四足运动的快速身体适配

中文摘要 AI 辅助

人类会随着年龄增长、受伤或携带负载导致身体变化时，轻易调整自身动作，但基于学习的机器人策略在硬件属性发生变化时往往失效。我们提出一种用于四足运动的在线身体适配框架，该框架可从短期交互历史中推断身体参数，并基于推断出的硬件状态进行控制。我们的方法将在身体随机化条件下训练的通用策略，与一个轻量级适配模块相结合，该模块能在半秒内识别物理变化。我们评估了两种代表性的身体变化形式：关节范围约束和躯干质量变化，分别对应关节级运动学退化和身体级动态变化。在仿真环境中，该模块能准确估计这些变化，实现的闭环控制性能明显优于直接基于交互历史调整的策略。在真实的Unitree Go2机器人上，我们的系统在应对评估的严重变化时仍能保持稳定运动，包括一条腿完全锁定和5公斤负载，而未采用适配的方法会失效。这些结果证明了显式在线身体识别在快速适配关节限制和负载质量变化方面的实用性，为处理更广泛的不确定、退化或变化的机器人硬件迈出了一步。

英文摘要

Humans readily adapt their movements as their bodies change through aging, injury, or load carrying, but learning-based robot policies often break when hardware properties shift. We introduce an online embodiment adaptation framework for quadrupedal locomotion that infers embodiment parameters from short interaction histories and conditions control on the inferred hardware state. Our method pairs a generalist policy trained under embodiment randomization with a lightweight adaptation module that identifies physical changes within half a second. We evaluate two representative forms of embodiment variation: joint-range constraints and trunk-mass changes, corresponding to joint-level kinematic degradation and body-level dynamic variation. In simulation, the module accurately estimates these changes and enables closed-loop control that substantially outperforms policies conditioned directly on interaction history. On a real Unitree Go2 robot, our system maintains stable locomotion under severe instances of the evaluated changes, including a fully locked leg and a 5 kg payload, where non-adaptive methods fail. These results demonstrate the practicality of explicit online embodiment identification for rapid adaptation to joint-limit and payload-mass changes, and provide a step toward handling broader forms of uncertain, degraded, or changing robot hardware.

发表机构

• UC San Diego（加州大学圣迭戈分校）
• Stanford University（斯坦福大学）
• TU Darmstadt（达姆施塔特工业大学）
• Sudo AI GmbH（Sudo AI 有限公司）
• German Research Center for AI (DFKI)（德国人工智能研究中心）
• Robotics Institute Germany（德国机器人研究所）

机构由 AI 辅助整理，请以论文原文为准。