Bringing Humanoids to LeRobot

LeRobot presents an open-source workflow for humanoid robot learning on the Unitree G1 that combines teleoperation, datasets, vision-language-action policies, and a fast whole-body controller. Building on OpenHLM, the system trains a π0.5 policy to predict 64-dimensional SONIC latent motion tokens from language, camera feeds, and robot state, while SONIC decodes these tokens into stable joint targets needed for balancing and locomotion. The project describes custom G1 hardware additions including grippers, cameras, and CAN equipment, as well as VR-based teleoperation that converts human motion into shared latent action representations. A can pick-and-place policy was trained from about 100 teleoperated episodes, while a separate depth-based ball-dodging experiment achieved 79.1% success in simulation but was not presented as a controlled hardware evaluation. The article also highlights support for alternative controllers, simulation, visualization, inverse kinematics, and proprioceptive data, alongside open-source teleoperation hardware such as the Homunculus glove and exoskeleton, affordable humanoid platforms, and a growing collection of whole-body humanoid datasets intended to make robot-learning tools and data more interoperable.

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