Most locomotion methods for humanoid robots focus on leg-based gaits, yet natural bipeds frequently rely on hands, knees, and elbows to establish additional contacts for stability and support in complex environments. This paper introduces Locomotion Beyond Feet, a comprehensive system for whole-body humanoid locomotion across extremely challenging terrains, including low-clearance spaces under chairs, knee-high walls, knee-high platforms, and steep ascending and descending stairs. Our approach addresses two key challenges: contact-rich motion planning and generalization across diverse terrains. To this end, we combine physics-grounded keyframe animation with reinforcement learning. Keyframes encode human knowledge of motor skills, are embodiment-specific, and can be readily validated in simulation or on hardware, while reinforcement learning transforms these references into robust, physically accurate motions. We further employ a hierarchical framework consisting of terrain-specific motion-tracking policies, failure recovery mechanisms, and a vision-based skill planner. Real-world experiments demonstrate that Locomotion Beyond Feet achieves robust whole-body locomotion and generalizes across obstacle sizes, obstacle instances, and terrain sequences.
@article{arxiv.2601.03607,
title = {Locomotion Beyond Feet},
author = {Tae Hoon Yang and Haochen Shi and Jiacheng Hu and Zhicong Zhang and Daniel Jiang and Weizhuo Wang and Yao He and Zhen Wu and Yuming Chen and Yifan Hou and Monroe Kennedy and Shuran Song and C. Karen Liu},
journal= {arXiv preprint arXiv:2601.03607},
year = {2026}
}