让双足机器人在行走中抗推力更稳，通过简化模型预测控制优化步伐和脚踝动作。
Robust Push Recovery on Bipedal Robots: Leveraging Multi-Domain Hybrid Systems with Reduced-Order Model Predictive Control
- 用简化版倒立摆模型结合分段运动规律，实时规划脚步位置与时机。
- 在不同步速、步长和走路方式下均显著提升抗干扰稳定性。
- 适合需要高动态适应性的双足机器人研究者或开发者参考。
本文提出一种新型控制框架，实现双足机器人行进中的鲁棒抗推力恢复。核心是将步态的分段动力学模型与降阶模型预测控制器相结合，联合决定脚部落点、步频及踝关节控制。所采用的降阶模型为扩展的线性倒立摆模型，包含零力矩点坐标，嵌入模型预测控制框架以增强外部扰动下的稳定性能。通过显式利用步态的混合动力学特性，该方法在不同行走高度、速度、步长时间下均表现优异，适用于平足及复杂足跟到足尖过渡的多域步态。在高保真仿真环境下对Cassie（一款3D欠驱动机器人）进行验证，证明了方法的实时可行性与显著增强的稳定性，展现出在动态环境中的鲁棒性。
原文摘要 · Abstract (English)
In this paper, we present a novel control framework to achieve robust push recovery on bipedal robots while locomoting. The key contribution is the unification of hybrid system models of locomotion with a reduced-order model predictive controller determining: foot placement, step timing, and ankle control. The proposed reduced-order model is an augmented Linear Inverted Pendulum model with zero moment point coordinates; this is integrated within a model predictive control framework for robust stabilization under external disturbances. By explicitly leveraging the hybrid dynamics of locomotion, our approach significantly improves stability and robustness across varying walking heights, speeds, step durations, and is effective for both flat-footed and more complex multi-domain heel-to-toe walking patterns. The framework is validated with high-fidelity simulation on Cassie, a 3D underactuated robot, showcasing real-time feasibility and substantially improved stability. The results demonstrate the robustness of the proposed method in dynamic environments.
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