Magnetic soft robots have attracted growing interest due to their unique advantages in terms of untethered actuation and excellent controllability. However, finding the required magnetization patterns or magnetic fields to achieve the desired functions of these robots is quite challenging in many cases. No unified framework for design has been proposed yet, and existing methods mainly rely on manual heuristics, which are hard to satisfy the high complexity level of the desired robotic motion. Here, we develop an intelligent method to solve the related inverse-design problems, implemented by introducing a novel simulation platform for magnetic soft robots based on Cosserat rod models and a deep reinforcement learning framework based on TD3. We demonstrate that magnetic soft robots with different magnetization patterns can learn to move without human guidance in simulations, and effective magnetic fields can be autonomously generated that can then be applied directly to real magnetic soft robots in an open-loop way.
翻译:磁软机器人因其在未交接的振动和极佳的可控性方面的独特优势而引起了越来越多的兴趣。然而,在许多情况下,找到所需的磁化模式或磁场以实现这些机器人的预期功能是相当具有挑战性的。还没有提出统一的设计框架,而现有的方法主要依靠人工超力,难以满足所希望的机器人运动的高度复杂程度。在这里,我们开发了一种解决相关反设计问题的智能方法,通过采用基于Cosserat棒模型的磁软机器人新颖模拟平台和基于TD3的深层强化学习框架来实施。 我们证明,具有不同磁化模式的磁软机器人可以在没有人类模拟指导的情况下学会移动,有效的磁场可以自主生成,然后可以直接用于开路的真正的磁软机器人。