Machine Learning Achieves Accurate and Smooth Navigation Control for Microrobots

Automated navigation control of microrobots in complex environments is essential for applications such as targeted drug delivery and micromanipulation. Recently, machine learning (ML) has shown great potential for automated microrobot control but still lacks accuracy and smoothness. In this work, we propose a novel Learning-from-Demonstration (LfD) -based control and navigation framework to achieve precise and smooth motion control of microrobots. This work represents an early attempt to directly utilize expert-provided data for designing a learning-based microrobot controller. The framework begins by collecting a small dataset of expert demonstrations (several thousand episodes), from which the controller learns compensatory behaviors and task-specific adaptability, eliminating the need for extensive exploration or parameter retuning. Based on this data, a time-series neural network is then developed to process the microrobot’s historical states and control actions, allowing the system to capture sequential dependencies and transitions for smooth and accurate path tracking. For demonstration, we take the magnetic microswarm as an illustrative example. Systematic simulations and comparative experiments validate the proposed framework, demonstrating its superior performance in tracking accuracy and smoothness, validating the efficacy of ML for low-level microrobot control.