  This letter presents a new predictive control architecture for
high-dimensional robotic systems. As opposed to a conventional Model Predictive
Control (MPC) approach to locomotion that formulates a hierarchical sequence 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 can be implemented using any general
multi-phase TO solver. 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 gap jumping 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.

Robotics