Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss

TL;DR

This paper presents ODIL, a robust and efficient closed-loop control method for microdevice navigation in fluid environments, outperforming reinforcement learning in speed and high-dimensional space handling.

Contribution

Introduction of ODIL, a novel discrete loss optimization approach for microfluidic navigation, offering improved robustness and computational efficiency over reinforcement learning.

Findings

ODIL is up to three orders faster than reinforcement learning.

ODIL demonstrates superior robustness in microfluidic navigation tasks.

ODIL effectively handles high-dimensional action and state spaces.

Abstract

Optimal path planning and control of microscopic devices navigating in fluid environments is essential for applications ranging from targeted drug delivery to environmental monitoring. These tasks are challenging due to the complexity of microdevice-flow interactions. We introduce a closed-loop control method that optimizes a discrete loss (ODIL) in terms of dynamics and path objectives. In comparison with reinforcement learning, ODIL is more robust, up to three orders faster, and excels in high-dimensional action/state spaces, making it a powerful tool for navigating complex flow environments.