Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery
Year: 2022
Type: preprint
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%.
Source: arXiv (Cornell University)
Authors: Zhu, Ruiqi, Zhang, Dandan, Lo, Benny
Institutions: Imperial College London, University of Bristol
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4297399215