用分阶段模仿学习让双臂机器人精准完成超声引导穿刺。
DAISS: Phase-Aware Imitation Learning for Dual-Arm Robotic Ultrasound-Guided Interventions
- 设计分阶段模仿策略，融合实时超声与视觉反馈。
- 仅需少量示范即可学习专家操作，执行精度高。
- 适合医疗机器人领域，减轻医生操作负担。
模仿学习在自动化复杂机器人操作方面展现出巨大潜力。在医疗机器人中，超声引导穿刺需要双手协同：一手持超声探头保持最佳成像视角，另一手操控穿刺针。这种非对称工作流程的自动化及专家策略的有效迁移仍极具挑战。本文提出双臂介入手术系统（DAISS），一个可采集高保真双臂示范的遥操作平台，并学习一种分阶段感知的模仿策略，用于超声引导干预。为避免限制操作者自然动作，DAISS采用基于NDI的灵活主端接口，实现两臂协调控制。为支持实时超声反馈下的稳定执行，开发了轻量、数据高效的模仿策略，其包含分阶段架构和针对非对称双臂控制设计的动态掩码损失。该网络以规划轨迹为条件，融合实时超声与外部视觉观测，生成平滑协调的双臂运动。实验表明，DAISS可从有限示范中学习个性化专家策略。结果凸显了分阶段模仿学习驱动的双臂机器人在提升图像引导干预精度、降低认知负荷方面的前景。
原文摘要 · Abstract (English)
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.
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