Locomotion tracking is a critical capability for humanoid robots to navigate environments and perform loco-manipulation tasks. Achieving this requires fulfilling various kinematic and dynamic sub-objectives, such as accurate tracking of the robot’s base, joints and feet, environment-collision avoidance, and dynamic balance and stability. The purpose of this paper is to propose a controller to generate motions for humanoid robots considering all sub-objectives of locomotion tracking.
This paper introduces a hierarchical model predictive control (MPC) framework for the locomotion tracking control problem of humanoid robots. All kinematics sub-objectives are firstly solved at the high-level MPC using full kinematics with second-order kinematics of base. Both kinematics and dynamics sub-objectives are optimized in the low-level kinodynamic MPC considering centroidal dynamics and surface contact dynamics.
This paper validates the effectiveness of this method through extensive simulation and hardware experiments. In comparison to traditional whole-body MPC, the proposed method improves the locomotion tracking accuracy while reducing the violations of the system’s physical limit constraints and environment-collision avoidance constraints.
Both reinforcement learning (RL) and whole-body MPC have become popular approaches for motion control of legged robot. However, achieving all the sub-objectives of locomotion within a single policy remains a challenge for RL methods. Due to computation limitations and strict real-time requirements, it is difficult for the whole-body MPC to generate optimal motions over a short-time horizon while considering multiple tracking goals and nonlinear dynamics of humanoid robots.
