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Related Experiment Video

Updated: Sep 16, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
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SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning.

Ji Woong Brian Kim1, Juo-Tung Chen1, Pascal Hansen1

  • 1Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA.

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Summary

This study introduces a hierarchical framework for autonomous surgery, enabling robots to perform complex procedures like cholecystectomy with 100% success. The system uses language-based planning for improved error recovery in real-world surgical tasks.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Surgical Technology

Background:

  • Current autonomous surgery research is limited to simple tasks in controlled settings.
  • Real-world surgery requires dexterity, long-term operation, and adaptability to tissue variability, which existing methods struggle with.

Purpose of the Study:

  • To develop a hierarchical framework for dexterous, long-horizon autonomous surgical procedures.
  • To enable robots to generalize to the variability of human tissue and recover from errors.

Main Methods:

  • A hierarchical framework with a high-level task planning policy and a low-level trajectory generation policy.
  • The high-level planner uses language space for task instructions and error correction.
  • Validation through ex vivo cholecystectomy experiments and ablation studies.

Main Results:

  • Achieved a 100% success rate in autonomous ex vivo cholecystectomy on eight gallbladders.
  • Demonstrated improved error recovery from suboptimal states in dynamic surgical environments.
  • The hierarchical approach enhanced policy robustness for realistic surgical applications.

Conclusions:

  • The proposed framework enables step-level autonomy in surgical procedures.
  • This research marks a significant advancement toward the clinical deployment of autonomous surgical systems.
  • The language-guided hierarchical approach is effective for complex, long-horizon surgical tasks.