Reinforcement Learning Controllers for Soft Robots using Learned Environments

TL;DR

This paper presents a reinforcement learning framework for soft robot control that uses learned environment models and safety-oriented exploration, enabling efficient policy learning without prior robot knowledge.

Contribution

It introduces a novel RL approach with learned synthetic environments and safety exploration protocols for soft robotic manipulators, improving control policy training.

Findings

Effective RL control policies learned in simulated environments

Safe actuation space exploration enhances real-world applicability

Framework enables benchmarking in soft robotics control

Abstract

Soft robotic manipulators offer operational advantage due to their compliant and deformable structures. However, their inherently nonlinear dynamics presents substantial challenges. Traditional analytical methods often depend on simplifying assumptions, while learning-based techniques can be computationally demanding and limit the control policies to existing data. This paper introduces a novel approach to soft robotic control, leveraging state-of-the-art policy gradient methods within parallelizable synthetic environments learned from data. We also propose a safety oriented actuation space exploration protocol via cascaded updates and weighted randomness. Specifically, our recurrent forward dynamics model is learned by generating a training dataset from a physically safe \textit{mean reverting} random walk in actuation space to explore the partially-observed state-space. We demonstrate a reinforcement learning approach towards closed-loop control through state-of-the-art actor-critic methods, which efficiently learn high-performance behaviour over long horizons. This approach removes the need for any knowledge regarding the robot's operation or capabilities and sets the stage for a comprehensive benchmarking tool in soft robotics control.