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：本文提出了一种基于黎曼运动策略（RMP）流的全身控制框架，以改进动态腿式运动的性能。RMPflow是一种受微分几何启发的算法，能够以几何一致的方式将多个任务空间策略（RMP）融合为构型空间策略。基于RMP的方法特别适用于设计同时追踪与避碰的行为，并已在串联机械臂上成功部署。然而，RMPflow的一个局限性在于其设计主要针对全驱动系统。在本研究中，我们首次将其扩展至动态腿式系统领域，这类系统具有难以处理的欠驱动特性和有限的控制输入。通过在仿真中开展全面的抗扰动实验，验证了整体框架的有效性。结果表明，与使用受限落脚区域的基线方法相比，基于RMP的避碰摆动腿控制器通过扩大有效落脚区域，使系统对外部干扰的平衡鲁棒性提升高达53%。此外，本文专门构建了一台点足双足机器人，用于动态双足运动的实验研究。通过初步的无辅助原地踏步实验，展示了该控制框架与硬件的可行性。