  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%.
