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Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning
Michael R Behrens1, Warren C Ruder1,2
1Department of Bioengineering, University of Pittsburgh; 300 Technology Drive, Pittsburgh, PA 15213, USA.
Summary
This study developed smart microrobots using deep reinforcement learning for autonomous control in complex environments. The soft actor critic algorithm enabled a helical magnetic hydrogel microrobot to navigate uncharacterized fluids effectively.
Area of Science:
- Robotics
- Biomimetic Engineering
- Artificial Intelligence
Background:
- Microrobots with complex designs require robust control in unpredictable environments.
- Modeling system dynamics and achieving precise positional control for microrobots is challenging.
Purpose of the Study:
- To develop a smart microrobot capable of autonomous navigation in uncharacterized environments.
- To utilize deep reinforcement learning for deriving adaptive control policies without system modeling.
Main Methods:
- Development of a helical magnetic hydrogel microrobot.
- Application of the soft actor critic reinforcement learning algorithm.
- Control via a time-varying magnetic field from a three-axis electromagnet array.
Main Results:
- The reinforcement learning agent successfully learned control policies from state vectors and raw images.
- The learned policies enabled the microrobot to swim through a biomimetic fluidic environment.
- The autonomous control strategies mimicked those of controllers based on physical models.
Conclusions:
- Deep reinforcement learning offers a powerful approach for creating adaptive microrobot controllers.
- This method significantly enhances microrobot capabilities for operation in complex, uncharacterized environments.
- Future microrobot generations will benefit from AI-driven control strategies.
