LibreVLA

Install

pip install "libreyolo[vla]"

Predict

from libreyolo import LibreVLA, SAMPLE_IMAGE # Python 3.12 or later. This demonstrates shapes with synthetic state.model = LibreVLA("smolvla-base", device="cpu")result = model.predict(SAMPLE_IMAGE, state=[0.0] * 6, instruction="pick up the object")print(result.actions.data.shape)print(result.actions.first)

The extra requires Python 3.12 or later. Supply real camera observations and the state representation used during training for meaningful actions. The library returns action values; the caller owns the robot control loop. Call reset() between episodes.

Train

SmolVLA

LibreVLA() selects SmolVLA. Training updates its action expert with the vision backbone frozen. Pass a LeRobot dataset ID or local v3 directory to train(data=...). Defaults are 5 epochs, batch 8, workers 0 and val_split=0.1.

ACT

LibreVLA("act") constructs an untrained action policy. Train it on a LeRobot dataset before prediction. It uses the dataset camera, state and action shapes without a language instruction. Its image backbone may download separately.

Diffusion Policy

LibreVLA("diffusion") also starts without pretrained policy weights. It keeps observation history between calls; reset() clears that history between episodes.

Validate

val(data=...) reports val/action_l1, val/action_mse, val/action_l1_first and per-dimension val/action_l1_dims. These compare predicted and recorded actions offline; they are not robot task success rates.

Checkpoints

Training saves directories containing policy weights, processors and libreyolo_vla.json. Reload a returned best-checkpoint directory with LibreVLA(path). See the policy API.

Licensing