发表机构
Imperial College London; The University of Hong Kong; Technical University of Munich（伦敦帝国理工学院; 香港大学; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MagServo提出一种分层学习框架，利用潜在磁特征实现鲁棒的6自由度磁力伺服控制，通过掩码重建和动力学学习，结合模型预测控制与雅可比反演，达到亚毫米和亚度精度，并在复杂轨迹跟踪中优于基线。
AI 中文摘要
磁导航为机器人系统提供了无接触且不受视线限制的反馈，然而现有方法通常依赖显式姿态估计或直接使用原始磁测量，这使得精确控制易受建模误差、测量噪声和干扰的影响。本工作提出MagServo，一种分层学习框架，直接利用学习到的潜在磁特征实现鲁棒的6自由度磁力伺服控制。MagServo通过掩码重建学习不确定性鲁棒的磁表示，并在无需解析磁模型或显式雅可比监督的情况下，捕捉机器人运动与潜在磁状态转移之间的状态依赖交互动力学。基于学习到的动力学，分层控制器结合非线性模型预测控制进行粗略接近，以及局部雅可比反演进行精确微调。大量物理实验展示了亚毫米和亚度精度，在6自由度姿态到达任务中实现了平均终端误差0.386毫米和0.479度。MagServo在复杂轨迹跟踪中进一步优于基于定位的控制基线，并在未见磁源配置下无需重新训练即保持鲁棒性能。真实机器人实验的补充视频可在该https URL获取。
英文摘要
Magnetic navigation provides contact-free and line-of-sight-independent feedback for robotic systems, yet existing approaches typically rely on explicit pose estimation or direct use of raw magnetic measurements, making accurate control susceptible to modeling errors, measurement noise, and disturbances. This work presents MagServo, a hierarchical learning-based framework for robust 6-DoF magnetic servoing directly using the learned latent magnetic feature. MagServo learns uncertainty-resilient magnetic representations through masked reconstruction and captures state-dependent interaction dynamics between robot motion and latent magnetic transitions without analytical magnetic models or explicit Jacobian supervision. Based on the learned dynamics, a hierarchical controller combines nonlinear model predictive control for coarse approach with local Jacobian inversion for precise fine regulation. Extensive physical experiments demonstrate submillimeter and subdegree accuracy, achieving mean terminal errors of 0.386 mm and 0.479 degree for 6-DoF pose reaching. MagServo further outperforms a localization-based control baseline in complex trajectory tracking and maintains robust performance under unseen magnetic-source configurations without retraining. A supplementary video of the real-robot experiments is available at https://youtu.be/rZt1NUP1Mr0.
