OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning
<|reference_start|>OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning: We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy. Using kinematic pose as a universal control interface, OmniH2O enables various ways for a human to control a full-sized humanoid with dexterous hands, including using real-time teleoperation through VR headset, verbal instruction, and RGB camera. OmniH2O also enables full autonomy by learning from teleoperated demonstrations or integrating with frontier models such as GPT-4. OmniH2O demonstrates versatility and dexterity in various real-world whole-body tasks through teleoperation or autonomy, such as playing multiple sports, moving and manipulating objects, and interacting with humans. We develop an RL-based sim-to-real pipeline, which involves large-scale retargeting and augmentation of human motion datasets, learning a real-world deployable policy with sparse sensor input by imitating a privileged teacher policy, and reward designs to enhance robustness and stability. We release the first humanoid whole-body control dataset, OmniH2O-6, containing six everyday tasks, and demonstrate humanoid whole-body skill learning from teleoperated datasets.<|reference_end|>
@article{he2024omnih2o:,
    title={OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body
  Teleoperation and Learning},
    author={Tairan He, Zhengyi Luo, Xialin He, Wenli Xiao, Chong Zhang, Weinan
  Zhang, Kris Kitani, Changliu Liu, Guanya Shi},
    journal={arXiv preprint arXiv:2406.08858},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08858},
    primaryClass={cs.RO cs.CV cs.LG cs.SY eess.SY}
}