mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-06 17:41:47 +00:00
Caroline Pascal
293a8d9a77
* 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>
193 lines
4.6 KiB
YAML
193 lines
4.6 KiB
YAML
- sections:
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- local: index
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title: LeRobot
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- local: installation
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title: Installation
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- local: cheat-sheet
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title: Cheat sheet
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title: Get started
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- sections:
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- local: il_robots
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title: Imitation Learning for Robots
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- local: lelab
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title: LeLab - Lerobot GUI
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- local: bring_your_own_policies
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title: Adding a Policy
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- local: integrate_hardware
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title: Bring Your Own Hardware
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- local: hilserl
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title: Train a Robot with RL
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- local: hilserl_sim
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title: Train RL in Simulation
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- local: multi_gpu_training
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title: Multi GPU training
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- local: hil_data_collection
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title: Human In the Loop Data Collection
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- local: peft_training
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title: Training with PEFT (e.g., LoRA)
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- local: rename_map
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title: Using Rename Map and Empty Cameras
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title: "Tutorials"
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- sections:
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- local: hardware_guide
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title: Compute Hardware Guide
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- local: torch_accelerators
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title: PyTorch accelerators
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title: "Compute & Hardware"
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- sections:
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- local: lerobot-dataset-v3
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title: Using LeRobotDataset
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- local: porting_datasets_v3
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title: Porting Large Datasets
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- local: using_dataset_tools
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title: Using the Dataset Tools
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- local: language_and_recipes
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title: Language Columns and Recipes
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- local: tools
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title: Tools
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- local: annotation_pipeline
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title: Annotation Pipeline
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- local: video_encoding_parameters
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title: Video encoding parameters
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- local: streaming_video_encoding
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title: Streaming Video Encoding
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title: "Datasets"
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- sections:
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- local: act
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title: ACT
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- local: smolvla
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title: SmolVLA
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- local: pi0
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title: π₀ (Pi0)
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- local: pi0fast
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title: π₀-FAST (Pi0Fast)
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- local: pi05
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title: π₀.₅ (Pi05)
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- local: molmoact2
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title: MolmoAct2
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- local: vla_jepa
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title: VLA-JEPA
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- local: eo1
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title: EO-1
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- local: lingbot_va
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title: LingBot-VA
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- local: fastwam
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title: FastWAM
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- local: evo1
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title: EVO1
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- local: groot
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title: NVIDIA GR00T
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- local: xvla
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title: X-VLA
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- local: multi_task_dit
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title: Multitask DiT Policy
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- local: walloss
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title: WALL-OSS
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title: "Policies"
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- sections:
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- local: sarm
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title: SARM
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- local: robometer
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title: ROBOMETER
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- local: topreward
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title: TOPReward
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title: "Reward Models"
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- sections:
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- local: inference
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title: Policy Deployment (lerobot-rollout)
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- local: async
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title: Use Async Inference
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- local: rtc
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title: Real-Time Chunking (RTC)
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title: "Inference"
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- sections:
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- local: envhub
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title: Environments from the Hub
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- local: envhub_leisaac
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title: Control & Train Robots in Sim (LeIsaac)
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title: "Simulation"
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- sections:
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- local: adding_benchmarks
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title: Adding a New Benchmark
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- local: libero
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title: LIBERO
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- local: libero_plus
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title: LIBERO-plus
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- local: metaworld
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title: Meta-World
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- local: robotwin
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title: RoboTwin 2.0
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- local: robocasa
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title: RoboCasa365
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- local: robocerebra
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title: RoboCerebra
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- local: robomme
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title: RoboMME
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- local: envhub_isaaclab_arena
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title: NVIDIA IsaacLab Arena Environments
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- local: vlabench
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title: VLABench
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title: "Benchmarks"
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- sections:
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- local: introduction_processors
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title: Introduction to Robot Processors
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- local: debug_processor_pipeline
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title: Debug your processor pipeline
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- local: implement_your_own_processor
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title: Implement your own processor
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- local: processors_robots_teleop
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title: Processors for Robots and Teleoperators
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- local: env_processor
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title: Environment Processors
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- local: action_representations
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title: Action Representations
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title: "Robot Processors"
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- sections:
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- local: so101
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title: SO-101
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- local: so100
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title: SO-100
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- local: koch
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title: Koch v1.1
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- local: lekiwi
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title: LeKiwi
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- local: hope_jr
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title: Hope Jr
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- local: reachy2
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title: Reachy 2
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- local: unitree_g1
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title: Unitree G1
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- local: earthrover_mini_plus
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title: Earth Rover Mini
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- local: omx
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title: OMX
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- local: openarm
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title: OpenArm
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- local: rebot_b601
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title: reBot B601-DM
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title: "Robots"
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- sections:
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- local: phone_teleop
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title: Phone
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- local: isaac_teleop
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title: Isaac Teleop
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title: "Teleoperators"
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- sections:
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- local: cameras
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title: Cameras
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title: "Sensors"
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- sections:
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- local: notebooks
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title: Notebooks
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- local: feetech
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title: Updating Feetech Firmware
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- local: damiao
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title: Damiao Motors and CAN Bus
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title: "Resources"
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- sections:
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- local: contributing
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title: Contribute to LeRobot
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- local: backwardcomp
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title: Backward compatibility
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title: "About"
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