发表机构
Florida Institute for Human and Machine Cognition; University of West Florida（佛罗里达人类与机器认知研究所; 西佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种利用学习模型快速评估手部接触点的人形机器人反应式稳定方法，通过两阶段规划选择最佳支撑区域和点，仿真和硬件实验均显著提升抗冲击能力并缩短稳定时间。
AI 中文摘要
我们提出了一种规划与控制方法，用于在低稳定性场景中反应式地利用手部接触来稳定人形机器人，在这些场景中，仅使用脚部接触可能导致摔倒。在机器人可达工作空间内对候选接触点进行采样，并通过滚动质心动力学经过碰撞前、碰撞和碰撞后阶段来计算预览。基于碰撞后阶段的压力中心（CoP）控制权限对采样点进行评分。我们方法的核心是一个学习模型，用于预测碰撞后阶段的CoP区域，与传统基于优化的方法相比，该模型能够快速评估候选接触点。所提出的规划器分为两个阶段：第一阶段选择最佳的支撑区域，第二阶段在该区域内计算最佳支撑点。我们的仿真结果表明，与不使用手部接触的恢复相比，脉冲韧性平均提高了89%，与朴素规划策略（最近可达区域）相比提高了17%。我们在硬件上验证了我们的框架，在站立和行走时进行了推力测试。站立试验显示，与朴素手部放置相比，稳定时间平均减少了43%，行走试验显示，与基线恢复（不使用手部接触）相比，稳定时间减少了18%。
英文摘要
We present a planning and control approach to reactively use hand contacts to stabilize a humanoid in low stability scenarios, where only using feet contacts may result in a fall. Candidate contacts are sampled within the robot's reachable workspace, and a preview is computed by rolling out the centroidal dynamics through pre-impact, impact and post-impact phases. Sampled points are scored based on the Center of Pressure (CoP) control authority at the post-impact phase. Central to our approach is a learned model of the robot's CoP region during post-impact, which enables rapid evaluation of candidate contact points compared to traditional optimization-based methods. The presented planner has two stages: the first selects an optimal bracing region and the second computes an optimal bracing point within the region. Our simulation results demonstrate an average increase in impulse resilience of 89% over recovery without hand contacts and 17% over a naive planning strategy (closest reachable region). We validate our framework on hardware, performing push tests while standing and walking. The standing trials show an average 43% reduction in stabilization time compared to naive hand placement and the walking trials demonstrate a 18% reduction compared to baseline recovery (without hand contacts).
