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This paper introduces a hierarchical model predictive control (MPC) framework for humanoid robot locomotion, leveraging a reduced-order model to achieve computational efficiency and robustness. The framework combines a high-level nonlinear MPC based on the ALIP model for step planning with a mid-level linear MPC that incorporates simplified arm and torso dynamics to enhance stability. Experimental validation on the Unitree G1 robot demonstrates improved push recovery (36% increase) and yaw disturbance rejection, enabling robust locomotion across diverse terrains.
Humanoid robots can now walk more robustly on uneven terrain thanks to a hierarchical MPC approach that cleverly incorporates arm and torso dynamics for improved stability.
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.
