  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.
