Soft Robots Learn to Crawl: Jointly Optimizing Design and Control with Sim-to-Real Transfer

Abstract

This work provides a complete framework for the simulation, co-optimization, and sim-to-real transfer of the design and control of soft legged robots. The compliance of soft robots provides a form of "mechanical intelligence"-the ability to passively exhibit behaviors that would otherwise be difficult to program. Exploiting this capacity requires careful consideration of the coupling between mechanical design and control. Cooptimization provides a promising means to generate sophisticated soft robots by reasoning over this coupling. However, the complex nature of soft robot dynamics makes it difficult to achieve a simulation environment that is both sufficiently accurate to allow for sim-to-real transfer and fast enough for contemporary co-optimization algorithms. In this work, we describe a modularized model order reduction algorithm that significantly improves the efficiency of finite element simulation, while preserving the accuracy required to successfully learn effective soft robot design-control pairs that transfer to reality. We propose a reinforcement learning-based framework for co-optimization and demonstrate successful optimization, construction, and zero-shot sim-to-real transfer of several soft crawling robots. Our learned robot outperforms an expert-designed crawling robot, showing that our approach can generate novel, high-performing designs even in well-understood domains.