# A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting

## TL;DR

This paper introduces a deep reinforcement learning-based tensioning method for surgical tissue cutting, aiming to automate tension control and improve accuracy and robustness in surgical procedures.

## Contribution

It proposes a novel multiple pinch point tensioning planner using deep reinforcement learning for autonomous surgical tissue cutting.

## Key findings

- Outperforms existing methods in simulation tests
- Demonstrates improved robustness and accuracy
- Validates effectiveness of the RL-based tensioning approach

## Abstract

Surgeons normally need surgical scissors and tissue grippers to cut through a deformable surgical tissue. The cutting accuracy depends on the skills to manipulate these two tools. Such skills are part of basic surgical skills training as in the Fundamentals of Laparoscopic Surgery. The gripper is used to pinch a point on the surgical sheet and pull the tissue to a certain direction to maintain the tension while the scissors cut through a trajectory. As the surgical materials are deformable, it requires a comprehensive tensioning policy to yield appropriate tensioning direction at each step of the cutting process. Automating a tensioning policy for a given cutting trajectory will support not only the human surgeons but also the surgical robots to improve the cutting accuracy and reliability. This paper presents a multiple pinch point approach to modelling an autonomous tensioning planner based on a deep reinforcement learning algorithm. Experiments on a simulator show that the proposed method is superior to existing methods in terms of both performance and robustness.