LeRobot
Follow one contract chain through LeRobot: embodiment, dataset, processor, checkpoint, runtime observations and actions, then evaluation. Find the first contract that does not match.
Establish the contract
- For a new manipulation app or first pretrained-policy demo, read architect before creating an environment or training pipeline. It finds the saved apps checkout and selects a compatible baseline. If selection already happened, continue here. Existing dataset, training, or evaluation work does not need onboarding; an inference-only request does not authorize training.
- Inspect the installed LeRobot version and current CLI help before writing flags. Dataset formats, policy families, scripts, and extras change quickly.
- Match the robot's state, action space, cameras, rates, and task to the dataset. A policy adapts to those features; it cannot repair a mismatched embodiment.
- Inspect every checkpoint's configuration and processor files, not only its weights. Base and fine-tuned checkpoints from one family can expect different camera layouts.
- Keep Hub identity, transfer, publication, and Jobs lifecycle at the Hugging Face boundary. Keep source-selection strategy in data.
Prove the loop cheaply
- Start with a small shipped policy and a known dataset/environment pair.
- Run a short train that writes a checkpoint, then load that exact checkpoint through evaluation. Completion and numeric metrics are the smoke-test result; policy quality is not.
- Confirm loss, saved processors, input/output shapes, rollout metrics, and video or real-robot behavior before increasing steps or hardware cost.
- Treat real-hardware rollout as a new safety boundary even when simulation evaluation passed.
Go deeper only when needed
- For loading, recording, editing, migration, and episode visualization, read references/datasets.md.
- For policy choice, camera remapping, training, compute sizing, and remote Jobs behavior, read references/policies-and-training.md.
- For simulation evaluation, headless rendering, EnvHub, or real-hardware rollout, read references/eval-and-sim.md.
- When a checkpoint, feature contract, evaluation worker, dependency, or remote run fails, start with FAILURES.md.
- The concrete PushT examples are useful only when that smoke path matches the application: load dataset and train ACT.
- Use the current LeRobot documentation and source for version-sensitive APIs and the shipped policy/environment list.
Done
- The target dataset loads, the checkpoint carries its processors, evaluation exercises matching observations and actions, and measured results justify any longer run or hardware deployment.
