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

Feng Li, Pei Liu, Shiting Wang, Ning Wang, Zhongliang Jiang, Nassir Navab, Yuan Bi

TL;DR

The Dual-Arm Interventional Surgical System (DAISS) is presented, a teleoperated platform that collects high-fidelity dual-arm demonstrations and learns a phase-aware imitation policy for ultrasound-guided interventions and Experimental results show that DAISS can learn personalized expert strategies from limited demonstrations.

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.

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

TL;DR

The Dual-Arm Interventional Surgical System (DAISS) is presented, a teleoperated platform that collects high-fidelity dual-arm demonstrations and learns a phase-aware imitation policy for ultrasound-guided interventions and Experimental results show that DAISS can learn personalized expert strategies from limited demonstrations.

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.
Paper Structure (14 sections, 4 equations, 8 figures, 1 table)

This paper contains 14 sections, 4 equations, 8 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. METHODS
  3. System Overview
  4. Hardware Architecture and Setup
  5. Kinematic Control
  6. Bimanual Safety Mechanisms
  7. Phase-aware Imitation Learning Framework
  8. EXPERIMENTS
  9. System Feasibility Evaluation
  10. Safety evaluation
  11. Tracking evaluation
  12. Analysis of the Phase-Aware Imitation Learning
  13. DISCUSSION
  14. CONCLUSIONS

Figures (8)

  • Figure 1: Overview of the DAISS Platform and Phase-Aware Imitation Learning. Our proposed bimanual teleoperation system integrates multimodal perception to execute complex ultrasound-guided interventions. By leveraging a dynamic phase-aware policy, DAISS effectively decouples the operation workflow, optimally balancing temporal efficiency with fine-grained kinematic precision.
  • Figure 2: The DAISS Framework. (a) Leader Demonstration Generation: captures expert multimodal trajectories. (b) Robotic Follower Actuation: ensures safe, physical execution of the bimanual clinical workflow. (c) Phase-Aware Imitation Learning: maps the demonstrations to autonomous policies, employing a dynamic mask loss to resolve the speed-accuracy trade-off across different interventional phases.
  • Figure 3: Phase-Aware Imitation Learning Module. Network module color scheme: multimodal inputs (blue), transformer-based action chunking (purple), phase-aware module (green), and dual-arm outputs (orange) controlling the FR3 (left) and Panda (right) manipulators.
  • Figure 4: Kinematic collision avoidance. Green and yellow virtual cylinders represent the geometric envelopes of the robotic end-joints and tools. By acting as impenetrable spatial barriers, this setup inherently precludes mechanical interference between the dual manipulators during teleoperated maneuvers.
  • Figure 5: Translational Tracking Performance.
  • ...and 3 more figures