MagServo: 学習潜在表現による不確実性に頑健な階層型磁気サーボ制御
MagServo: Uncertainty-Resilient Hierarchical Magnetic Servoing via Learned Latent Representations
生の磁気計測から学習した潜在特徴を直接用い、解析的モデルなしで6自由度磁気サーボを実現する階層型学習フレームワークを提案し、サブミリ・サブ度の精度を達成した。
著者: Yuhan Tan, Yameng Zhang, Pei Liu, Yao Zhong, Zhongliang Jiang
分類: cs.RO
原文アブストラクト
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.