用深度强化学习+混合现实，让磁性微机器人更智能地导航。
Deep Reinforcement Learning-Based Semi-Autonomous Control for Magnetic Micro-robot Navigation with Immersive Manipulation
- 基于深度强化学习实现半自主控制，结合混合现实提升操作沉浸感。
- 在模拟微血管环境中导航效率提升，控制误差显著降低。
- 适合生物医学微操作、远程手术等需要高精度操控的场景。
磁性微机器人因其精确可控性和微型化，在体内药物递送、无创诊断和细胞治疗等生物医学应用中展现出巨大潜力。然而，现有微操纵技术通常仅依赖二维显微视图作为感知反馈，传统控制界面也缺乏直观性，导致操作者需在有限信息下进行复杂决策，认知负荷高。为此，我们提出一种基于深度强化学习的半自主控制框架（DRL-SC），用于在模拟微血管系统中导航磁性微机器人。该框架融合混合现实（MR）技术，实现对微机器人的沉浸式操控，从而增强态势感知与控制精度。仿真与实验结果表明，该方法显著提升了导航效率，降低了控制误差，并增强了系统在模拟微血管环境中的整体鲁棒性。
原文摘要 · Abstract (English)
Magnetic micro-robots have demonstrated immense potential in biomedical applications, such as in vivo drug delivery, non-invasive diagnostics, and cell-based therapies, owing to their precise maneuverability and small size. However, current micromanipulation techniques often rely solely on a two-dimensional (2D) microscopic view as sensory feedback, while traditional control interfaces do not provide an intuitive manner for operators to manipulate micro-robots. These limitations increase the cognitive load on operators, who must interpret limited feedback and translate it into effective control actions. To address these challenges, we propose a Deep Reinforcement Learning-Based Semi-Autonomous Control (DRL-SC) framework for magnetic micro-robot navigation in a simulated microvascular system. Our framework integrates Mixed Reality (MR) to facilitate immersive manipulation of micro-robots, thereby enhancing situational awareness and control precision. Simulation and experimental results demonstrate that our approach significantly improves navigation efficiency, reduces control errors, and enhances the overall robustness of the system in simulated microvascular environments.
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