LeRobot v0.6.0: Imagine, Evaluate, Improve

This new release is about closing the robot learning loop: policies that imagine the future before acting, reward models that tell you when your robot succeeds, a deployment CLI that turns failures into training data, and six new simulation benchmarks to measure it all. It also brings depth sensing, VLM-powered dataset annotation, custom video encoding, cloud training on HF Jobs, and a much leaner install.

TL;DR

LeRobot v0.6.0 introduces world model policies (VLA-JEPA, FastWAM, LingBot-VA) that learn to imagine the future, a wave of new VLAs (GR00T N1.7, MolmoAct2, EO-1, EVO1, Multitask DiT), and a new reward models API (Robometer, TOPReward). It ships six new simulation benchmarks unified under lerobot-eval, the lerobot-rollout CLI with DAgger-style human-in-the-loop corrections, FSDP training, and cloud training on HF Jobs. Datasets get depth support, an automatic language annotation pipeline, custom video encoding, and up to 2x faster data loading, all on top of a leaner installation.

Table of contents

• LeRobot v0.6.0: Imagine, Evaluate, Improve
  • TL;DR
  • Table of contents
  • World models: policies that imagine
    • VLA-JEPA
    • LingBot-VA
    • FastWAM
  • VLAs: the model zoo keeps growing
    • GR00T N1.7
    • MolmoAct2
    • EO-1
    • Multitask DiT
    • EVO1
  • Reward models: knowing when your robot succeeds
    • Robometer
    • TOPReward
  • Datasets: faster loading, richer data
    • Your codec, your rules
    • Depth support, end to end
    • Language annotations at scale
    • Up to 2x faster data loading
  • Benchmarks: one CLI to evaluate them all
  • Training & inference
    • lerobot-rollout: deployment gets its own CLI
    • FSDP: train models bigger than your GPU
    • Cloud training with HF Jobs
  • Codebase: leaner and cleaner
  • Community & ecosystem
  • Final thoughts

World models: policies that imagine

VLA-JEPA

lerobot-train \
  --policy.path=lerobot/VLA-JEPA-Pretrain \
  --dataset.repo_id=${HF_USER}/my_dataset \
  --policy.repo_id=${HF_USER}/my_finetuned_policy

Check out the VLA-JEPA documentation and the paper to learn more.