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://dittogym.github.io.

翻译：机器人协同设计——将机器人形态与学习策略联合优化以解决特定任务，是一个新兴的研究领域。这对软体机器人尤其具有前景，因为新型制造技术能够实现学习得到的形态与执行器。受自然界及最新机器人设计的启发，我们提出进一步探索新型可重构机器人——即能在生命周期内改变自身形态的机器人。我们将可重构软体机器人的控制形式化为高维强化学习问题，将形态变化、运动与环境交互统一于同一动作空间，并引入由粗到精的分层课程，从而发现能实现精细控制的策略。同时，我们推出DittoGym——一个针对需要精细形态变化以完成任务的可重构软体机器人的综合性强化学习基准。最后，我们在DittoGym上评估所提出的由粗到精算法，展示了机器人能在序列中多次改变形态的独特能力，这完全归功于我们的强化学习算法。更多结果详见https://dittogym.github.io。