Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning

Swimming microrobots are increasingly developed with complex materials and dynamic shapes and are expected to operate in complex environments in which the system dynamics are difficult to model and positional control of the microrobot is not straightforward to achieve. Deep reinforcement learning is a promising method of autonomously developing robust controllers for creating smart microrobots, which can adapt their behavior to operate in uncharacterized environments without the need to model the system dynamics. This article reports the development of a smart helical magnetic hydrogel microrobot that uses the soft actor critic reinforcement learning algorithm to autonomously derive a control policy which allows the microrobot to swim through an uncharacterized biomimetic fluidic environment under control of a time‐varying magnetic field generated from a three‐axis array of electromagnets. The reinforcement learning agent learns successful control policies from both state vector input and raw images, and the control policies learned by the agent recapitulate the behavior of rationally designed controllers based on physical models of helical swimming microrobots. Deep reinforcement learning applied to microrobot control is likely to significantly expand the capabilities of the next generation of microrobots. Deep reinforcement learning discovers and implements optimal swimming behavior for magnetic microrobots, abrogating the need for both kinematic models of swimming and classical feedback controls approaches. This approach reveals a new strategy for microrobot manipulation in fluid environments similar to those in the human body, and thus has potential for medical impact in the future.