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 this https URL

机器人共同设计是一个新兴的研究领域，尤其在软体机器人方面有巨大应用潜力，本文提出了一种基于增强学习的高维度算法来控制软体机器人的形态变化，同时引入了一个 RL 基准，展示了通过该算法使机器人在序列中多次改变形态的方法。