超越双脚的运动
Locomotion Beyond Feet
AI总结：
本文提出Locomotion Beyond Feet系统，通过结合关键帧动画与强化学习，实现人形机器人在复杂地形中的稳健全身运动与泛化能力。
摘要：
大多数人形机器人运动方法专注于基于腿部的步态，然而自然双足生物经常依赖双手、膝盖和肘部来建立额外的接触以在复杂环境中获得稳定性和支撑。本文介绍了Locomotion Beyond Feet，一个全面的全身人形运动系统，能够应对极其具有挑战性的地形，包括低空隙空间下的椅子、膝盖高度的墙壁、膝盖高度的平台以及陡峭的上下楼梯。我们的方法解决了两个关键挑战：接触丰富的运动规划和在多样化地形上的泛化能力。为此，我们结合了基于物理的关键帧动画与强化学习。关键帧编码了人类对运动技能的知识，具有特定的具身体验，并且可以方便地在模拟或硬件上验证，而强化学习将这些参考转化为稳健且物理准确的运动。我们进一步采用由地形特定的运动跟踪策略、故障恢复机制和基于视觉的技能规划器组成的分层框架。现实世界实验表明，Locomotion Beyond Feet实现了稳健的全身运动，并在障碍物大小、障碍物实例和地形序列上实现了泛化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
