论文标题
基于强化学习的半自主控制机器人手术
Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery
论文作者
论文摘要
近几十年来，手术机器人带来了巨大的好处，并见证了患者。随着灵巧的手术和高度的精度，手术机器人可以为患者提供更少的康复时间和更少的住院时间。但是，外科医生通过远距离进行了目前的实际用法中手术机器人的控制。在手术过程中，存在许多重复但简单的操作，这可能会给外科医生带来不必要的疲劳。在本文中，我们提出了一个基于学习的深度增强，用于机器人手术的半自主控制框架。用户研究表明，该框架可以将完成时间减少19.1％，而旅行期限则增加了58.7％。
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%.
