In this paper, we present a Riemannian Motion Policy (RMP)flow-based whole-body control framework for improved dynamic legged locomotion. RMPflow is a differential geometry-inspired algorithm for fusing multiple task-space policies (RMPs) into a configuration space policy in a geometrically consistent manner. RMP-based approaches are especially suited for designing simultaneous tracking and collision avoidance behaviors and have been successfully deployed on serial manipulators. However, one caveat of RMPflow is that it is designed with fully actuated systems in mind. In this work, we, for the first time, extend it to the domain of dynamic-legged systems, which have unforgiving under-actuation and limited control input. Thorough push recovery experiments are conducted in simulation to validate the overall framework. We show that expanding the valid stepping region with an RMP-based collision-avoidance swing leg controller improves balance robustness against external disturbances by up to 53\% compared to a baseline approach using a restricted stepping region. Furthermore, a point-foot biped robot is purpose-built for experimental studies of dynamic biped locomotion. A preliminary unassisted in-place stepping experiment is conducted to show the viability of the control framework and hardware.

翻译：本文提出一种基于黎曼运动策略（RMP）流的全身控制框架，用于改进动态腿部运动。RMPflow是一种受微分几何启发的算法，能以几何一致的方式将多个任务空间策略（RMP）融合为配置空间策略。基于RMP的方法特别适合设计同时跟踪与避碰行为，并已成功应用于串联机械臂。然而，RMPflow的一个缺陷是其针对全驱动系统设计。本研究首次将其扩展至动态腿部系统领域，此类系统存在苛刻的欠驱动特性与控制输入限制。通过在仿真环境中进行全面的推倒恢复实验验证整体框架，结果表明：与采用受限落脚区域的基线方法相比，基于RMP避碰摆腿控制器扩展有效落脚区域后，对外部扰动的平衡鲁棒性提升高达53%。此外，为研究动态双足运动，本文专门搭建了点式足部双足机器人平台，并通过初步的无辅助原地踏步实验验证了控制框架与硬件的可行性。