Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery

TL;DR

This paper proposes a deep reinforcement learning framework for semi-autonomous control in robotic surgery, aiming to reduce surgeon fatigue and improve efficiency during repetitive tasks.

Contribution

It introduces a novel deep reinforcement learning-based control system specifically designed for semi-autonomous robotic surgery tasks.

Findings

Reduced surgery completion time by 19.1%

Decreased travel length by 58.7% during procedures

Demonstrated effectiveness through user study

Abstract

In recent decades, the tremendous benefits surgical robots have brought to surgeons and patients have been witnessed. With the dexterous operation and the great precision, surgical robots can offer patients less recovery time and less hospital stay. However, the controls for current surgical robots in practical usage are fully carried out by surgeons via teleoperation. During the surgery process, there exists a lot of repetitive but simple manipulation, which can cause unnecessary fatigue to the surgeons. In this paper, we proposed a deep reinforcement learning-based semi-autonomous control framework for robotic surgery. The user study showed that the framework can reduce the completion time by 19.1% and the travel length by 58.7%.