DAISS：面向双臂机器人超声引导干预的相位感知模仿学习
DAISS: Phase-Aware Imitation Learning for Dual-Arm Robotic Ultrasound-Guided Interventions
AI总结：
DAISS通过相位感知模仿学习实现双臂机器人在超声引导干预中的精准操作，减少认知负担。
摘要：
模仿学习在自动化复杂机器人操作方面显示出强大潜力。在医疗机器人领域，超声引导的针刺需要精确的双臂协调，因为临床医生必须同时操控超声探头以保持最佳的声学视图，同时操控介入针。自动化这种不对称的工作流程，并可靠地将专家策略转移到机器人上，仍然极具挑战性。在本文中，我们提出了双臂介入手术系统（DAISS），这是一个远程操作平台，能够收集高保真的双臂示范，并学习一种相位感知的模仿策略用于超声引导的干预。为了避免限制操作员的自然行为，DAISS使用基于NDI的灵活领导者接口来远程操作两个协调的跟随臂。为了支持在实时超声反馈下的稳健执行，我们开发了一种轻量、数据高效的模仿策略。具体来说，该策略结合了相位感知的架构和动态掩码损失，以适应不对称的双臂控制。在计划轨迹的条件下，网络融合实时超声与外部视觉观测，以生成平滑、协调的双臂运动。实验结果表明，DAISS可以从有限的示范中学习个性化的专家策略。总体而言，这些发现突显了相位感知模仿学习驱动的双臂机器人在提高图像引导干预的精度和减少认知负荷方面的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
