Gathering Physical Particles with a Global Magnetic Field Using Reinforcement Learning
For biomedical applications in targeted therapy delivery and interventions, a large swarm of micro-scale particles ("agents") has to be moved through a maze-like environment ("vascular system") to a target region ("tumor"). Due to limited on-board capabilities, these agents cannot move autonomously; instead, they are controlled by an external global force that acts uniformly on all particles. In this work, we demonstrate how to use a time-varying magnetic field to gather particles to a desired location. We use reinforcement learning to train networks to efficiently gather particles. Methods to overcome the simulation-to-reality gap are explained, and the trained networks are deployed on a set of mazes and goal locations. The hardware experiments demonstrate fast convergence, and robustness to both sensor and actuation noise. To encourage extensions and to serve as a benchmark for the reinforcement learning community, the code is available at Github at https://github.com/NeoExtended/gym-ga....
"Gathering Physical Particles with a Global Magnetic Field Using Reinforcement Learning"
Authors: Matthias Konitzny, Yitong Lu, Julien Leclerc, Sándor P. Fekete, and Aaron T. Becker
https://iros2022.org/
IROS 2022 paper, presented in Kyoto Oct 25-27, 2022.
Paper: https://ieeexplore.ieee.org/abstract/...