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DP4AuSu: Autonomous Surgical Framework for Suturing Manipulation Using Diffusion Policy With Dynamic Time
Wenda Xu1, Zhihang Tan1, Zexin Cao1
1State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China.
Summary
This study introduces Diffusion Policy for Autonomous Suturing (DP4AuSu), achieving high success rates in robotic suturing. This novel approach significantly reduces suturing time, paving the way for complex surgical task automation.
Area of Science:
- Robotics
- Surgical Automation
- Machine Learning
Background:
- Imitation learning (IL) offers innovative solutions for autonomous robotic suturing.
- Surgical robots aid surgeons in complex manipulations.
Purpose of the Study:
- Introduce a novel framework for autonomous robotic suturing.
- Leverage diffusion policy for enhanced surgical task performance.
Main Methods:
- Developed Diffusion Policy for Autonomous Suturing (DP4AuSu).
- Integrated diffusion policy (DP) with dynamic time warping-based locally weighted regression.
- Utilized demonstrations to learn suturing policies.
Main Results:
- Achieved 94% success rate in simulation for insertion subtasks.
- Attained 85% success rate in real-world suturing trials.
- Reduced suturing time compared to conventional diffusion policy.
Conclusions:
- DP4AuSu effectively captures multimodal demonstrations for learning suturing policies.
- This represents the first application of diffusion policy in robotic suturing.
- The framework shows potential for automating more complex surgical tasks.
