Soft robots have gained significant attention due to their flexibility and safety, particularly in human-centric applications. The co-design of structure and controller in soft robotics has presented a longstanding challenge owing to the complexity of the dynamics involved. Despite some pioneering work dealing with the co-design of soft robot structures and actuation, design freedom has been limited by stochastic design search approaches. This study proposes the simultaneous optimization of structure and controller for soft robots in locomotion tasks, integrating topology optimization-based structural design with neural network-based feedback controller design. Here, the feedback controller receives information about the surrounding terrain and outputs actuation signals that induce the expansion and contraction of the material. We formulate the simultaneous optimization problem under uncertainty in terrains and construct an optimization algorithm that utilizes automatic differentiation within topology optimization and neural networks. We present numerical experiments to demonstrate the validity and effectiveness of our proposed method.

翻译：软体机器人因其柔韧性和安全性而备受关注，尤其在以人为中心的应用中。由于涉及动力学复杂性，软体机器人结构与控制器的协同设计长期以来一直面临挑战。尽管已有一些先驱性工作处理软体机器人结构与驱动的协同设计，但随机设计搜索方法限制了设计自由度。本研究提出在运动任务中同时优化软体机器人的结构与控制器，将基于拓扑优化的结构设计与基于神经网络的反馈控制器设计相结合。在此框架中，反馈控制器接收周围地形信息并输出驱动信号，从而引发材料的膨胀与收缩。我们构建了地形不确定条件下的同步优化问题，并开发了一种在拓扑优化和神经网络中利用自动微分的优化算法。通过数值实验验证了所提方法的有效性与可行性。