Locomotion Beyond Feet
Tae Hoon Yang, Haochen Shi, Jiacheng Hu, Zhicong Zhang, Daniel Jiang, Weizhuo Wang, Yao He, Zhen Wu, Yuming Chen, Yifan Hou, Monroe Kennedy III, Shuran Song, C. Karen Liu

TL;DR
This paper presents a comprehensive system enabling humanoid robots to perform stable, whole-body locomotion across complex terrains by combining physics-based keyframe animation with reinforcement learning and hierarchical planning.
Contribution
It introduces a novel approach that integrates human-like keyframe motion encoding with reinforcement learning for robust, terrain-generalized humanoid locomotion in challenging environments.
Findings
Robust locomotion achieved on complex terrains
System generalizes across obstacle types and sequences
Successful real-world robot experiments conducted
Abstract
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,…
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Taxonomy
TopicsRobotic Locomotion and Control · Human Motion and Animation · Social Robot Interaction and HRI
