DAISS: 位相認識模倣学習による双腕ロボット超音波ガイド下介入
DAISS: Phase-Aware Imitation Learning for Dual-Arm Robotic Ultrasound-Guided Interventions
超音波ガイド下の針挿入手術を自動化するため、位相認識アーキテクチャと動的マスク損失を備えた模倣学習ポリシーを開発し、限られたデモから専門家の戦略を学習する双腕システムを提案した。
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著者: Feng Li, Pei Liu, Shiting Wang, Ning Wang, Zhongliang Jiang, Nassir Navab, Yuan Bi
分類: cs.RO
原文アブストラクト
Imitation learning has shown strong potential for automating complex robotic manipulation. In medical robotics, ultrasound-guided needle insertion demands precise bimanual coordination, as clinicians must simultaneously manipulate an ultrasound probe to maintain an optimal acoustic view while steering an interventional needle. Automating this asymmetric workflow -- and reliably transferring expert strategies to robots -- remains highly challenging. In this paper, we present the Dual-Arm Interventional Surgical System (DAISS), a teleoperated platform that collects high-fidelity dual-arm demonstrations and learns a phase-aware imitation policy for ultrasound-guided interventions. To avoid constraining the operator's natural behavior, DAISS uses a flexible NDI-based leader interface for teleoperating two coordinated follower arms. To support robust execution under real-time ultrasound feedback, we develop a lightweight, data-efficient imitation policy. Specifically, the policy incorporates a phase-aware architecture and a dynamic mask loss tailored to asymmetric bimanual control. Conditioned on a planned trajectory, the network fuses real-time ultrasound with external visual observations to generate smooth, coordinated dual-arm motions. Experimental results show that DAISS can learn personalized expert strategies from limited demonstrations. Overall, these findings highlight the promise of phase-aware imitation-learning-driven dual-arm robots for improving precision and reducing cognitive workload in image-guided interventions.
関連論文
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- RCM-ACT: 動的RCMキャリブレーションを用いた模倣学習による眼内異物の自律除去医療ロボティクス/模倣学習
