Multi-Cable Rolling Locomotion with Spherical Tensegrities Using Model Predictive Control and Deep Learning

Abstract

This work presents a model-based approach for creating robust control policies for rolling locomotion with a spherical tensegrity topology. Utilizing the structured dynamics of Class-1 tensegrity systems, we turn to model predictive control (MPC) to generate optimal multi-cable actuation trajectories for dynamic rolling. Although the resulting multi-cable state-action trajectories successfully outperform the benchmark single-cable policy performance in speed, computational constraints prevent MPC from being applied in real-time. To address this, we demonstrate that a contextual policy trained using supervised deep learning on the generated optimal MPC trajectories can be used as an end-to-end feedback policy for real-time directed rolling locomotion.