Magnetically actuated microrobots have been used as wireless, non-contact manipulation tools at microscales, making them promising for minimally invasive applications. However, their control remains challenging due to indirect actuation, limited sensing, and nonlinear magnetic interactions. In this work, we propose Mag-VLA, a vision-language-action (VLA) model for dexterous magnetic microrobot manipulation using two robotic arms with mounted magnets for dynamic magnetic-field construction. Bimanual coordination enables capabilities such as microrobot reorientation that are difficult or infeasible with a single arm, but it also introduces coupled control challenges, as the policy must generate coordinated trajectories for both actuators within a shared workspace. Our framework adapts a Qwen2.5-VL-7B backbone using Low-Rank Adaptation (LoRA) to process visual observations and language instructions for action prediction. To capture task progression, we introduce a motion-aware phase classifier and a phase-conditioned Action Chunking Transformer (ACT) decoder for temporally coherent multi-step control. We further construct a teleoperated magnetic microrobot manipulation dataset covering three task configurations. Ablation studies show that the ACT-based decoder substantially outperforms alternative generative action heads. In real-robot experiments, Mag-VLA achieves a 90% approach success rate across all tasks and transport success rates of 80%, 70%, and 50% as task difficulty increases. These results demonstrate that hierarchical VLA modeling provides a promising framework for magnetic microrobot manipulation.

翻译：磁性驱动微机器人已被用作微观尺度下的无线、非接触式操作工具，在微创应用中具有广阔前景。然而，由于间接驱动、有限传感以及非线性磁相互作用，其控制仍具有挑战性。本文提出Mag-VLA，一种面向灵巧磁性微机器人操作的视觉-语言-动作（VLA）模型，该模型利用两个装有磁体的机械臂构建动态磁场。双手协调可实现单臂难以或无法完成的微机器人重新定向等能力，但同时也引入了耦合控制难题——策略需在共享工作空间内为两个执行器生成协同轨迹。我们的框架采用基于低秩适配的Qwen2.5-VL-7B骨干网络，处理视觉观测与语言指令以预测动作。为捕捉任务进展，我们引入运动感知相位分类器及相位条件化的动作分块Transformer解码器，实现时间一致的多步控制。此外，我们构建了涵盖三种任务配置的遥操作磁性微机器人操作数据集。消融研究表明，基于动作分块Transformer的解码器显著优于其他生成式动作头。在真实机器人实验中，Mag-VLA在所有任务中达到90%的趋近成功率，并随任务难度增加分别实现80%、70%和50%的搬运成功率。这些结果表明层次化VLA建模为磁性微机器人操作提供了有前景的框架。