用简化模型引导全身体控，让机器人稳定走不平路
Reduced-Order Model Guided Contact-Implicit Model Predictive Control for Humanoid Locomotion
- 用简化模型生成步态，全身体控动态调整接触
- 实现在24自由度机器人上50Hz实时运行，能抗扰动
- 适合需要高鲁棒性的复杂地形人形机器人控制
人形机器人因能在人类环境中作业而潜力巨大，但其高维非线性混合动力学控制仍具挑战。传统简化模型如混合线性倒立摆（HLIP）虽高效却缺乏全身表现力；而近期的接触隐式模型预测控制（CI-MPC）虽可规划多接触模式，却易陷入局部最优且需大量调参。本文提出一种融合两者优势的控制框架：由简化模型生成基线步态，再由CI-MPC管理全身动力学并按需调整接触时序。我们在新构建的24自由度人形机器人Achilles上进行仿真验证，结果表明该方法可实现粗糙地形行走、扰动恢复、对模型与状态不确定性的鲁棒性，并支持与环境障碍物交互，所有操作均以50 Hz在线实时运行。
原文摘要 · Abstract (English)
Humanoid robots have great potential for real-world applications due to their ability to operate in environments built for humans, but their deployment is hindered by the challenge of controlling their underlying high-dimensional nonlinear hybrid dynamics. While reduced-order models like the Hybrid Linear Inverted Pendulum (HLIP) are simple and computationally efficient, they lose whole-body expressiveness. Meanwhile, recent advances in Contact-Implicit Model Predictive Control (CI-MPC) enable robots to plan through multiple hybrid contact modes, but remain vulnerable to local minima and require significant tuning. We propose a control framework that combines the strengths of HLIP and CI-MPC. The reduced-order model generates a nominal gait, while CI-MPC manages the whole-body dynamics and modifies the contact schedule as needed. We demonstrate the effectiveness of this approach in simulation with a novel 24 degree-of-freedom humanoid robot: Achilles. Our proposed framework achieves rough terrain walking, disturbance recovery, robustness under model and state uncertainty, and allows the robot to interact with obstacles in the environment, all while running online in real-time at 50 Hz.
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