Model Hierarchy Predictive Control of Robotic Systems
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This paper presents a new predictive control architecture for high-dimensional robotic systems. As opposed to a conventional Model-Predictive Control (MPC) approach that formulates a hierarchy of optimization problems, the proposed work formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC). MHPC is formulated as a multi-phase receding-horizon Trajectory Optimization (TO) problem, and is solved by an efficient solver called Hybrid Systems Differential Dynamic Programming (HSDDP). MHPC is benchmarked in simulation on a quadruped, a biped, and a quadrotor, demonstrating control performance on par or exceeding whole-body MPC while maintaining a lower computational cost in each case. A preliminary bounding experiment is conducted on the MIT Mini Cheetah with the control policy generated offline, demonstrating the physical validity of the generated trajectories and motivating online MHPC in future work.
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