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Surgical embodied intelligence for generalized task autonomy in laparoscopic robot-assisted surgery.

Yonghao Long1, Anran Lin1, Derek Hang Chun Kwok2

  • 1Department of Computer Science and Engineering, Chinese University of Hong Kong, HKSAR, China.

Science Robotics
|July 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an open-source simulator for surgical robots, enabling embodied artificial intelligence to achieve autonomous task performance. The developed methods demonstrate zero-shot transfer from simulation to real-world surgical scenarios.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Surgical Technology

Background:

  • Current surgical robots automate specific tasks but struggle with generalization in diverse surgical environments.
  • Embodied intelligence for general-purpose robot learning is promising but underutilized in medicine.

Purpose of the Study:

  • To develop and validate an open-source surgical embodied intelligence simulator for reinforcement learning.
  • To enable zero-shot transfer of simulation-trained policies to real-world surgical robots.
  • To advance autonomous capabilities in minimally invasive surgery.

Main Methods:

  • Introduced an open-source surgical embodied intelligence simulator for interactive reinforcement learning.
  • Developed a paradigm combining data-driven policy and classic controllers, including visual parsing with stereo depth estimation and image segmentation.
  • Utilized a visual foundation model for complex scene handling in visual parsing.

Main Results:

  • Demonstrated autonomous skill acquisition in seven game-based tasks on the da Vinci Research Kit.
  • Achieved automation of five surgical assistive tasks on ex vivo animal tissues using the Sentire surgical system.
  • Validated learned policies in a live-animal trial for three tasks in dynamic in vivo environments.

Conclusions:

  • The open-source infrastructure and general-purpose learning paradigm facilitate research in embodied intelligence for autonomous surgical robots.
  • The proposed method enables robust performance across varied conditions, including different scenes, object sizes, instrument types, and illuminations.
  • This work paves the way for more adaptable and intelligent surgical robotic systems.