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251 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 pepijn
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c80ddfe22c |
Merge remote-tracking branch 'origin/main' into feat/smolvla-on-steerable
Co-authored-by: Cursor <cursoragent@cursor.com> # Conflicts: # src/lerobot/configs/train.py # src/lerobot/datasets/__init__.py # src/lerobot/policies/factory.py # src/lerobot/policies/groot/groot_n1.py # src/lerobot/scripts/lerobot_eval.py # src/lerobot/scripts/lerobot_train.py # uv.lock |
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Caroline Pascal
|
b895ed0fe4 |
docs(prettier): making video encoding parameters docs prettier (#3911)
* docs(prettier): making video encoding parameters docs prettier * chore(format): formatting code * chore(contrast): removing poor contrast elements |
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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>
|
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Steven Palma
|
7957d4e2dc |
chore(docs): update readme + gr00t libero results (#3941)
* chore(docs): update readme + gr00t libero results * chore(docs): update template and in-tree policy steps |
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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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Nikodem Bartnik
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911734ec9c |
Docs/improve HF jobs documentation (#3909)
* improve hf jobs docs * Update docs/source/hardware_guide.mdx Co-authored-by: Nicolas Rabault <rabault.nicolas@gmail.com> Signed-off-by: Nikodem Bartnik <39432165+NikodemBartnik@users.noreply.github.com> --------- Signed-off-by: Nikodem Bartnik <39432165+NikodemBartnik@users.noreply.github.com> Co-authored-by: Nicolas Rabault <rabault.nicolas@gmail.com> |
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Steven Palma
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052d329470 |
feat(visualization): add foxglove support (#3902)
* Add Foxglove display mode for teleoperate
Add a --display_mode flag (rerun|foxglove) to lerobot-teleoperate. When set
to foxglove, stream observations/actions over a Foxglove WebSocket server:
images as RawImage/CompressedImage, scalars as typed JSON channels with
schemas generated from the feature names (sanitized so paths don't need
quoting). Adds a `foxglove` extra.
* Add Foxglove display mode to lerobot-record
Wire the --display_mode flag (rerun|foxglove) into lerobot-record, matching
lerobot-teleoperate: route init/log through the backend-agnostic dispatchers
and stop the visualization backend on exit.
* update foxglove-sdk to 0.25.1
* Use static lerobot.Scalars schema for Foxglove state topics
Replace the per-topic JSON schema derived from feature names with a single
static lerobot.Scalars schema: a scalars array of {label, value} objects. The
same schema fits any robot regardless of which observation/action features it
reports, and the label field lets Foxglove name each series automatically so
one filtered path plots every feature.
* add foxglove option to dataset viz
* Make Foxglove dataset playback loop the sole frame emitter
Address review: the listener no longer emits frames, it only mutates
playback state and queues a one-shot seek index that the playback loop
services. The loop is now the only caller of emit_frame, so concurrent
random access into the on-disk dataset / video decoder never overlaps.
Also remove the dead server_holder and tighten the _foxglove_safe_name
docstring to state what it does and why.
* Label Foxglove dataset scalars with feature dimension names
Use the dataset's per-dimension feature names (e.g. joint names) as the
Foxglove series labels for /observation/state and /action/state instead
of bare indices. LeRobot stores `names` inconsistently (flat list,
{category: [...]}, or {name: index}), so _feature_dim_names handles each
and falls back to indices on any unknown format or length mismatch.
* Make Foxglove server host bindable and refactor topic/channel handling
Pass display_ip through as the Foxglove WebSocket bind host (127.0.0.1
for local only, 0.0.0.0 for all interfaces) instead of always binding
locally. In lerobot-dataset-viz, fold the separate --port into --web-port
so one flag covers both the Rerun web viewer and the Foxglove server port.
Add a _foxglove_topic() helper and thread a per-topic channel cache
through the log helpers so dataset playback stays self-contained instead
of mutating the module-global cache. Promote SUCCESS to constants.py.
* feat(viz): add support for foxglove in rollout + add to viz tag
* fix(docs): remove misleading installation note
* fix(visualization): no duplicated prefix, consolidated norm + warnings log
* chore(viz): minor improvements
* refactor(viz): split files + autoplay + updated docs + added minimal tests
* fix(viz): right tags + warning
* feat(deprecated ws-port): removing rerun's depreacted ws-port parameter in dataset visualization
* chore(web ports): adding global variables for default foxglove/rerun web ports
* feat(depth): adding depth support to foxglove visualizer. Because of foxglove limitations (min and max values on RawImage cannot be set from the SDK), depth is normalized between [0,1] when a depth range is provided.
* fix(rerun depth range): making rerun depth range computation safe against missing stats
* chore(foxglove depth): make it simple, and make it work.
* fix(scaling): fixing depth frames scaling
---------
Co-authored-by: Roman Shtylman <roman@foxglove.dev>
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
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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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Khalil Meftah
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2f2b567951 |
Enable MolmoAct2 rollout on SO-100/101 with calibration correction (#3879)
* fix(rollout): improve visual feature mismatch error with --rename_map hint * feat(policies): add joint frame transform and hardware deployment docs for MolmoAct2 Add MolmoAct2StateFrameTransformStep and MolmoAct2ActionFrameTransformStep processor steps for cross-calibration compatibility on SO-100/101. Add joint_signs and joint_offsets config fields. Add hardware deployment section to molmoact2.mdx with camera naming convention, joint frame correction, and safety guidance. * chore(docs): address PR comment * fix: address reviewer comments |
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Nicolas Rabault
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5ac3b49a5f |
feat(train): run training remotely on HF Jobs via --job.target (#3856)
* feat(train): add JobConfig group, save_checkpoint_to_hub flag, Hub checkpoint helper
Introduce a JobConfig draccus group on TrainPipelineConfig (--job.target/image/
timeout/detach/tags) whose is_remote property gates remote dispatch, plus a
save_checkpoint_to_hub flag and validation. Add push_checkpoint_to_hub(), which
uploads a saved checkpoint directory to the model repo under checkpoints/<step>/
and creates the repo idempotently (private propagates from policy.private).
* feat(train): run training remotely on HF Jobs via --job.target
When --job.target names a GPU flavor, train() dispatches to lerobot.jobs.submit_to_hf
instead of training locally: it authenticates, ensures the dataset is on the Hub
(pushing a local-only one privately), serializes a pod-compatible train_config.json
(strips client-only fields, points at the model repo), submits via HfApi.run_job
with HF_TOKEN/WANDB_API_KEY secrets, then streams logs and finishes when the model
is pushed. Wires push_checkpoint_to_hub into the training loop behind
save_checkpoint_to_hub, and tags jobs/datasets/model with 'lerobot' + --job.tags.
* docs(train): document remote training on HF Jobs
* test(train): skip remote-dispatch tests without the dataset extra
The module imports lerobot.scripts.lerobot_train, which eagerly pulls in
lerobot.datasets (dataset extra). The base fast-test CI tier runs without
that extra, so collection failed there. Guard with pytest.importorskip,
matching the existing tests/scripts dataset-extra tests.
* refactor(jobs): hoist huggingface_hub imports to module level in hf.py
huggingface_hub is a core dependency, so the per-function dynamic imports
had no lazy-loading rationale. Move them to a single module-level import
and update test monkeypatch targets to lerobot.jobs.hf.* accordingly.
* refactor(jobs): build remote config dict via cfg.to_dict()
TrainPipelineConfig.to_dict() already returns the canonical draccus
encoding, so the StringIO + draccus.dump + json.loads round-trip was
redundant. Use it directly and drop the now-unused io/draccus imports.
* refactor(train): use module-level HfApi import in push_checkpoint_to_hub
huggingface_hub is a core dependency; the in-function import was
unnecessary. Move HfApi to a module-level import and point the test
monkeypatches at lerobot.common.train_utils.HfApi.
* refactor(configs): export JobConfig from the configs package
Re-export JobConfig in lerobot/configs/__init__.py so external callers
import it as `from lerobot.configs import JobConfig`, matching the other
config classes. Adapt the train script and test imports.
* refactor(jobs): check dataset presence with api.repo_exists
Replace the dataset_info try/except RepositoryNotFoundError dance with a
direct api.repo_exists(repo_id, repo_type="dataset") call, dropping the
httpx/RepositoryNotFoundError test scaffolding.
* chore(jobs): annotate ensure_dataset_available api param as HfApi
Add the missing HfApi type hint via a TYPE_CHECKING import.
* refactor(jobs): use HF_LEROBOT_HOME constant for the local cache root
Resolve the local dataset cache via lerobot.utils.constants.HF_LEROBOT_HOME
instead of re-reading the env var by hand, dropping the os/Path imports.
Tests now patch the imported constant and assert on a stable message
substring (the previous "neither" match only passed by accident, matching
the test name embedded in the pytest tmp_path).
* chore(jobs): guard LeRobotDataset import with require_package
Surface a clear "install lerobot[dataset]" error if the datasets extra
is missing, instead of a raw ImportError, before pushing a local dataset.
* docs(configs): clarify the is_remote_target/is_remote split
Add a comment explaining why JobConfig keeps both the staticmethod (tests
a raw target string from argv before a config exists) and the property
(accessor for an existing config instance).
* docs(train): note how to pin a pushed model version for inference
Document --policy.pretrained_revision alongside --policy.path so a
specific Hub-pushed checkpoint (once --save_checkpoint_to_hub has
committed several) can be selected for inference.
* test(jobs): skip dataset import guard in base-deps test
The fast test env installs base deps only, so require_package('datasets')
raised ImportError before the mocked lerobot.datasets import was reached.
Monkeypatch the guard to a no-op so the unit test exercises the upload logic.
* fix(jobs): address claude review findings on remote training
Resolve the claude[bot] review on #3856:
- Reject reward-model training under --job.target with a clear error instead
of crashing on a None policy inside build_remote_config_file.
- Support --policy.path remote runs: validate() no longer requires repo_id for
remote runs (it is auto-generated in submit_to_hf), and repo_id/push_to_hub
are now set after validate() resolves the policy.
- Narrow the bare `except Exception` in _tail_logs/_poll_until_done to
(OSError, httpx.HTTPError) so programming errors surface instead of being
silently retried or counted as job failures.
- Install the SIGINT detach handler only on the main thread.
- Generate model repo timestamps in UTC.
* docs(jobs): document the model-pushed marker contract and orphaned repos
Follow-up to the claude[bot] review on #3856 (non-blocking observations):
- Cross-reference the "Model pushed to <url>" log line between its producer
(PreTrainedPolicy.push_model_to_hub) and the remote-run consumer in
submit_to_hf, noting the contract is an early-finish optimization that
falls back to status polling if it drifts.
- Note in the HF Jobs guide that a failed remote run leaves its model repo
on the Hub (it is not auto-deleted) and how to remove it.
* feat(train): tag each pushed checkpoint with its step
Address review feedback on #3856: pushing a checkpoint to the Hub now
also creates a tag named after the checkpoint step, so a checkpoint can
be recovered with --policy.pretrained_revision=<step> instead of having
to look up its commit sha.
* fix(jobs): hoist ensure_dataset_available to a module-level import
Addresses Caroline's review comment on PR #3856: the local import of
ensure_dataset_available inside submit_to_hf was vestigial. dataset.py
does not import hf.py, so there is no circular-import risk and no extra
load cost (its heavy deps stay lazy), so make it a top-level import.
* refactor(configs): untangle config_path/resume resolution in validate()
Split the re-parse HACK block in TrainPipelineConfig.validate() into focused
helpers (_resolve_pretrained_from_cli, _resolve_resume_checkpoint) that handle
the policy path, reward-model path, and resume config_path as separate,
readable units. Behavior-preserving.
* feat(train): resume training from a Hub checkpoint
Allow --config_path to be a Hub repo id when resuming, not only a local path.
The latest checkpoint under checkpoints/<step>/ is downloaded into a fresh local
run dir and resumed from there (optimizer, scheduler, RNG and data order
restored as for a local resume). TrainPipelineConfig.from_pretrained falls back
to the latest checkpoint's train_config.json when a repo has no root config
(an interrupted run that only pushed checkpoints). The download is skipped when
dispatching remotely so the executor (local machine or HF Jobs pod) performs it.
- add find_latest_hub_checkpoint (utils/hub) and resolve_resume_checkpoint
(common/train_utils), the symmetric download counterpart to
push_checkpoint_to_hub
- unit tests for both helpers and the from_pretrained fallback
* feat(jobs): resume a run on HF Jobs from a checkpoint
When --resume is set with a remote --job.target, submit_to_hf resumes from the
checkpoint repo instead of staging a fresh config. A Hub config_path is resumed
in place (its checkpoint config already targets that repo); a local config_path
has its checkpoint uploaded to a new private repo first and the run is forced to
push back to it. The pod command carries --job.target=local so the checkpoint's
saved job.target can't make the pod re-dispatch itself, and the user's CLI
overrides are forwarded so a remote resume matches the same local command.
ensure_dataset_available is hoisted before the resume/fresh branch since it
applies to both.
* docs(train): document resuming from a Hub checkpoint, locally and on jobs
Show that --config_path accepts a Hub repo id for --resume, and that adding
--job.target resumes on HF Jobs (uploading a local checkpoint/dataset first).
* fix(jobs): default remote job timeout to 2d instead of the platform default
HF Jobs applies its own short 30-minute timeout when none is sent, which
silently kills long training runs. Pass an explicit, generous 2d cap by
default; users can still override --job.timeout to fail fast or extend it.
* fix(jobs): drop --dataset.root on resume + restore keyboard-control docs
Address the latest Claude review on #3856:
- _build_resume_job no longer forwards --dataset.root to the pod (a
host-local path it can't read); the fresh-run path already nulls it in
build_remote_config_file, so this makes resume consistent. Add a unit
test for _pod_forwarded_args covering the drop in both flag forms.
- Restore the display-independent keyboard-control docs (n/r/q letter
equivalents + X11/Wayland/headless Tip) in il_robots.mdx that this
branch was stale on relative to main (#3875).
* fix(jobs): handle str-typed job stage from huggingface_hub
inspect_job's status.stage is an enum (with .value) in some
huggingface_hub versions and a plain str in others. The poller
assumed the enum shape, raising "'str' object has no attribute
'value'" on resume for users on the str-returning version.
Read it via getattr(..., "value", ...) so both shapes work, and
parametrize the poll test over enum and str stages so the str case
is actually exercised (the old mock only ever simulated the enum).
* refactor(jobs): use relative import for ensure_dataset_available
* refactor(train): hoist submit_to_hf import to module top
The `from lerobot.jobs import submit_to_hf` was a function-local import in
train(); it pulls no heavy/optional deps and has no circular-import risk, so
move it to the top-level import block.
* refactor(train): hoist _remote_target_in_argv imports to module top
Move `import sys` and `from lerobot.configs import JobConfig` out of the
function body and into the top-level import block.
* refactor(utils): use relative import for sibling constants in hub.py
`from lerobot.utils.constants import CHECKPOINTS_DIR` was the odd one out in
utils/ — sibling modules there are imported relatively (.constants, .errors,
.utils, ...). Match that convention.
* refactor(jobs): hoist LeRobotDataset import, guard dataset extra at package init
Move the `from lerobot.datasets import LeRobotDataset` import to the top of
dataset.py and relocate the `require_package("datasets", extra="dataset")`
guard to the jobs package __init__, per review feedback.
* test(jobs): skip test_hf if datasets extra is missing
lerobot.configs.train pulls in datasets at import time, so the module
fails to collect without lerobot[dataset]. Guard with importorskip,
matching the convention in tests/training/test_multi_gpu.py.
* test(jobs): skip test_dataset if datasets extra is missing
tests/jobs/test_dataset.py imports lerobot.jobs.dataset, which triggers
the require_package("datasets") guard in lerobot/jobs/__init__.py at
import time. Without lerobot[dataset] the module fails to collect in the
base CI tier. Guard with importorskip, same as test_hf.py.
|
||
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Caroline Pascal
|
3dd19d043e |
feat(depth maps): adding support for depth in LeRobot (#3644)
* feat(depth): add depth quantization helpers and tests
* feat(video): add ffv1 to supported codecs
* feat(depth): persist depth metadata
* feat(depth): extend quantization tools to better fit the encoding/decoding pipeline
* feat(depth): plumb DepthEncoderConfig through LeRobotDataset and DatasetWriter
* feat(depth): wire StreamingVideoEncoder + writer to depth encoder
* feat(depth): wire DatasetReader to decode_depth_frames
* feat(cameras/realsense): expose async depth in metric meters
* feat(features): route 2D camera shapes to observation.depth.<key>
* feat(robots/so_follower): emit + populate depth keys when use_depth
* feat(record): plumb DepthEncoderConfig through lerobot-record
* feat(viz): render depth observations as rr.DepthImage in Viridis
* feat(depth maps writer): adding support for raw depth maps recording with image writer
* chore(format): format code
* feat(depth shape): ensuring depth maps shape is always including the channel
* feat(is_depth): simplifying is_depth nested name + legacy support
* fix(stop_event): fixing stop_event race condition in camera classes
* fix(plumbing): fixing missing parts in the depth maps pipeline
* chore(typos): fixing typos
* test(fix): fixing exisiting tests to still work with latest features
* tests(depth): adding new tests for depth integration validation
* feat(pix_fmt channels): use PyAv to check get pixel formats number of channels
* feat(refactor): refactor DepthEncoderConfig quantization pipeline, so that the methods do not live in the config class. Add pixel format - channels validation.Move the default pixel format for depth in the config file.
* fix(pre-commit): fixing mutable defautl value
* fix(info): fixing info metadata update when is_depth_map was set
* tests(typos): fixing typos in tests
* fix(realsense): fixing typo in realsense serial number
* fix(normalization): restricting 255 normalization to non depth/uint8 images only
* fix(typo): fixing typo
* fix(TIFF): add missing quantization and cleanup for TIFF files
* feat(batched dequantization): optimizing dequantize_depth for torch based batched dequantization
* feat(tools): adding depth support in LeRobotDataset edition tools
* test(aggregate): extending aggregation tests to depth frames
* test(cleaning): cleaning up tests
* fix(from_video_info): fixing early validation issue in from_video_info
* fix(typo): fixing typo
* fix(is_depth): adding missing doctrings and is_depth arguments in video decoding functions
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* fix(depth units): fixing depth units output for the realsense cameras
* feat(output unit): adding support for output unit specification at dataset reading/training time
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* test(depth): cleaning up depth tests
* test(depth encoding): updating and cleaning video/depth encoding tests
* chore(format): formatting code
* docs(depth): improving depth maps docs
* test(fix): fixing depth tests
* test(dataset tools): adding missing tests for new dataset edition tools features
* chore(format): formatting code
* fix(pyav check): fixing PyAV option validation for integer codec options by normalizing
numeric values before calling `is_integer()`
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* docs(mermaid): fixing mermaid diagram
* fix(rebase): rebase follow up corrections
* feat(dataset tools): adding missing docstrings and features for depth fill support in dataset edition tools
* docs(docstring): updating docstrings
* docs(dataset tools): updating docs
* fix(save images): fixing image saving in dataset tools
* fix(update video info): fixing update video info logic to match the recording and editing use cases
* test(reencode): fixing reencoding monkeypatch
* fix(review): add Claude review
* chore(format): format code
* fix(update video info): ditching the differentiated approahces for video info update - video info are always updated unless for preserved keys.
* chore(rebase): fixing rebase merge conflicts
* test(visualization): fixing visualization tests
* feat(docstrings): adding explicit docstring for encoding parameters. Docstrigns will now show up as description in the CLI --help.
* feat(mm as default): adding a global DEFAULT_DEPTH_UNIT variable setting mm as default depth unit
* fix(RGB <-> camera): renaming camera_encoder to rgb_encoder for clarity
* chore(TODO): removing deprecated TODO
* doc(write_u16_plane): improving docstrings for write_u16_plane
* feat(units): adding constants for depth frames units (m and mm)
* fix(spam): replacing spamming warning but a debug log
* feat(leagcy metadata): adding automatic metadata update for legacy 'video.is_depth_map' feature
* fix(copy&reindex): fixing metadat reshaping for single channel frames
* fix(ImageNet): excluding dpeth frames from ImageNet stats
* fix(PyAV container seek): fixing initial PyAV container seek to be robust againsy codec choice
* feat(lerobot-dataset-viz): adding support for depth in lerobot-dataset-viz
* fix(compress): removing rerun compression for DepthImages
* fix(signle channel squeeze): fixing single channel squeezing
* chore(format): format code
* fix(streaming): adding support for dequantization in streaming_dataset.py
* refactor(read depth): factorizing depth reading methods for realsense camera and adding support for depth-only usage
* chore(renaming): fixing missed RGBEncoderConfig renamings
* docs(renaming): reflecting renamings in a clearer way in the docs
* chore(annotation): excluding depth from the annotation pipeline
* feat(robots): adding depth support in compatible follower robots
* feat(LeSadKiwi): excluding LeKiwi from depth support (for now)
* chore(fail): removing misplaced file
* chore(fail): removing misplaced file
* fix(remove ffv1): removing ffv1 as it does not support MP4
* docs(cheat sheet): adding depth and video encoding to the cheat sheet
* fix(lossless): tuning depth encoding parameters for lossless depth storage
* test(fix): fixing failing tests
* depth(ZMQ): excluding ZMQ from depth support
* Revert "depth(ZMQ): excluding ZMQ from depth support"
This reverts commit
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||
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Khalil Meftah
|
6a788fbdb0 |
Add inline offline validation with train/eval split (#3824)
* refactor(training): rename eval_freq to env_eval_freq - Rename eval_freq to env_eval_freq to distinguish sim environment evaluation from offline loss evaluation. * feat(training): add inline offline validation with train/eval split - Add eval_split config for balanced per-task holdout - Add eval_steps for periodic inline eval loss computation - Add max_eval_samples to cap eval cost * fix(datasets): remap absolute indices in __getitem__ for filtered datasets * fix(train): vectorize eval subset selection for max_eval_samples * fix(datasets): Move the remapping into EpisodeAwareSampler via absolute_to_relative_idx * fix(validation): add eval_split range check and eval_steps warning Validate eval_split is in [0.0, 1.0) to prevent garbage splits from out-of-range values. Raise when eval_steps > 0 but eval_split is 0.0 since no offline eval will run. * fix(train): prepare eval dataloader with accelerator for multi-GPU Prepare eval_dataloader through accelerator.prepare() so eval data is sharded across ranks instead of duplicated. Reduce eval_loss across ranks with mean reduction for consistent logging. * fix(test): rename eval_freq to env_eval_freq for multi-GPU training |
||
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Khalil Meftah
|
c3f180e115 |
refactor(policies): clean MolmoAct2 to follow EO1/TOPReward patterns (#3724)
Align the MolmoAct2 implementation with lerobot codebase conventions: - Rename hf_model/ to molmoact2_hf_model/ - Slim config: move all I/O and runtime logic to modeling - Remove blanket from 8 vendored files, fix 66 lint issues - Deduplicate _hf_token() and _resolve_checkpoint_location() - Make huggingface_hub imports lazy - Remove custom MolmoAct2CosineDecayWithWarmupSchedulerConfig, use base class - Extract 13 static/classmethods from MolmoAct2Policy to free functions - Replace print() with logger in vendored action_tokenizer - Add module docstrings, class docstring, and key method docstrings - Add module-level loggers to modeling and processor - Fix docs: pip to uv install, deduplicate README symlink - Remove shebangs from all files |
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Eric Chan
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324086abc3 |
Update follower arm description in documentation (#3780)
Signed-off-by: Eric Chan <hazzelnut@pm.me> |
||
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Steven Palma
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b4e454c0ff |
feat(utils): display-independent keyboard controls for recording (Wayland / headless / macOS) (#3875)
* feat(utils): headless keyboard control * refactor(utils): consolidate keyboard listener creation * fix(rollout): remove import require guard for pynput --------- Co-authored-by: Leo Toff <leo@toff.dev> Co-authored-by: Stefano Maestri <stefano.maestri@javalinux.it> Co-authored-by: Sahil Chande <85823961+SahilChande@users.noreply.github.com> Co-authored-by: Vinayak Agarwal <63502278+Vinayak-Agarwal-2004@users.noreply.github.com> Co-authored-by: Abdul Rahim Mirani <abdulrahimmirani@gmail.com> |
||
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someone114514
|
508d18f8a1 | Fix ACT policy type examples in docs (#3792) | ||
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Alexandre Edmond
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536b9621b2 | Fix pi0fast model id in docs (#3855) | ||
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Pepijn
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6f0c776017 | chore(pi052): trim logging and recipes | ||
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Pepijn
|
4dbe83d3bc |
Merge remote-tracking branch 'origin/main' into feat/smolvla-on-steerable
# Conflicts: # docs/source/annotation_pipeline.mdx # examples/annotations/run_hf_job.py # pyproject.toml # src/lerobot/annotations/steerable_pipeline/config.py # src/lerobot/annotations/steerable_pipeline/frames.py # src/lerobot/annotations/steerable_pipeline/modules/plan_subtasks_memory.py # src/lerobot/annotations/steerable_pipeline/vlm_client.py # src/lerobot/annotations/steerable_pipeline/writer.py # src/lerobot/datasets/__init__.py # src/lerobot/datasets/sampler.py # src/lerobot/scripts/lerobot_annotate.py # src/lerobot/scripts/lerobot_train.py # tests/annotations/test_frames.py # tests/annotations/test_modules.py # tests/annotations/test_writer.py # tests/datasets/test_sampler.py # tests/scripts/test_lerobot_annotate.py # uv.lock |
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Maxime Ellerbach
|
73782447f2 |
feat(train): FSDP checkpoint saving (#3810)
* feat(train): FSDP checkpoint saving * adding docs for FSDP * adding a test for the fsdp checkpoint path * cleanup * fixing final upload to hub * refactored initial implementation to use torch fsdp api and adding new tests |
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Steven Palma
|
d576c59afb |
refactor(robots): homogenize bi-manual setups implementations (#3772)
* chore(robots): homogenize bi setups * feat(robots): split openarm mini into single and bi * refactor(robots): mixin for bi classes * docs: update docs |
||
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Pepijn
|
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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Pepijn
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c5965d4971 | Merge branch 'main' into feat/smolvla-on-steerable | ||
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Maxime Ellerbach
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09808183ca |
feat(rollout): adding episodic strategy (#3717)
* feat(rollout): adding legacy strategy * adding legacy to existing tests * updating docs and docstring * changing misleading docstring Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> * adding extra guard like dagged with try except finally * Potential fix for pull request finding Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> * adding reset to initial position * moving smooth teleop handover to control_utils and adding this behavior to legacy strategy * reducing duration of the handover * * renaming to episodic * changing semantics of the docstring * fixing leader - follower handover disable torque * adding optionnal config to disable handover * wiring the smooth_leader_follower_handover config * renaming config smooth_leader_to_follower_handover --------- Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> |
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pepijn223
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7b35af6eca |
Merge remote-tracking branch 'origin/main' into feat/smolvla-on-steerable
Co-authored-by: Cursor <cursoragent@cursor.com> # Conflicts: # uv.lock |
||
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Maxime Ellerbach
|
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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Pepijn
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0a6a799317 |
Merge feat/language-annotation-pipeline into feat/smolvla-on-steerable
Bring the authoritative annotation pipeline from the annotation branch.
The annotation surface is forced to EXACTLY match feat/language-annotation-
pipeline (the annotation branch is the source of truth for annotation
code), which also removes smolvla's stale copies:
- deleted: steerable_pipeline/vocabulary.py, tests/annotations/test_
vocabulary.py, prompts/module_0_vocabulary.txt, module_1_action_record
.txt, module_3_vqa.txt, module_1_plan.txt, and the old module_* prompt
names (now plan_*/interjections_*/vqa.txt).
- synced: all of src/lerobot/annotations/, lerobot_annotate.py,
examples/annotations/, tests/annotations/, datasets/language.py,
tests/datasets/test_language.py, docs/annotation_pipeline.mdx.
Non-annotation conflicts resolved by union (keeping both branches' intent):
- pyproject.toml: keep smolvla's pi extra (+sentencepiece) and add the
molmoact2 extra from main.
- policies/factory.py: keep both dataset_repo_id (pi052 FAST tokenizer)
and dataset_meta (both are referenced); union the policy-type docstring.
- scripts/lerobot_train.py: keep smolvla's pi052 / use_relative_actions
processor-rebuild block.
- uv.lock: regenerated from the merged pyproject.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
|
973318ef65 |
annotate: dedup task_aug + row-normalization; docs module on/off table
Two behavior-preserving simplifications:
* plan_subtasks_memory.run_episode: the task_aug 'axes' and free-form
branches built identical deduped rows via copy-pasted seen/append
loops. Collapse to one branch that picks the variant source, then a
shared _task_aug_rows() helper does the dedup + row build (-~25 LOC).
* writer: _normalize_persistent_row / _normalize_event_row shared the
same camera-validate + struct construction. Extract _normalize_row(),
keeping the exact key order (the parquet struct schema is inferred
from insertion order, so timestamp must stay between style and camera).
docs: 'Which modules run' is now a table giving each module's on/off flag
(--plan.enabled / --interjections.enabled / --vqa.enabled) and what it
turns off.
Verified: 40 tests pass (incl. test_writer struct round-trip); pre-commit clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
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20c7a12dd5 |
annotate: remove dead code, document CLI options, compact config
Dead code (defined but never referenced anywhere in src/tests/examples):
* reader.py: keyframe_indices, episode_frame_timestamps, lookup_data_path,
and the now-orphaned gather_data_paths + episode_offsets_per_path
(lookup_data_path was their only caller).
* staging.py: iter_staged_episodes.
* writer.py: normalize_rows_for_writer.
* config.py VlmConfig: json_mode, batch_size, tensor_parallel_size,
gpu_memory_utilization, trust_remote_code — consumed only by the
in-process vllm/transformers backends that were removed; the openai
auto-serve path carries those vLLM flags via serve_command instead.
Kept max_model_len (still used as the serve-command default).
* config.py TaskAugAxesConfig.total property.
Docs: new 'Key options' section in annotation_pipeline.mdx — grouped
tables (dataset in/out, module toggles, --vlm.*, --plan.*, interjections
+ vqa) describing the flags users actually reach for, with defaults.
config.py: compact the verbose field comments + ActionRecordsConfig /
TaskAugAxesConfig docstrings; fix two stale 'verify' references (the
verify pass was removed — it's describe -> segment now) and the stale
'renders record back to subtask text' note (that path was removed).
vlm_client docstring no longer mentions the removed json_mode field.
Verified: tests/annotations + tests/datasets/test_language +
tests/scripts/test_lerobot_annotate (40 passed); pre-commit clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
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2af2402a0c |
docs(annotate): cleaner architecture diagram layout
Top-down flow (read episodes → 3 modules fan out → validator → writer → parquet) with aligned boxes, instead of the cramped bordered version. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
||
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Pepijn
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7bec991cdf |
docs(annotate): friendlier rewrite + architecture diagram; drop reproducibility section
Rewrite annotation_pipeline.mdx in plainer, easier-to-read language (shorter sentences, active voice, a plain-text intro), add an ASCII 'How it fits together' architecture diagram, and remove the 'Reproducibility via seed and prompt hashes' section. Content/links are preserved; only wording and structure change. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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c6f682b3f4 |
annotate docs: install lerobot from main (post-merge wording)
The example already pins '@main'; update the doc step and the script docstring from 'the branch under test' to 'lerobot (from main)' now that the pipeline is merging to main. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
||
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Pepijn
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eba3ab3741 |
annotate: address review feedback — bug fixes, docs/code drift, naming, cleanup
Bugs
* validator: don't re-raise on unknown style. The second column_for_style
lookup (used to route persistent vs event) now sits in try/except so an
unknown style is recorded by _check_column_routing and skipped instead
of crashing the whole validation pass.
* general_vqa._target_cameras: when restrict_to_default_camera is set but
the configured camera_key isn't one the provider exposes, warn and fall
back to all cameras instead of returning a phantom key that KeyErrors
deep in frame decode.
* interjections: clamp interjection timestamps to frame_timestamps[0]
rather than a hardcoded 0.0 (datasets can start at non-zero t).
Docs / code drift
* annotation_pipeline.mdx: drop the phantom 'vocabulary discovery / phase
0 / --vocabulary.* / canonical_vocabulary.json' section (none of it
exists); describe the real describe->segment + coverage-stitch flow.
Soften the src/lerobot/tools/ + TOOL_REGISTRY reference to 'not part of
this PR' (matches tools.mdx, which already marks the runtime layer as
not-yet-implemented). Fix the --push_to_hub/--new_repo_id wording. Note
the default is now a single h200. Add a 'Contributing new modules'
section inviting module / prompt / quality contributions.
* executor docstring: six phases, no phantom phase 0.
run_hf_job.py
* add the Apache 2.0 license header (was flagged repeatedly).
* default to a single GPU: flavor=h200, parallel_servers=1, num_gpus=1
(scale to h200x4 noted in the docstring).
* pin the install to @main instead of the feature branch (won't break
after merge).
Naming / cleanup
* rename dest_repo_id -> new_repo_id across config / script / example /
test to match the LeRobot dataset edit tools.
* rename prompt templates module_N_*.txt -> descriptive (plan_*,
interjections_*, vqa.txt) and update every load_prompt() call.
* remove dead _messages_to_prompt (used only by the removed in-process
backends).
* declare _warned_decode_fail (frames) and _warned_no_camera (vqa) as
real init=False dataclass fields instead of getattr monkey-patches.
* scope bandit B607 to the two ffmpeg subprocess.run sites via
'# nosec B607' and drop it from the global skip list.
Tests
* fix stale canned-VLM markers ('ONE realistic interruption' ->
'compact interjection', 'Update the memory' -> 'compressed semantic
memory') and drop the dead 'concise hierarchical PLAN' plan responders
(plan generation is deterministic now) in run_e2e_smoke,
test_pipeline_recipe_render, test_modules.
* run_e2e_smoke now asserts interjection + speech rows are produced so a
stale marker can't silently pass again.
* drop remaining 'PR 1' / 'PR 2' references from test comments / names.
Verified: tests/annotations + tests/datasets/test_language +
tests/scripts/test_lerobot_annotate (31 passed); make-style E2E smoke
(interjections=1 speech_atoms=2); pre-commit (ruff, mypy, bandit,
prettier) clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
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b9a0187335 |
annotate: drop local in-process VLM backends — HF Jobs (openai) only for now
The shipped workflow is Hugging Face Jobs (examples/annotations/run_hf_
job.py): it serves the model with vLLM in the vllm/vllm-openai image and
the pipeline talks to it over the OpenAI-compatible API. The in-process
vllm / transformers local backends added surface (and the vllm
one pinned an old torch) without being part of that path, so they're
removed for now.
* vlm_client.make_vlm_client: keep only backend='openai' (+ 'stub'
rejected with the usual guidance). Requesting 'vllm'/'transformers'
now raises a clear 'not supported for now — use the HF Jobs flow'
error. Removed _make_vllm_client and _make_transformers_client.
* config: backend docstring updated (openai-only); default model_id
bumped to Qwen/Qwen3.6-27B to match run_hf_job.
* docs/annotation_pipeline.mdx: remove the '## Running locally'
section; the launcher description now says one vLLM server per GPU
over the OpenAI API, and the 'One Qwen-VL pass' note drops the
'vLLM/transformers fallback' wording.
Tests are unaffected (they construct StubVlmClient directly; nothing
referenced the removed backends).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
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870980efd6 | Merge branch 'main' into feat/language-annotation-pipeline | ||
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Jaimin
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d1b1c5c8cf |
docs: fix broken dataset script paths (datasets/v30 -> scripts) (#3695)
The docs pointed at src/lerobot/datasets/v30/, which does not exist. Both scripts actually live in src/lerobot/scripts/: - convert_dataset_v21_to_v30.py - augment_dataset_quantile_stats.py Updated the four references (one python -m module path and three file-path invocations) to the correct location, matching each script's own usage docstring. |
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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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Pepijn
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1417fd69b2 |
docs(annotate): prettier format annotation_pipeline.mdx
Quality-gate fix: ruff-format/markdown prettier hook reflow of the annotation pipeline doc. No content change. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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3662c41b85 |
Merge remote-tracking branch 'origin/main' into feat/language-annotation-pipeline
# Conflicts: # uv.lock |
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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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1e9a6d044d |
Merge remote-tracking branch 'origin/feat/language-annotation-pipeline' into feat/smolvla-on-steerable
# Conflicts: # src/lerobot/datasets/__init__.py # src/lerobot/policies/__init__.py # src/lerobot/policies/factory.py # src/lerobot/processor/render_messages_processor.py # uv.lock |
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Pepijn
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c37b1fc7d0 | Merge origin/feat/language-annotation-pipeline (8 fix(annotate) commits + vocabulary phase) | ||
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Pepijn
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9020635b14 |
Merge branch 'main' into feat/language-annotation-pipeline
Resolves conflicts from 32 commits on main: * docs/source/_toctree.yml — keep both new toc entries (annotation_pipeline + video_encoding_parameters). * docs/source/language_and_recipes.mdx — adopt main's section ordering (Layer 2 before "Temporal semantics") and float32 timestamp dtype to match the codebase. * src/lerobot/configs/__init__.py — keep both export sets (recipe + video encoder). * src/lerobot/datasets/dataset_metadata.py — drop redundant lazy imports (top-level imports cover both LANGUAGE_COLUMNS and DEFAULT_TOOLS); adopt main's @tools.setter for info.json write-back. * src/lerobot/datasets/feature_utils.py — call the real validate_feature_language() instead of returning "". * src/lerobot/datasets/language.py — float32 timestamps to match pa.float32() used in video_utils.py and the rest of the codebase. * src/lerobot/datasets/language_render.py — adopt main's unwrap_scalar() helper (drops two hand-rolled .item()/list unwrappers); float32 in docstring. * src/lerobot/processor/render_messages_processor.py — drop PR-local _scalar() helper, use shared unwrap_scalar(). * tests/datasets/test_language.py — adopt main's new float32 dtype + validate_feature_language warning tests. * tests/datasets/test_dataset_metadata.py — adopt main's new tools.setter persist/clear tests. * uv.lock — regenerated cleanly from main's resolver. 90 of 92 touched tests pass. Two pre-existing test failures (test_module1_plan_memory_subtask_smoke, test_module2_mid_episode_emits_paired_interjection_and_speech in tests/annotations/test_modules.py) are unrelated to this merge — that test file doesn't exist on main, so the failures originate on the branch and are addressed by the 8 newer fix(annotate) commits already on origin that will land in a follow-up. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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1ff10b935c |
Merge branch 'feat/language-annotation-pipeline' into feat/smolvla-on-steerable
Resolves conflicts from 66 commits on the base branch: * pyproject.toml — keep base's transformers>=5.4.0,<5.6.0; add the sentencepiece-dep entry pi052 (FAST action tokenizer) needs. * policies/__init__.py — keep pi052 export; drop the RewardClassifierConfig export that base removed. * policies/factory.py — docstring list resolution (keep pi052; drop reward_classifier, removed by base). * annotations/steerable_pipeline/executor.py — adopt base's renamed _ensure_annotation_metadata_in_info (it already advertises the say tool); drop pi052's older _ensure_tools_in_info call. * configs/train.py — keep pi052's vqa_target_fraction; adopt base's SampleWeightingConfig (legacy RA-BC inline params already covered by the migration shim base added). * scripts/lerobot_train.py — merge pi052's per-policy processor rebuild + dataset_repo_id pass-through with base's active_cfg / is_reward_model_training tightening, and re-route vqa-weighted sampler to active_cfg.drop_n_last_frames. * datasets/language_render.py — adopt base's _select_one + timestamp tolerance (drops pi052's stale _select_latest / per-style sort_key). * tests — adopt base's parametrized per-camera blend + tolerance test; drop pi052 tests that overlap with base's tighter rewrites; keep pi052's flow-only / VQA-blend coverage; add a test_canonical_recipe_loads check on subtask_mem_vqa_speech.yaml. * policies/pi052/processor_pi052.py — import RenderMessagesStep directly from render_messages_processor (base intentionally dropped it from lerobot.processor's re-exports). * uv.lock — regenerated cleanly from base + pi052's pocket-tts / beartype. All 67 touched tests pass (30 pi052 + 37 recipe / language-render / pipeline / render-messages). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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pepijn
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54221ceea2 |
feat(annotate): let the VLM decide vocabulary size
Hardcoding ``n_subtask_target=10`` and ``n_memory_target=6`` baked task complexity into the config — a simple pick-and-place needs ~6, a multi-step recipe needs ~20. The VLM already sees the clips, so let it pick the count itself from what's recurring across episodes. Drop both knobs from ``VocabularyConfig`` and the ``module_0_vocabulary`` prompt template. The prompt now says "decide the count yourself based on what you see — the smallest set that still covers every recurring phase" and adds an "each label must recur across the demos" rule so the VLM filters out one-off motions. Update the launcher script + docs to remove the old knobs. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Co-authored-by: Cursor <cursoragent@cursor.com> |
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pepijn
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86a7edc590 |
feat(annotate): phase 0 — derive canonical vocabulary from sample episodes
The pipeline previously emitted near-unique subtask + memory phrasings
per episode (free-form LLM rephrasing). On the downstream low-level
policy that collapses the action expert's conditioning to noise: every
episode pairs a different paraphrase with similar motions, so the
expert learns a flat scene-prior that ignores the subtask string —
then at inference the high-level head invents *yet another* paraphrase
and the expert produces tiny "uncertain hover" chunks.
Add a vocabulary-discovery phase (phase 0) that runs once per dataset:
- watches the first ``vocabulary.sample_episodes`` (default 3)
episode videos as one Qwen-VL prompt,
- asks the VLM to derive ~``n_subtask_target`` canonical imperative
subtask labels and ~``n_memory_target`` first-person past-tense
memory milestones that recur across the demos,
- persists them to ``meta/canonical_vocabulary.json`` (human-
inspectable, hand-editable), and
- wires the resulting ``Vocabulary`` into the ``plan`` module so
every per-episode subtask + memory call is constrained to those
exact strings (both as prompt-side instructions *and* post-VLM
validation: paraphrases snap to the closest canonical entry via
token-set overlap; below a 0.5 Jaccard floor the subtask is
dropped rather than warped into something semantically wrong).
Operator workflow:
- first run discovers the vocabulary, writes the JSON, and runs
the ``plan`` module against it,
- subsequent runs reuse the on-disk file (``reuse_existing=True``
default) so hand-edits stick,
- set ``--vocabulary.enabled=False`` to fall back to free-form
generation (the original behaviour).
The discovery prompt forbids gerunds / third-person / adverbs and
caps the lists to the requested counts, matching the Hi-Robot /
π0.6-MEM convention of small per-environment vocabularies. The
``plan`` module's subtask + memory prompts grow a conditional
``{vocabulary_block}`` slot rendered only when a vocabulary is
present; without one the templates collapse to their previous
free-form form.
Tests: 11 new unit tests under tests/annotations/test_vocabulary.py
cover the on-disk round-trip, discovery against the fixture dataset,
``reuse_existing`` short-circuit, paraphrase canonicalisation, off-
vocab subtask dropping, and the no-vocabulary pass-through path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
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