Using EmbodiChain with LeRobot

Using EmbodiChain with LeRobot#

EmbodiChain integrates LeRobot for demonstration datasets and official ACT imitation learning. The bundled visual PushCube workflow collects Franka demonstrations, invokes LeRobot’s trainer and evaluates native policies in EmbodiChain simulation.

Select the dependency profile#

The repository’s pyproject.toml selects dependencies by Python version:

Workflow

Python

LeRobot

Core collection with the older dependency profile

3.10 / 3.11

0.4.4

Official ACT collection, training and simulation evaluation

3.12 workflow

lerobot[training]==0.6.1

Standalone offline export without EmbodiChain

>=3.12

lerobot[dataset]==0.6.1

Follow Installation for the simulator installation and the ACT example for its CUDA/PyTorch profile. Do not upgrade only LeRobot inside the older profile: Python, Torch, TorchVision and TorchCodec constraints must agree. Dataset storage version (v3.0) is separate from the library version (0.6.1).

Collect, train and evaluate ACT#

Run the example modules from the repository root after installing the Python 3.12 environment. The complete commands and qualification criteria live in the ACT walkthrough.

  1. Run python -m examples.learning.act.collect qualify to qualify the expert.

  2. Use its qualification report with collect collect to create independent training and validation datasets. Preserve each dataset’s report.json.

  3. Run python -m examples.learning.act.train with those two reports. The wrapper invokes lerobot.scripts.lerobot_train, records the recipe and fingerprints, and uses native LeRobot training state for recovery.

  4. Run python -m examples.learning.act.evaluate on the development panel to select a checkpoint, then evaluate that policy on held-out test scenes.

These module names identify workflow stages; use the walkthrough’s full arguments, seeds and fresh output directories. Smoke collection produces an unqualified corpus and is not evidence of learning acceptance.

The policy observes RGB and measured joints and predicts absolute arm targets plus a normalized gripper command. The adapter in embodichain_models.policy.act validates ordered data/control contracts. Loading uses native weights and processors together with embodichain_act.json; keep the complete pretrained_model/ directory or portable development best/ directory. Normalization, temporal ensembling and episode resets follow the official runtime. Historical custom ACT .pt checkpoints require retraining.

Use existing demonstration datasets#

For finalized expert recordings, first follow Validate, split, and recover expert datasets. Use Export expert data for LeRobot 0.6.1 when preparing the supported source format for the official 0.6.1 consumer, including timestamped subtask language and native depth. The exporter runs in a separate environment, preserves source evidence and checks the official reader before publishing its output.

Export does not choose a policy, convert action semantics or automatically apply the generated language recipe. Inspect feature names, units, action ordering, image dimensions and cadence before using a different upstream training recipe. The bundled ACT workflow uses its own collection reports and provenance checks; an arbitrary exported dataset is not automatically admitted by that wrapper.

Ownership and troubleshooting#

Symptom

Check

LeRobot import or binary codec failure

Install the complete matching Python/Torch/TorchCodec profile

ACT report or schema rejected

Use qualified collection reports with the matching task/action contract

Resume rejected

Preserve output ownership, source contents and recipe arguments

Policy loads but actions are wrong

Retain processors and manifest; check units, ordering and normalization

Dataset reads but language is missing from training

Explicitly apply the exported language recipe in the consumer

EmbodiChain owns collection, provenance and simulation integration. LeRobot owns its ACT model, optimizer, processors and native checkpoint state. See Choose a training framework for native RL/SL and Using EmbodiChain with RLinf for external RL.

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