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Sim-to-real domain adaptation based completion level recognition for autonomous micro-drilling in biomedical

Enduo Zhao1,2, Saul Alexis Heredia Perez3, Kanako Harada3

  • 1Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8654, Japan. endowzhao@mail.tsinghua.edu.cn.

Scientific Reports
|November 28, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances autonomous micro-drilling for surgery by using a novel simulation-to-real model. This approach significantly reduces annotation time and improves drilling accuracy, making it more practical for biomedical applications.

Keywords:
Autonomous micro-drillingBiomedical applicationDomain adaptationSim-to-real transfer

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Computer Vision

Background:

  • Micron-level precision drilling in bone is crucial for surgical and neuroscience applications.
  • Current manual and imaging-based methods face limitations in speed, accuracy, and adaptability.
  • Previous autonomous systems relied on manual annotation, hindering scalability and performance.

Purpose of the Study:

  • To enhance an autonomous micro-drilling system using simulation-to-real domain adaptation.
  • To reduce the reliance on manual annotation and improve system accuracy and scalability.
  • To validate the effectiveness of a task-specific adversarial model for bridging the sim-to-real gap.

Main Methods:

  • Developed a photorealistic simulator to generate synthetic data for training.
  • Implemented a task-specific adversarial model for domain adaptation.
  • Trained and tested the enhanced system on eggshell drilling tasks.

Main Results:

  • Annotation time reduced from 600 s/frame to 1.8 s/frame.
  • Drilling success rate improved from 80% to 85% over 20 trials.
  • Demonstrated significant reduction in the domain gap between simulated and real-world data.

Conclusions:

  • Simulation-based training combined with domain adaptation effectively improves autonomous micro-drilling.
  • The novel adversarial model enhances system performance and reduces manual annotation burden.
  • This approach shows promise for advancing precision drilling in biomedical applications.