DittoGym: Learning to Control Soft Shape-Shifting Robots

TL;DR

This paper introduces DittoGym, a reinforcement learning benchmark for reconfigurable soft robots that can change their morphology during operation, enabling complex control and task achievement.

Contribution

It formalizes control of reconfigurable soft robots as a high-dimensional RL problem and proposes a coarse-to-fine curriculum for effective policy learning.

Findings

RL policies enable robots to change morphology multiple times

The coarse-to-fine curriculum improves learning efficiency

Reconfigurable robots achieve complex tasks with learned control

Abstract

Robot co-design, where the morphology of a robot is optimized jointly with a learned policy to solve a specific task, is an emerging area of research. It holds particular promise for soft robots, which are amenable to novel manufacturing techniques that can realize learned morphologies and actuators. Inspired by nature and recent novel robot designs, we propose to go a step further and explore the novel reconfigurable robots, defined as robots that can change their morphology within their lifetime. We formalize control of reconfigurable soft robots as a high-dimensional reinforcement learning (RL) problem. We unify morphology change, locomotion, and environment interaction in the same action space, and introduce an appropriate, coarse-to-fine curriculum that enables us to discover policies that accomplish fine-grained control of the resulting robots. We also introduce DittoGym, a comprehensive RL benchmark for reconfigurable soft robots that require fine-grained morphology changes to accomplish the tasks. Finally, we evaluate our proposed coarse-to-fine algorithm on DittoGym and demonstrate robots that learn to change their morphology several times within a sequence, uniquely enabled by our RL algorithm. More results are available at https://suninghuang19.github.io/dittogym_page/.