Optimally Sparse Deep Reinforcement Learning Policies for Surgical Robot Task Automation

In today’s world, a robotic surgeon manually manipulates the robot via a console using joystick-like controllers. However, such manual methods of control require the surgeon to spend a significant amount of time performing routine but important tasks, such as suturing or tissue cutting, thousands of times over the course of a single surgery. By automating these tasks, we can improve the accuracy and efficiency of the surgery while also allowing the surgeon more time to focus on the more complex surgical tasks at hand.

• Medicine