Imitation and reinforcement learning to control soft robots: a perspective

Abstract

It would not be an over-stretch to appraise soft robotics as one of the most sizzling topic in this era, owing to its unparalleled performance in terms of exhibited deformability, soft-interaction and luscious dexterity. In spite of that, we see minimal applications of this class around us. We believe the reason roots in the gap between design and fabrication of the soft robots and the capability to control them. The remedy to this imbalance, we believe, is a necessity for a climacteric rise in the capability to control them. The control, however poses challenges due to virtually infinite degrees of freedom of soft robot design, in terms of modelling the behavior of the robot, modelling its interaction with the environment or the environment’s effect on the robot. These challenges provoke the preponderant need for solutions that either can work with the inaccurate models of the robot obtained or avoid it altogether. To answer what can be used with these limitations we present here two potential classes of algorithms from machine learning capable of performing well under just the listed conditions: reinforcement learning (RL) can provide a solution in the form of a policy to control a platform with sophisticated dynamics even when the presented environment to train said policy has questionable accuracy, while Imitation Learning (IL) trains a policy while avoiding the need of a model or its constituent parts altogether and a policy is learned based on the teachings of an expert.