Model Hierarchy Predictive Control of Robotic Systems

TL;DR

This paper introduces Model Hierarchy Predictive Control (MHPC), a novel control architecture for high-dimensional robotic systems that simplifies hierarchical optimization into a single multi-phase trajectory optimization problem, achieving efficient and effective control.

Contribution

The paper proposes MHPC, a new hierarchical predictive control framework formulated as a single multi-phase trajectory optimization problem, improving computational efficiency and control performance for complex robots.

Findings

MHPC achieves control performance comparable or superior to whole-body MPC.

MHPC demonstrates lower computational cost in simulation benchmarks.

Preliminary experiments validate the physical plausibility of generated trajectories.

Abstract

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.