SofaGym: An Open Platform for Reinforcement Learning Based on Soft Robot Simulations

Abstract

OpenAI Gym is one of the standard interfaces used to train Reinforcement Learning (RL) Algorithms. The Simulation Open Framework Architecture (SOFA) is a physics-based engine that is used for soft robotics simulation and control based on real-time models of deformation. The aim of this article is to present SofaGym , an open-source software to create OpenAI Gym interfaces, called environments, out of soft robot digital twins. The link between soft robotics and RL offers new challenges for both fields: representation of the soft robot in an RL context, complex interactions with the environment, use of specific mechanical tools to control soft robots, transfer of policies learned in simulation to the real world, etc. The article presents the large possible uses of SofaGym to tackle these challenges by using RL and planning algorithms. This publication contains neither new algorithms nor new models but proposes a new platform, open to the community, that offers non existing possibilities of coupling RL to physics-based simulation of soft robots. We present 11 environments, representing a wide variety of soft robots and applications; we highlight the challenges showcased by each environment. We propose methods of solving the task using traditional control, RL, and planning and point out research perspectives using the platform.