面向人形机器人运动的降阶模型引导接触隐式模型预测控制
Reduced-Order Model Guided Contact-Implicit Model Predictive Control for Humanoid Locomotion
AI总结：
针对人形机器人高维混合动力学控制难题，提出结合HLIP降阶模型与CI-MPC的控制框架，在24自由度机器人Achilles仿真中实现多种复杂运动能力，且支持50Hz实时运行。
摘要：
人形机器人因能在为人类建造的环境中作业，在实际应用中潜力巨大，但其底层高维非线性混合动力学的控制难题阻碍了部署。混合线性倒立摆（HLIP）这类降阶模型简单且计算高效，但会丧失全身表达能力。同时，接触隐式模型预测控制（CI-MPC）的最新进展使机器人能规划多种混合接触模式，但仍易受局部极小值影响，且需要大量调参。我们提出一种结合HLIP与CI-MPC优势的控制框架：降阶模型生成标称步态，CI-MPC处理全身动力学并按需调整接触时序。我们在一款新型24自由度人形机器人Achilles上通过仿真验证了该方法的有效性：所提框架实现了崎岖地形行走、扰动恢复、模型与状态不确定性下的鲁棒性，还能让机器人与环境中的障碍物交互，且全程以50Hz实时在线运行。
英文摘要：
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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