外观
论文arXiv 2610.1793015 天前
面向精确落足的在线动力学感知运动
原文链接：Locomotion with Online Awareness of Dynamics for Precision Footholds（arXiv 2610.17930 · 发布日期：2026-10-13）
Locomotion with Online Awareness of Dynamics for Precision Footholds
针对未知载荷下窄梁精确接触困难，LOAD结合显式惯性估计与隐式动力学特征，并在仿真和硬件窄梁通行中验证。
核心要点
- 未知载荷会改变机器人动力学，在落足裕度小的窄梁上增加精确接触难度。
- LOAD用循环模块从本体感知推断躯干惯性变化和任务相关历史。
- 将高度图教师蒸馏为基于深度的学生，并融合显式估计与隐式特征。
- 仿真和硬件结果显示，隐式特征加速训练，显式惯性估计提升通行成功率。
验证范围与局限：验证范围为仿真及硬件窄梁通行，覆盖不同梁宽和载荷配置，含单侧6/9 kg和均布11 kg载荷。
英文摘要 (Abstract)
Unknown payloads alter robot dynamics and make precise contact execution difficult when foothold margins are small. We present LOAD, a perceptive locomotion framework that combines explicit inertial estimates with implicit dynamics features for narrow-beam traversal. Separate recurrent modules infer torso inertial changes and task-relevant history from proprioception, while a height-map teacher is distilled into a depth-based student. We extensively evaluate LOAD and its variants in simulation and on hardware across beam widths and payload configurations. The results show that implicit dynamics features accelerate training, while explicit inertial estimation further improves traversal success; combining both enables LOAD to approach the performance of a privileged policy supplied with ground-truth inertial parameters. Hardware experiments demonstrate traversal with unilateral payloads of 6 kg on a 10 cm beam and 9 kg on a 15 cm beam, and an evenly distributed 11 kg payload on a 15 cm beam. These results support combining physical estimates and history features for precision locomotion under unknown loads.
作者：Kunzhao Ren, Zhishuo Zhu, Xiaobin Xiong
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