# Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery

## TL;DR

This paper introduces a deep reinforcement learning approach for autonomous robotic soft tissue cutting, utilizing multiple pinch points to improve accuracy and safety in complex cutting trajectories without requiring multi-gripper robots.

## Contribution

It proposes a novel multi-point tensioning method using deep reinforcement learning, enhancing cutting precision and safety in robotic surgery tasks.

## Key findings

- Multi-point tensioning outperforms single-point methods in accuracy.
- Deep reinforcement learning effectively finds optimal tensioning policies.
- Simulation results demonstrate improved reliability in soft tissue cutting.

## Abstract

In robotic surgery, pattern cutting through a deformable material is a challenging research field. The cutting procedure requires a robot to concurrently manipulate a scissor and a gripper to cut through a predefined contour trajectory on the deformable sheet. The gripper ensures the cutting accuracy by nailing a point on the sheet and continuously tensioning the pinch point to different directions while the scissor is in action. The goal is to find a pinch point and a corresponding tensioning policy to minimize damage to the material and increase cutting accuracy measured by the symmetric difference between the predefined contour and the cut contour. Previous study considers finding one fixed pinch point during the course of cutting, which is inaccurate and unsafe when the contour trajectory is complex. In this paper, we examine the soft tissue cutting task by using multiple pinch points, which imitates human operations while cutting. This approach, however, does not require the use of a multi-gripper robot. We use a deep reinforcement learning algorithm to find an optimal tensioning policy of a pinch point. Simulation results show that the multi-point approach outperforms the state-of-the-art method in soft pattern cutting task with respect to both accuracy and reliability.