冲击准备：基于简化模型的人形机器人稳健推倒恢复与行走
Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models
AI总结：
提出统一框架，结合单刚体模型预测控制与混合线性倒立摆动力学，利用环境（如墙壁）和手臂支撑实现人形机器人动态行走中的推倒恢复，在高达0.5m/s行走速度下抵抗100N持续0.2s的推力。
摘要：
行走过程中的推倒恢复将促进人形机器人在以人为中心的环境中的部署。本文提出了一个用于人形机器人行走控制和推倒恢复的统一框架，在动态行走时利用手臂进行推倒恢复。关键创新在于利用环境（如墙壁）辅助推倒恢复，通过将单刚体模型预测控制（SRB-MPC）与混合线性倒立摆（HLIP）动力学相结合，实现稳健的行走、推倒检测和恢复，利用机器人手臂支撑墙壁并动态调整期望接触力和步态模式。在人形机器人上的大量仿真结果表明，与单独使用HLIP相比，该方法在扰动抑制和跟踪性能方面均有提升，机器人能够在指令速度高达0.5m/s行走时，从100N持续0.2s的推力中恢复。在倾斜墙壁和多方向推力场景中进一步验证了鲁棒性。
英文摘要：
Push recovery during locomotion will facilitate the deployment of humanoid robots in human-centered environments. In this paper, we present a unified framework for walking control and push recovery for humanoid robots, leveraging the arms for push recovery while dynamically walking. The key innovation is to use the environment, such as walls, to facilitate push recovery by combining Single Rigid Body model predictive control (SRB-MPC) with Hybrid Linear Inverted Pendulum (HLIP) dynamics to enable robust locomotion, push detection, and recovery by utilizing the robot's arms to brace against such walls and dynamically adjusting the desired contact forces and stepping patterns. Extensive simulation results on a humanoid robot demonstrate improved perturbation rejection and tracking performance compared to HLIP alone, with the robot able to recover from pushes up to 100N for 0.2s while walking at commanded speeds up to 0.5m/s. Robustness is further validated in scenarios with angled walls and multi-directional pushes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
