Fast Deformation and Imitation Learning-Based Control for Microswarms in Unstructured Environments

Magnetic microswarms hold significant potential in minimally invasive medical procedures, owing to their reconfigurability, wireless actuation, and high biocompatibility. However, conventional microswarm deformation strategies are hindered by slow response rate, making them unsuitable for rapid obstacle avoidance. Moreover, Traditional model-based control methods rely heavily on precise modeling of microscale systems, limiting their applicability in uncertain scenarios. Meanwhile, data-driven approaches face challenges such as limited hardware adaptability. To address these issues, we propose a Fast Deformation Strategy of Swarm (FDSwarm), which enables rapid shape transformation through coordinated modulation of multiple magnetic field parameters. Furthermore, we introduce Fast Deformation and Motion control based on Imitation Learning (FDM-IL), which is a framework that addresses challenges in microswarm dynamics modeling, deformation-motion coupling, and environmental adaptability. By integrating expert demonstrations with real-time environmental perception, our approach achieves the efficient co-optimization of microswarm shape adaptation and locomotion. Experimental results show that, compared with conventional deformation strategies, FDSwarm achieves a 58.0% improvement in deformation speed. In unstructured environments, FDM-IL successfully guides the microswarm to perform autonomous deformation and navigation. This work provides an robust and efficient control framework for coordinated deformation and navigation of microrobot swarms. Videos link: Supplementary videos