Magnetic levitation is poised to revolutionize industrial automation by integrating flexible in-machine product transport and seamless manipulation. It is expected to become the standard drive technology for automated manufacturing. However, controlling such systems is inherently challenging due to their complex, unstable dynamics. Traditional control approaches, which rely on hand-crafted control engineering, typically yield robust but conservative solutions, with their performance closely tied to the expertise of the engineering team. In contrast, learning-based neural control presents a promising alternative. This paper presents the first neural controller for 6D magnetic levitation. Trained end-to-end on interaction data from a proprietary controller, it directly maps raw sensor data and 6D reference poses to coil current commands. The neural controller can effectively generalize to previously unseen situations while maintaining accurate and robust control. These results underscore the practical feasibility of learning-based neural control in complex physical systems and suggest a future where such a paradigm could enhance or even substitute traditional engineering approaches in demanding real-world applications. The trained neural controller, source code, and demonstration videos are publicly available at https://sites.google.com/view/neural-maglev.

翻译：磁悬浮技术有望通过集成柔性化机器内产品运输与无缝操控，彻底革新工业自动化领域，预计将成为自动化制造的标准驱动技术。然而，由于该系统具有复杂的非稳态动力学特性，对其实现控制本身就极具挑战性。传统控制方法依赖人工设计的控制工程，通常能提供鲁棒但保守的解决方案，其性能与工程团队的 expertise 高度相关。相比之下，基于学习的神经控制展现出极具前景的替代方案。本文首次提出了面向六维磁悬浮的神经控制器。该控制器基于专有控制器的交互数据进行端到端训练，可直接将原始传感器数据与六维参考位姿映射为线圈电流指令。该神经控制器能够有效泛化至未见过的场景，同时保持精准且鲁棒的控制性能。这些结果强调了基于学习的神经控制在复杂物理系统中的实际可行性，并预示着未来此类范式可能增强甚至取代苛刻实际应用中的传统工程方法。训练好的神经控制器、源代码及演示视频已公开至 https://sites.google.com/view/neural-maglev。