LeRobot published version v0.6.0 on July 7, 2026, introducing new world model policies, Vision-Language-Action (VLA) models, a reward models application programming interface (API), simulation benchmarks, and command-line interface (CLI) tools. The update includes performance enhancements, such as up to 2x faster data loading and a streamlined installation process.

The new world model policies include VLA-JEPA, FastWAM, and LingBot-VA. VLA-JEPA is built on Qwen3-VL-2B and utilizes a JEPA world model during training to anticipate future frames based on the model's actions, though this world model is not employed during inference. Three VLA-JEPA checkpoints are available on the Hub, including a DROID-pretrained base model engineered for fine-tuning. LingBot-VA is an autoregressive video-action model designed to predict video and actions concurrently, feeding real observations back into the system to maintain prediction accuracy. Users can save predicted video output using the parameter --policy.save_predicted_video=true, and its inference runs on a single 24–32 GB graphics processing unit (GPU). FastWAM incorporates an approximately 5B video-generation expert with a compact action expert within a single network, bypassing future imagination during inference to directly denoise action chunks.

Version v0.6.0 also introduces new Vision-Language-Action models including GR00T N1.7, MolmoAct2, EO-1, EVO1, and Multitask DiT. LeRobot upgraded its NVIDIA GR00T integration to GR00T N1.7, which replaces the previous GR00T N1.5 version. GR00T N1.7 features Cosmos-Reason2-2B, built on Qwen3-VL, as its VLM, along with a flow-matching action head. The LeRobot GR00T N1.7 integration has been parity-tested against NVIDIA's original Isaac-GR00T implementation, and flash-attention is an optional feature. Users can pin lerobot==0.5.1 to access GR00T N1.5. MolmoAct2, a VLA model from the Allen Institute for AI, has been ported into LeRobot with support for fine-tuning, evaluation, and real-robot deployment. Its ready-made checkpoints include calibration correction, and it can operate zero-shot on an SO-100/101 robot. MolmoAct2 inference requires approximately 12 GB of memory at bf16 precision, and LoRA fine-tuning fits on a single 24 GB GPU. EO-1, contributed by one of its paper's authors, is a VLA model pretrained on interleaved vision-text-action data, employing a Qwen2.5-VL-3B backbone with a flow-matching action head. EVO1 has 0.77 billion parameters, using an InternVL3-1B backbone with a flow-matching action head, and supports two-stage fine-tuning and Real-Time Chunking. Multitask DiT is a diffusion transformer policy based on the TRI Large Behavior Models recipe, featuring approximately 450 million parameters and conditioned on CLIP vision and language embeddings. It supports both diffusion and flow-matching objectives.

LeRobot v0.6.0 includes a new unified reward models API, lerobot.rewards, featuring Robometer and TOPReward. Robometer is a pretrained general-purpose reward model that assesses task progress and success from raw video and language instructions without requiring task-specific training. Built on Qwen3-VL-4B, Robometer was trained using trajectory comparisons over a dataset of more than one million robot trajectories. TOPReward operates without reward weights, wrapping an off-the-shelf VLM like Qwen3-VL and reading the log-probability of the token "True" given the trajectory video and task instruction. Both Robometer and TOPReward ship with labeling scripts that write per-frame progress curves into datasets.

The release also ships six new simulation benchmarks, all unified under lerobot-eval, and the lerobot-rollout CLI tool, which includes DAgger-style human-in-the-loop corrections. Support for FSDP training and cloud training on HF Jobs has been added. Datasets now include depth sensing support, automatic language annotation pipelines, and custom video encoding capabilities.