Robot policies

Robot policies return an action chunk from camera observations and robot state.

Models

LibreVLA provides SmolVLA, ACT and Diffusion Policy. SmolVLA conditions on a language instruction. ACT and Diffusion Policy train without a pretrained policy base.

Predict

Python

from libreyolo import LibreVLA, SAMPLE_IMAGE # Python 3.12+. Synthetic observation for checking the API.model = LibreVLA(device="cpu")result = model(SAMPLE_IMAGE, state=[0.0] * 6, instruction="pick up the object")print(result.actions.data.shape)

Use real robot state and camera frames for meaningful predictions. Actions.data is a float32 array of shape (T, D); Actions.first is the first action. The payload also stores action names, control rate and instruction when available. Call reset() between episodes.

Dataset format

Use a LeRobot v3 dataset directory or Hub dataset ID. Training reads episode boundaries, camera features, state, actions and timestamps from that dataset. There is no detection-style YAML for this task.

Train

Install libreyolo[vla] on Python 3.12 or later. Call model.train(data=...); defaults are 5 epochs, batch 8 and a 0.1 held-out episode fraction.

Validate

Offline validation reports val/action_l1, val/action_mse, val/action_l1_first and per-dimension val/action_l1_dims. These are errors against recorded actions in dataset units. They do not measure task success on a robot.

Export

Policy export is not supported. Reload a training checkpoint directory through LibreVLA(path).