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.

翻译：大多数人形机器人的运动方法聚焦于基于腿部的步态，然而自然双足生物在复杂环境中常依赖手、膝和肘部建立额外接触点以获取稳定性与支撑。本文提出"超越足部的运动"——一套面向极端挑战性地形的全身人形机器人运动系统，涵盖椅子下方的低矮空间、齐膝高度的墙体、齐膝平台以及陡峭的上下楼梯场景。我们的方法着力解决两大核心挑战：密集接触的运动规划及跨地形泛化能力。为此，我们将基于物理原理的关键帧动画与强化学习相结合。关键帧编码了人类对运动技能的先验知识，具有本体特异性，可在仿真或硬件平台上直接验证；而强化学习则将这些参考动作转化为鲁棒且物理精确的运动。我们进一步采用分层框架，包含地形特异性运动跟踪策略、故障恢复机制及基于视觉的技能规划器。真实世界实验表明，"超越足部的运动"系统实现了鲁棒的全身运动能力，并在障碍物尺寸、障碍物实例及地形序列等方面展现出卓越的泛化性能。