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

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.