DAISS: Phase-Aware Imitation Learning for Dual-Arm Robotic Ultrasound-Guided Interventions

2026-03-08

AI Summary

This work addresses the challenge of automating asymmetric bimanual coordination in ultrasound-guided robotic interventional surgery by proposing the Dual-Arm Interventional Surgery System (DAISS). The system leverages high-fidelity teleoperation to collect expert demonstrations and introduces a phase-aware imitation learning architecture coupled with a dynamic mask loss function to enable data-efficient, personalized policy transfer. By fusing real-time ultrasound and visual information, DAISS incorporates key technical components including an NDI optical tracking-based teleoperation interface, a lightweight imitation learning network, and trajectory-conditioned control. Experimental results demonstrate that DAISS can learn expert-level surgical strategies from only a few demonstrations, significantly reducing cognitive workload while achieving high-precision, coordinated autonomous bimanual manipulation.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• dual-arm robotics
• ultrasound-guided intervention
• bimanual coordination
• imitation learning
• asymmetric manipulation

Innovation

• phase-aware imitation learning
• dual-arm robotic system
• ultrasound-guided intervention
• asymmetric bimanual coordination
• teleoperated demonstration