让机器人靠墙借力，实现稳定抗推和行走
Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models
- 用墙作支点，结合人体动力学模型动态调整步伐和受力
- 能扛住100牛顿冲击持续0.2秒，速度达0.5米/秒仍可恢复
- 适合需在复杂环境应对推挤的人形机器人应用
行走中的推力恢复有助于人形机器人在人类中心环境中部署。本文提出一个统一框架，实现人形机器人的步态控制与推力恢复，利用手臂在动态行走中进行推力抵消。核心创新是借助墙壁等环境，结合单刚体模型预测控制（SRB-MPC）与混合线性倒立摆（HLIP）动力学，实现鲁棒步态、推力检测与恢复。通过机器人手臂抵住墙壁，动态调节期望接触力与步态模式。大量仿真结果表明，相比仅使用HLIP的方法，该框架在扰动抑制和轨迹跟踪性能上均有提升：机器人可在0.5米/秒行进速度下，承受最大100牛顿的冲击，持续时间0.2秒，并成功恢复平衡；鲁棒性在斜墙及多方向推力场景中亦得到验证。
原文摘要 · Abstract (English)
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.
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