Hierarchical World Models as Visual Whole-Body Humanoid Controllers
<|reference_start|>Hierarchical World Models as Visual Whole-Body Humanoid Controllers: Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visual observations further exacerbates this difficulty. In this work, we explore highly data-driven approaches to visual whole-body humanoid control based on reinforcement learning, without any simplifying assumptions, reward design, or skill primitives. Specifically, we propose a hierarchical world model in which a high-level agent generates commands based on visual observations for a low-level agent to execute, both of which are trained with rewards. Our approach produces highly performant control policies in 8 tasks with a simulated 56-DoF humanoid, while synthesizing motions that are broadly preferred by humans. Code and videos: https://nicklashansen.com/rlpuppeteer<|reference_end|>
@article{hansen2024hierarchical,
    title={Hierarchical World Models as Visual Whole-Body Humanoid Controllers},
    author={Nicklas Hansen, Jyothir S V, Vlad Sobal, Yann LeCun, Xiaolong Wang,
  Hao Su},
    journal={arXiv preprint arXiv:2405.18418},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18418},
    primaryClass={cs.LG cs.CV cs.RO}
}