---

name: lerobot description: "Route LeRobot robotics workflows for datasets, policies, training, evaluation, simulation, physical robot control, and package extensions with verified configuration and safety gates." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LeRobot

Use this repo skill when a task uses LeRobot, lerobot-* commands, LeRobotDataset, robot-learning policies, robot/camera/motor control, or the LeRobot plugin and service interfaces. It is an operating guide for a later Researcher: inspect and plan first, then execute only the requested bounded workflow. Do not treat a package import or CLI help check as proof that a specific robot, simulator, model checkpoint, codec, or CUDA policy is runnable.

First gates

1. Record the package version, Python version (LeRobot 0.6.2 requires Python 3.12+), source of data/checkpoint, device/backend, optional extras, output location, time/compute budget, and whether network, credentials, simulator assets, or physical actuation are authorized.

2. Prefer a private environment and the smallest feature extra. Install the public package with pip install lerobot and add only the documented extra for the chosen route (dataset, training, hardware, viz, a policy, an environment, or a service). Do not use lerobot[all] as a first fix.

3. Run lerobot-info or the bundled environment probe before deeper work. A minimal import is:

   import lerobot
   from lerobot.datasets.lerobot_dataset import LeRobotDataset
   print(lerobot.__version__)
   

4. Keep local, deterministic checks separate from Hub downloads, remote jobs, W&B logging, model downloads, simulator asset acquisition, long training, and hardware commands. Ask for explicit consent before those side effects.

5. If the repository checkout or package version differs from the snapshot in repo-provenance.md, refresh this skill before relying on version-sensitive instructions.

Route the request

• Dataset files, episodes, features, Parquet/MP4, streaming, stats, transforms, visualization, conversion, or dataset edits: read dataset-workflows.
• Policy type/checkpoint, processors, device placement, training, evaluation, inference, PEFT, or rollout planning: read policy-training-inference.
• Robot, motor, camera, teleoperator, calibration, serial/CAN, teleoperation, recording, replay, or physical deployment: read robot-control-data-collection and require its safety gates.
• Gymnasium environment, LIBERO/MetaWorld/PushT/RobotWin/VLABench/RoboCasa, HIL-SERL, RL, or reward model: read simulation-and-rl.
• Custom policy/processor/plugin, async inference, gRPC transport, annotation, HF Jobs, or service endpoint: read extensions-and-services.

For a multi-stage task, load the dataset route before policy training, and load the policy route before physical rollout. Cross-route handoffs must name validated feature shapes/statistics, device, checkpoint, environment, and side-effect approvals; never assume a preceding route proved them.

Cross-cutting failure rules

Use quick-reference.md for terminology, extra selection, and backend boundaries. Use troubleshooting.md when installation/import, optional dependencies, configuration, codecs, Hub/network, CUDA, or a CLI fails. Run validate_skill_links.py only when checking this skill tree itself; it must report no external runtime links.

Stop and report blocked rather than guessing when a required dataset field, checkpoint processor, simulator asset, optional package, GPU backend, camera, serial/CAN device, credential, or safety control is unavailable. A CPU import may validate CPU packaging but cannot validate GPU-only or physical behavior.