SLIP-based Iterative Learning for Efficient and Compliant Locomotion of Articulated Soft Quadrupeds

Abstract

Effectively controlling and exploiting the natural dynamics of Articulated Soft Robots for energy-efficient motions remains challenging. In literature, the problem is often split in two; in energy-efficient motion planning and structure-preserving control, where the focus is on one, and the other is largely disregarded. This work aims to unify these two using a motion planning and control strategy based on trajectory optimization and functional Iterative Learning Control. Using a reduced-order model, the planner generates an energy-efficient reference trajectory by minimizing the Cost of Transport. The controller then iteratively learns the feedforward control signal such that the full-order system tracks the reference, without altering its stiffness characteristics. We show that our strategy results in energy-efficient tracking of the reference. We also show how functional Iterative Learning Control can be used in a continuous approach to learn a stable forward pronking gait. We give experimental validation of this approach through experiments on the compliant quadruped E-Go. We show that the pronking gait can be learned on hardware in minutes and that it is robust to various types of terrain.