Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 06, 2022
- Source ID
- 10.1002/aisy.202200023
Entities
People
- Michael R. Behrens
- Warren C Ruder
Organizations
- Air Force Office of Scientific Research
- Carnegie Mellon University
- National Institutes of Health
- National Science Foundation
- Office of Naval Research
- United States Army Research Laboratory
- University of Pittsburgh