Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery

• URL: http://arxiv.org/abs/2204.05433v1
• Date: Mon, 11 Apr 2022 22:59:33 GMT
• Title: Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery
• Authors: Ruiqi Zhu, Dandan Zhang and Benny Lo
• Abstract summary: We propose a deep reinforcement learning-based semi-autonomous control framework for robotic surgery. The framework can reduce the completion time by 19.1% and the travel length by 58.7%.
• Score: 13.940778824773414
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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%.

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