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

TL;DR

This paper introduces DAISS, a dual-arm robotic system that learns phase-aware imitation policies for ultrasound-guided interventions, enabling precise, coordinated bimanual control with limited expert demonstrations.

Contribution

The paper presents a novel phase-aware imitation learning approach for dual-arm robots in medical interventions, integrating real-time ultrasound feedback and flexible teleoperation.

Findings

DAISS successfully learns expert strategies from limited data.

The phase-aware architecture improves coordination and smoothness of dual-arm motions.

Experimental results demonstrate enhanced precision and reduced workload in ultrasound-guided tasks.

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.