Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery
This addresses fatigue for surgeons using robotic surgery systems, but it is incremental as it builds on existing teleoperation methods.
The paper tackled the problem of surgeon fatigue from repetitive tasks in robotic surgery by proposing a deep reinforcement learning-based semi-autonomous control framework, resulting in a 19.1% reduction in completion time and a 58.7% reduction in travel length.
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%.