@RemiCadene: Wow so much open data!
Summary
LeRobot released the largest open-source humanoid teleoperation dataset, re-encoded into the LeRobot format for efficient streaming and reduced storage footprint.
View Cached Full Text
Cached at: 06/26/26, 02:05 AM
Wow so much open data!
LeRobot (@LeRobotHF): The largest open-source humanoid teleop dataset EVER just dropped on @LeRobotHF @huggingface
https://t.co/RrUlR5scuz
We re-encoded the full dataset into LeRobot format: ~10TB → ~2TB, no loss of fidelity. Same trajectories, a fraction of the footprint, far easier to stream and
Similar Articles
@BitRobotNetwork: 1/ Introducing HIW-500 (Humanoids-in-the-Wild 500): the largest open-source humanoid teleop dataset collected in real h…
Introducing HIW-500, the largest open-source humanoid teleoperation dataset collected in real homes, with over 500 hours and 23K+ episodes across 12 homes in Southeast Asia.
LeRobot v0.5.0: Scaling Every Dimension
LeRobot v0.5.0 is a major release featuring support for Unitree G1 humanoid robots, new policy architectures (Pi0-FAST VLAs, Real-Time Chunking), streaming video encoding for 3x faster training, and EnvHub for loading simulation environments from Hugging Face Hub.
@lukas_m_ziegler: Another win for open-source robotics! @huggingface just released a fully open-source humanoid robot, and you can build …
Hugging Face released LeRobot Humanoid, an open-source, low-cost ($2,500) 3D-printed humanoid robot platform, including hardware, simulation, and control tools for robot learning research.
@NVIDIARobotics: Robotics is built on collaboration. Hear @huggingface CSO & Co-founder Thomas Wolf explain how open source gives develo…
LeRobot v0.6.0 introduces new world models, reward models, faster data loading, and improved benchmarks for robot learning, closing the robot learning loop.
LeRobot v0.6.0: Imagine, Evaluate, Improve
LeRobot v0.6.0 is a major release of Hugging Face's robot learning library, adding world model policies (VLA-JEPA, FastWAM, LingBot-VA), new VLAs, reward models, six simulation benchmarks, depth sensing, VLM-powered dataset annotation, custom video encoding, cloud training, and a deployment CLI for human-in-the-loop corrections.