用于人形机器人鲁棒行走的分层降阶模型预测控制
Hierarchical Reduced-Order Model Predictive Control for Robust Locomotion on Humanoid Robots
AI总结：
针对人形机器人真实环境鲁棒行走需求，提出基于降阶模型的分层控制框架，结合ALIP步间动力学优化与扩展SRB-MPC，经Unitree G1实验验证，推力恢复率提升36%，可适应多种复杂地形。
摘要：
随着人形机器人进入真实世界环境，确保其在多样环境中的鲁棒行走至关重要。本文提出了一种基于降阶模型的人形机器人行走分层控制框架，该框架计算效率高，可实现通用的步长规划，并结合手臂与躯干动力学以更好地稳定行走过程。在高层，我们利用ALIP（角动量线性倒立摆）模型的步间动力学，通过非线性MPC（模型预测控制）同时优化步长周期、步长和踝关节力矩。ALIP轨迹被用作线性MPC框架的参考，该框架对标准SRB-MPC（单刚体模型预测控制）进行了扩展，纳入了简化的手臂与躯干动力学。我们通过在Unitree G1人形机器人上开展仿真与硬件实验验证了所提方法的性能。在该框架中，高层步长规划器运行频率为40 Hz，中层MPC运行频率为500 Hz，均通过机载微型PC实现。自适应步长时序将推力恢复成功率提升了36%，上半身控制则改善了偏航扰动抑制能力。我们还展示了机器人在多种室内外地形（包括草地、石板路和不平的健身房地垫）上的鲁棒行走能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
