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.
@article{arxiv.2405.18418,
title = {Hierarchical World Models as Visual Whole-Body Humanoid Controllers},
author = {Nicklas Hansen and Jyothir S and Vlad Sobal and Yann LeCun and Xiaolong Wang and Hao Su},
journal= {arXiv preprint arXiv:2405.18418},
year = {2025}
}
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Code and videos at https://nicklashansen.com/rlpuppeteer