * precommit format * supress bandit as these are intentional cli commands * fix setup-can * add test * skip test in ci * nit precommit * update doc example * dont import can for tests --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Pepijn <pepijn@huggingface.co> |
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Woojin Wie
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9e10eb4a77 | fix(robots): update gripper configuration and calibration settings for OMX (#2815) | ||
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Jade Choghari
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1d86c9b7f2 | feat(policies): add autoregressive VLAs with tokenization PiFast (#2734) | ||
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githubnemo
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e670ac5daf |
Add basic PEFT support to train script + record module (#1411)
* Add basic support for PEFT adapter methods This changes adds support for training policies with much less parameters by applying adapter methods such as LoRA on specific parts of the policies and therefore possibly higher learning rates / batch sizes. To make this as accessible as possible I thought it useful to provide defaults for `target_modules` and `modules_to_save`. Currently only SmolVLA has such defaults but when we agree that this change is useful I will set out to generate more such defaults. While the user can override these settings, they are expected to only change the peft_method, rank and init_type parameters. * Implement loading of PEFT adapters Loading a PEFT adapter is currently done by initializing a policy with default config and then applying the adapter on the resulting model. This has the obvious drawback that any configurations done during training are not applied in the adapted model. Currently the `use_peft` attribute of `PreTrainedConfig` is only set during loading to signal the following code that it has to deal with a PEFT adapter. However we could imagine a scenario where this is already set at training time and stored alongside the adapter. * Store policy config alongside PEFT checkpoint Before this change the PEFT-wrapped policy did not save the policy's config alongside the adapter config / weights which prevented us from changing the policy config. Now the policy config is saved both in full training and PEFT training. This change makes loading the PEFT policy adapter much easier as well. * Add default config for ACT * Support targets like `all-linear` * Formatting * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix failing tests * Remove PEFT compatibility changes in config We'll wait for the PEFT release that fixes this for good. * Remove `use_peft` parameter from training script Instead we make the PEFT config optional which has the same effect. * Log adapter config to WandB * Better documentation for CLI arguments * Don't unload & merge the PEFT model This can make things hard when using quantized layers (user expects quantized base layers with unquantized adapters for example, merging defaults to upcast the layers leading to higher memory). * Correct way of identifying when to save config * Add CLI end-to-end tests Currently there don't seem to be any way to test the CLI commands. Since this change mostly happens in those I thought it best to add a way to test these commands end-to-end. More integrated commands like `lerobot-record` need patching but standalone commands like training seem to work fine. * Update default targets Removed ACT since it doesn't make sense to fine-tune ACT without having it pretrained beforehand. SmolVLA and Pi0/0.5 are much more senseful targets. * Clean up loading code - Centralized instantiation of the PEFT wrapper in `make_policy` for inference (e.g. in `lerobot-record`) - Training a PEFT policy also sets `cfg.use_peft` so that all inference code loading the policy can rely on that attribute to identify if PEFT loading is needed - Modified RTC example to also include PEFT policies. Mostly because this is an example I'm currently exploring. * Make sure push_to_hub works Since PEFT only wraps `push_to_hub` and not `push_model_to_hub`, the reference to `self` in `policy.push_model_to_hub` is the unwrapped policy which, of course, doesn't know anything about PEFT. To make the upload process aware of PEFT, we pass the unwrapped policy down to `push_model_to_hub` as a kwarg. This is not ideal but I think it is the best way for now. * formatting * Warn when encountering from-scratch-training * Revamp pretrained model loading There were quite a few factors that convinced me that the status quo is able to load pretrained models from the PEFT adapter config but in fact that didn't work. This commit fixes the following things: - policies wrapped in PEFT will now have a `name_or_path` attribute containing the name or path of the pretrained model we're fine-tuning - we further assume that SmolVLA without `pretrained_path` and `load_vlm_weights==False` must be an user-side error - we assume that using PEFT on from-scratch-policies must be an user-side-error * Make it possible to unset policy features This is necessary to train pre-trained policies on new datasets so that the features are inferred from the new dataset and not from the pretrained policy. * Use correct loading for PEFT in RTC example * Make it possible to use PeftModels in eval * Add test checking that PEFT actually reduces params * Adapt state/action projections instead of full-finetuning There doesn't seem to be a benefit to fully fine-tune these layers over just adapting them, so we do that instead. * Disallow PEFT training on non-pretrained policies At first I thought it would make sense to have this feature in case you want to fine-tune a pre-trained section but in the end it makes more trouble than it's worth. It's still possible to allow this in the future when a concrete need arises. * Add basic documentation * Formatting * Add peft as extra dependency, mark tests Fast tests currently fail because of the missing dependency. * Fix pre-commit issues * Add walx <> peft conflict for uv * Exclude peft from pi install for now --------- Co-authored-by: nemo <git@ningu.net> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> |
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Kartik
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fc296548cb |
feat(envs): Add NVIDIA IsaacLab-Arena Lerobot (#2699)
* adding Isaaclab Arena from collab
* adding into lerobot-eval
* minor modification
* added bash script for env setup
* setups
* fix applauncher not getting the arguments
* data conversion, train and eval smolvla
* fixed imports
* clean-up
* added test suits & clean up - wip
* fixed video recording
* clean-up
* hub integration working
* clean-up
* added kwargs
* Revert "added kwargs"
This reverts commit 9b445356385d0707655cf04d02be058b25138119.
* added kwargs
* clean-up
* cleaned unused function
* added logging
* docs
* cleaned up IsaaclabArenaEnv
* clean-up
* clean-up
* clean up
* added tests
* minor clean-up
* fix: support for state based envs
* feat(envs): Add NVIDIA IsaacLab Arena integration with LeRobot for policy evaluation at scale
* feat(envs): Add IsaacLab Arena integration for policy evaluation
Integrate NVIDIA IsaacLab Arena with LeRobot to enable GPU-accelerated
simulation through the EnvHub infrastructure.
This enables:
- Training imitation learning policies (PI0, SmolVLA, etc.)
- Evaluating trained policies in with IsaacLab Arena
The implementation adds:
- IsaaclabArenaEnv config with Arena-specific parameters
- IsaaclabArenaProcessorStep for observation processing
- Hub loading from nvkartik/isaaclab-arena-envs repository
- Video recording support
Available environments include GR1 microwave manipulation, Galileo
pick-and-place, G1 loco-manipulation, and button pressing tasks.
Datasets: nvkartik/Arena-GR1-Manipulation-Task
Policies: nvkartik/pi05-arena-gr1-microwave,
nvkartik/smolvla-arena-gr1-microwave
* added isaaclab arena wrapper and corresponding tests
* added error handling
* renamed wrapper file: isaaclab_arena to isaaclab
* added extra kwarg changes
* adjustments for hub envs
* correct class name in test file
* fixed parsing of env_kwargs
* tested end to end
* removed unused code
* refactor design
* shifted IsaacLab to hub
* removed IsaacLab tests
* docs: Add LW-BenchHub evaluation instructions
* docs: Add LW-BenchHub evaluation instructions
* docs diet
* minor edits to texts
* IL Arena commit hash
* update links
* minor edits
* fix numpy version after install of lerobot
* links update
* valideated on vanilla brev
* docs: Add LW-BenchHub evaluation instructions
* remove kwargs from all make_env calls
* remove kwargs from all make_env calls
* fix LW table and indentations
* remove environment list from docs
* docs: Update lw-benchhub eval config in envhub docs
* removing kwargs
* removed extra line
* ensure pinocchio install for lightwheel + add lightwheel website link
* remove env_kwargs
* no default empty value for hub_path
* not using assert method
* remove env_processor defaults
* revert and adding default "" value for hub_path
* pinning down packages versions
* explicit None value for hub_path
* Update src/lerobot/configs/eval.py
Co-authored-by: Jade Choghari <chogharijade@gmail.com>
Signed-off-by: Lior Ben Horin <liorbenhorin@gmail.com>
* corrected formatting
* corrected job_name var in config
* updated docs and namespace
* updated namespace
* updated docs
* updated docs
* added hardware requirements
* updated docs
---------
Signed-off-by: Lior Ben Horin <liorbenhorin@gmail.com>
Co-authored-by: lbenhorin <lbenhorin@nvidia.com>
Co-authored-by: Lior Ben Horin <liorbenhorin@gmail.com>
Co-authored-by: Jade Choghari <chogharijade@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: tianheng.wu <tianheng.wu@lightwheel.ai>
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Tong Wu
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a64f2fd322 |
modify the README file for wallx (#2705)
* support wallx * fix bugs in flow * incorporate wallx model into lerobot * update the policy methods * reduce to least config and params & pass lerobot basic test * fixed dtype bugs * add wallx dependencies * update * remove flash-attn requirement && fix bug in inference and fast mode * fix bug for inference * add some small modifications * fix pre-commit errors * remove lerobot[wallx] * fix ci * fix precommit issues * fix: exclude wallx extra properly in CI workflows * fix: add uv conflicts for wallx transformers version * fix: peft test import * pre-commit * only export WallXConfig from wall_x package to avoid peft import in CI * remove torch dep * precommit * add import * update doc files * fix minor errors --------- Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: vincentchen <chenlufang@x2robot.com> Co-authored-by: Geoffrey19 <sympathischmann35@gmail.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Pepijn <pepijn@huggingface.co> |
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Pepijn
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f04958527e |
Add sarm (#2639)
* add initial modeling * make rewind pretrained policy * add annotation * small fix * add sarm * subtasks * fix spawn * fix rewind discrepancies * Add script to generate embedding for dataset (#2138) * Add generate and validate script * fix precommit * Improve generate embeddings function by using dataset tools (#2206) --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> * cleanup * change order train log * print batch size * update sarm processor * add reward output * change expected features * add image validation * change validation * get state input from dataset stats * raise if no state key is found * pass stats * cleanup and refactor * add episode inddex to complementary data * add subtask init and detection * revert lerobot_train changes * pass dataset metadata to policy * change loadig subtasks * add small logging * fix progress conversion and adding initial frame * use large offset for initial frame (ugly) * Remove rewind, use clip tokenizer * add tests, implement formula 1,2 correctly and cleanup * use task from dataset, cleanup visualizer * simplify * simplify and cleanup code and move compute_temporal_proportions to utils * fix normalization in visualization * Fix visualization and change prompt * fix formatting * add visualize subtask annotations * use qwen thinking * try different prompt * format * update prompt * higher temp, long output * different settings * use instruct * show full resp * split message * Temp: increase tolerance dataset * Fix RA-BC (#2572) * Add next observation loading for RA-BC progress deltas * Compute weights based on temporal progress deltas instead of static rewards * Add hard-masking for negative progress deltas in weight computation * Feat/add dual head (#2582) * Add dual dense sparse head and annotation * Add docs * add dual to procesor * cleanup * change sampling in visualize and cleanup * remove validation * remove compile * Feat/test uniform (#2587) * test uniform * add different string for misaligned * Fix rewind and add tests * uncomment text implementation * run precommit * Add head mode for ra-bc * fix visalization of single task * add * return per sample loss * Fix RA_BC (#2602) * update rabc implementation * compute rabc beforehand * fix import * add only progress calulation * use precomputed progress * multi gpu processing * import * fix dataset meta data extraction * add logging * logging * log * progress per episode * split differently * move clip to gpu * pre decode frames for an episode * fix cuda initalization * fix import * multi processing * rename * fix import * fix * fix rabc * use last known progress if oob * use last known progress if oob * add misalignment loss with random embeddings * discard previous changes * add selection of models to docs for ra_bc * add transformers dep * extend tolerance * initial commit with new codebase * add tests * fix * remove temporal sampler * drop last frame for sampler * use original ref * some fixes * fix visualization * remove smoothing and fix order subtasks * add stride rabc computation * add push to hub * add explanation * add kappa expllaination * better rabc logging * feedback pr * remove dataset tolerance * revert dataset tool * revert dataset changes * add credit * run precommit * change path for generate ra_bc * fix type * include sarm in all in pyproject * fix precommit * lazy import matplotlib * lazy import qwen * remove rich console * skip if transformers is not installed? * run only when we have faker * place transformer lazy loading * Dont test if low transformer version * fix * increase transformer * increase as 4.57.0 is yanked * remove pi from all * go back --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: s1lent4gnt <kmeftah.khalil@gmail.com> |
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Vladislav Sovrasov
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d79dd6d31f | Add a documentation page with a brief intro to hw backends (#2385) | ||
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Steven Palma
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f8a4cf225b |
feat(robots): add earth rover robot support (#2575)
Co-authored-by: somthecoder <sbaner64@gmail.com> Co-authored-by: randomSmarts <Aarshsmittal@gmail.com> Co-authored-by: Hassoonu <halsae2@illinois.edu> Co-authored-by: Saketh06 <saketh.kantipudi@gmail.com> Co-authored-by: sairajshetye <sairajshetye2@gmail.com> Co-authored-by: Khalil Meftah <kmeftah.khalil@gmail.com> |
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Jade Choghari
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43b0f17eb9 |
feat(policies): Add X-VLA (#2405)
* first commit * more fixes * add franka action * update testing script * add changes * update files * logits matching * add imagenet as a norm type * logits matching atol1e-2 * more eval fixes * more changes * xvla works on libero * remove seed * more refactoring * more fixes * more changes * more changes * more fixes * migrate policy revert * major pre-commit cleanup * renaming * revert to self.transformer * refactor * new changes * clean * update libero * more changes * make it work * more changes: * remove imagenet dependency * style * more * more refactor * remove proprio * add loss * more * more * add freeze/unfreeze options * add testing * upgrade transformers version * update testing * add installation * remove .sh file * fix testing * silent linter in xvlatest * fix failing test * upgrade test, fix failing * fix testing * more fixes to testing * require cuda in tests * temp check * add xvla docs * fix styling * update libero doc * remove timm dep * add different dtype support * remove timm skip * remove white lines * Enhance X-VLA finetuning documentation with optimizer details (#2537) Added detailed instructions for implementing a custom optimizer and modifying parameter retrieval for X-VLA finetuning. Signed-off-by: Jinliang Zheng <54488861+2toinf@users.noreply.github.com> * fix style * iterate on review * iterate on cpilot * revert xvla dep * free up ci * test(xvla): remove main test (#2565) * Add xvla custom optim and dtype (#2567) * add custom optim * add custom optim * add auto mode * more changes * add identity to all * add auto * release * add docs * make image smaller docs * smaller image in doc * evan smaller image doc * finalize doc --------- Signed-off-by: Jinliang Zheng <54488861+2toinf@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Jinliang Zheng <54488861+2toinf@users.noreply.github.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> |
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Steven Palma
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b0b755471b |
Revert "Earth Rover Mini Plus integration (#2544)" (#2574)
This reverts commit
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s1lent4gnt
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35c5a27352 |
Earth Rover Mini Plus integration (#2544)
* feat: Add EarthRover Mini Plus robot integration with Frodobots SDK * refactor: Clean up * refactor: Remove VirtualCamera implementation for EarthRover Mini Plus integration * fix: Reduce timeout for camera requests * fix: Add empty cameras dict for compatibility with recording script * refactor: Remove record.py script for EarthRover Mini Plus use lerobot_record instead * refactor: Update documentation for EarthRover Mini Plus integration * refactor keyboard teleoperation * refactor: Remove angular velocity * docs: Add documentation for EarthRover Mini Plus integration * Add earthrover_mini_plus robot to replay and teleoperate scripts * refactor: Update stop key from Space to X * refactor: Implement caching for camera frames and robot telemetry data * refactor * refactor: Replace string literals with constants for action and observation keys * Add Earth Rover Mini to robots section in documentation Co-authored-by: somthecoder sbaner64@gmail.com Co-authored-by: randomSmarts Aarshsmittal@gmail.com Co-authored-by: Hassoonu halsae2@illinois.edu Co-authored-by: Saketh06 saketh.kantipudi@gmail.com Co-authored-by: sairajshetye sairajshetye2@gmail.com |
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Daniel San José Pro
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9ec9ee781a |
feat(policies): Allow users to register 3rd party policies - pip install lerobot_policy_mypolicy (#2308)
* feat: Register external policies * ruff fix * move policy util functions to policy factory * refactor register_third_party_devices -> register_third_party_plugins * feat: Update docs with bring your own policies * Improve docs for new policies * fix: Inconsistent quotation marks * fix: Remove print statement * fix: wrong base class name in documentation * fix: Handle better how the models are parsed * fix: precommit passing * Update docs/source/bring_your_own_policies.mdx Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Signed-off-by: Daniel San José Pro <42489409+danielsanjosepro@users.noreply.github.com> --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Signed-off-by: Daniel San José Pro <42489409+danielsanjosepro@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> |
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Martino Russi
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37f43df88a |
Feat/add unitree g1 robot (#2530)
* add unitree_g1_robot_class * finish locomotion loading code * precommit * separate groot locomotion logic * remove leftover locomotion variable, unify kp kd * format config * properly comment config, example locomotion and unitree_g1 class * ready to review * download policy from the hub in `examples/unitree_g1/gr00t_locomotion` * fix linter * make precommit happy, add ignore flags * linter pt3 * linter pt4 * [done] make precommit happy * fix linter 5 * add docs * push utils * feat(robots): add Unitree G1 humanoid support with ZMQ bridge (#2539) * feat(robots): add Unitree G1 humanoid support with ZMQ bridge - Use JSON + base64 serialization for secure communication instead of pickle - Add documentation section - Rename robot_server to run_g1_server - Add dependecies to pyproject.toml * nit in docs * remove globals use * cast robot data to int/float * ensure robot is connected before changing mode * temperature can be list, average in such case --------- Co-authored-by: Martino Russi <nopyeps@gmail.com> * style nit * remove transform_imu_data * remove scipy dependency * modify toml, add external unitree_sdk2py dep * return actions from send_action * cleaning * add instructions for local deployment * Update src/lerobot/robots/unitree_g1/unitree_g1.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> * update config and readme * update docs * update docs * remove torch import * fix docs * remove ip from docs * add licence header --------- Signed-off-by: Martino Russi <77496684+nepyope@users.noreply.github.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> |
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Jade Choghari
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36e8feefe3 |
docs: Add LeIsaac x LeRobot Envhub tutorial (#2498)
* add leisaac doc * depreciate il in sim * fix readme * more * fix styling * update title * more changes * more * fix style * more * fix style |
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Jade Choghari
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6e86a69dcd |
feat(envs): add envs pre-post processor (#2474)
* more changes * working changes * more changes * more fixes * fix style * more * clean * put axis-1 * more fixes * more styling fixes: * iterate on review: * more changes * add env processor * style * more changes * add docs * fix imports * fix test, add to train * Update src/lerobot/envs/factory.py Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Signed-off-by: Jade Choghari <chogharijade@gmail.com> * iterate on review --------- Signed-off-by: Jade Choghari <chogharijade@gmail.com> Co-authored-by: jade.choghari@huggingface.co <“chogharijade@gmail.com”> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> |