LeRobot v0.6.0: Imagine, Evaluate, Improve

Back to Articles LeRobot v0.6.0: Imagine, Evaluate, Improve Published July 7, 2026 Update on GitHub Upvote 1 Steven Palma imstevenpmwork Follow Pepijn Kooijmans pepijn223 Follow Caroline Pascal CarolinePascal Follow Khalil Meftah lilkm Follow Martino Russi nepyope Follow Nikodem Bartnik nikodembartnik Follow Nicolas Rabault Nico-robot Follow Thomas Wolf thomwolf Follow 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 The robotics world is asking a big question: do world models actually help robot policies? v0.

6.0 brings three policies to LeRobot to help answer that question. Each one learns to imagine the future as part of its training, and each takes a different path to keep that imagination affordable.

VLA-JEPA VLA-JEPA teaches a compact VLA (built on Qwen3-VL-2B) to predict the future in latent space while it learns to act: during training, a JEPA world model has to anticipate upcoming frames from the model's own actions. The trick is that the world model then disappears at inference, so you get world-model supervision at zero extra inference cost. Three ready-to-use checkpoints are on the Hub, including a DROID-pretrained base for fine-tuning: 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.

LingBot-VA LingBot-VA goes one step further: an autoregressive video-action model that predicts future video and actions together, chunk by chunk, and feeds real observations back in to keep its imagination grounded. You can even save what the robot imagined (--policy.save_predicted_video=true) and compare it with what actually happened.

Inference runs on a single 24–32 GB GPU. Check out the documentation and the paper for the technical details. FastWAM FastWAM asks the question in its paper title: do world action models need test-time future imagination?

It pairs a ~5B video-generation expert with a compact action expert in a single network, so the model literally learns to dream its own rollouts. At inference it skips the dreaming entirely and directly denoises action chunks. Fine-tune it from lerobot/fastwam_base, and read more in the documentation.

VLAs: the model zoo keeps growing GR00T N1.7 We upgraded our NVIDIA GR00T integration to GR00T N1.7, the newest open generation of NVIDIA's cross-embodiment foundation model.

N1.7 swaps the previous VLM for Cosmos-Reason2-2B (built on Qwen3-VL) feeding a flow-matching action head, and our integration is parity-tested against NVIDIA's original Isaac-GR00T implementation: same inputs, same outputs. Flash-attention is now optional, so pip install 'lerobot[groot]' just works, and you can load NVIDIA's published checkpoints directly.

GR00T N1.7 replaces N1.5 in LeRobot.

If you need N1.5, pin lerobot==0.5.

1. MolmoAct2 MolmoAct2, the Allen Institute for AI's vision-language-action model, is now ported into LeRobot with the full lifecycle covered: fine-tuning (full or LoRA), evaluation, and real-robot deployment. Ready-made checkpoints with calibration correction baked in mean you can run it zero-shot on an SO-100/101: lerobot-rollout \ --policy.

path=lerobot/MolmoAct2-SO100_101-LeRobot \ --robot.type=so100_follower \ --robot.port=/dev/ttyACM0 \ --robot.

cameras='{cam0: {type