Soft robotics is a rapidly growing area of robotics research that would benefit greatly from design automation, given the challenges of manually engineering complex, compliant, and generally non-intuitive robot body plans and behaviors. It has been suggested that a major hurdle currently limiting soft robot brain-body co-optimization is the fragile specialization between a robot's controller and the particular body plan it controls, resulting in premature convergence. Here we posit that modular controllers are more robust to changes to a robot's body plan. We demonstrate a decreased reduction in locomotion performance after morphological mutations to soft robots with modular controllers, relative to those with similar global controllers - leading to fitter offspring. Moreover, we show that the increased transferability of modular controllers to similar body plans enables more effective brain-body co-optimization of soft robots, resulting in an increased rate of positive morphological mutations and higher overall performance of evolved robots. We hope that this work helps provide specific methods to improve soft robot design automation in this particular setting, while also providing evidence to support our understanding of the challenges of brain-body co-optimization more generally.

翻译：软体机器人是机器人研究中快速发展的领域，鉴于手动设计复杂、柔顺且通常非直观的机器人身体结构及行为的挑战，其将极大受益于设计自动化。已有研究表明，当前限制软体机器人“大脑-身体”协同优化的主要障碍在于机器人控制器与其特定身体结构之间的脆弱专一性，这会导致过早收敛。本文提出，模块化控制器对机器人身体结构的变化具有更强的鲁棒性。我们证明，与采用类似全局控制器的软体机器人相比，具有模块化控制器的软体机器人在经历形态突变后，其运动性能下降幅度更小——从而产生更适应环境的后代。此外，我们发现，模块化控制器向相似身体结构的迁移性增强，能够实现更有效的软体机器人“大脑-身体”协同优化，从而提高正向形态突变率并提升进化机器人的整体性能。我们希望这项工作不仅能为改善特定场景下的软体机器人设计自动化提供具体方法，同时也能为更广泛意义上理解“大脑-身体”协同优化面临的挑战提供证据支持。