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.

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.

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.

The Multitask Diffusion Transformer policy brings the TRI Large Behavior Models recipe to LeRobot: a ~450M-parameter diffusion transformer conditioned on CLIP vision and language embeddings, so one model learns many tasks selected via natural language. It supports both diffusion and flow-matching objectives, and it is small enough to train yourself. See the documentation .

VLAs don't have to be huge. EVO1 packs its policy into 0.77B parameters, an InternVL3-1B backbone with a flow-matching action head, small enough to fine-tune and run in real time on modest GPUs. It ships with two-stage fine-tuning and Real-Time Chunking support out of the box. See the EVO1 documentation and the paper .

TOPReward goes fully zero-shot: no reward weights at all. It wraps an off-the-shelf VLM (Qwen3-VL) and reads the log-probability of the token "True" given the trajectory video and the task instruction. Any capable VLM becomes a reward function.

Your dataset stops being one task string per episode. LeRobot datasets now natively store rich language annotations (timestamped subtasks, plans, memory, corrections, speech, and per-camera VQA pairs), and the new lerobot-annotate CLI fills them in automatically using a VLM that watches your episodes:

EO-1, a VLA pretrained upstream on interleaved vision-text-action data, joins LeRobot: a Qwen2.5-VL-3B backbone with a flow-matching action head, contributed by one of the paper's own authors. Train it with the standard lerobot-train workflow using --policy.type=eo1 . Details in the documentation and the paper .

Deploying a policy used to be a hack on top of lerobot-record . The new lerobot-rollout CLI makes deployment its own workflow, with pluggable strategies and inference backends (including Real-Time Chunking for slow compatible VLAs). The base strategy just runs the policy. sentry records continuously, rotating episodes and uploading to the Hub as it goes. highlight keeps a ring buffer and saves the last N seconds when you hit a key, so an interesting moment is never lost. episodic mirrors the classic episode/reset recording workflow. And dagger turns deployment into data collection.

Recording is no longer stuck with one hard-coded codec. The new --dataset.rgb_encoder.* options expose the full encoding surface (codec, quality, pixel format, GOP, presets), and vcodec=auto probes for hardware encoders like NVENC, VideoToolbox, VAAPI, and QSV before falling back to the default software AV1 encoder. For existing datasets, one command re-encodes everything:

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:

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 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 .