用简化模型实现人形机器人高效稳定步态控制
Hierarchical Reduced-Order Model Predictive Control for Robust Locomotion on Humanoid Robots
- 分层架构：高层用非线性MPC优化步长与踝力，中层线性MPC融合躯干手臂动态
- 实测在40Hz高层与500Hz中层运行，推倒恢复成功率提升36%
- 适合需高鲁棒性的复杂地形行走任务，尤其对上身姿态控制有要求
随着人形机器人进入真实环境，确保在多样化地形下的鲁棒行走至关重要。本文提出一种基于降阶模型的分层控制框架，实现高效的人形机器人步态规划并整合手臂与躯干动力学以增强稳定性。高层采用ALIP模型的步间动力学，通过非线性模型预测控制（MPC）同步优化步长、步周期和踝部扭矩；底层线性MPC以ALIP轨迹为参考，将标准SRB-MPC扩展至包含简化的臂和躯干动力学。通过在Unitree G1人形机器人上的仿真与实机实验验证，该框架高层控制器运行于40 Hz，中层控制器运行于500 Hz，使用机载微型计算机。自适应步态时序使推倒恢复成功率提高36%，上身控制显著改善了偏航扰动抑制能力。此外，在草地、石板路及不平整健身房垫等多种室内外地形上均实现了稳健行走。
原文摘要 · Abstract (English)
As humanoid robots enter real-world environments, ensuring robust locomotion across diverse environments is crucial. This paper presents a computationally efficient hierarchical control framework for humanoid robot locomotion based on reduced-order models -- enabling versatile step planning and incorporating arm and torso dynamics to better stabilize the walking. At the high level, we use the step-to-step dynamics of the ALIP model to simultaneously optimize over step periods, step lengths, and ankle torques via nonlinear MPC. The ALIP trajectories are used as references to a linear MPC framework that extends the standard SRB-MPC to also include simplified arm and torso dynamics. We validate the performance of our approach through simulation and hardware experiments on the Unitree G1 humanoid robot. In the proposed framework the high-level step planner runs at 40 Hz and the mid-level MPC at 500 Hz using the onboard mini-PC. Adaptive step timing increased the push recovery success rate by 36%, and the upper body control improved the yaw disturbance rejection. We also demonstrate robust locomotion across diverse indoor and outdoor terrains, including grass, stone pavement, and uneven gym mats.
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