Surgical robotics is a rising field in medical technology and advanced robotics. Robot assisted surgery, or robotic surgery, allows surgeons to perform complicated surgical tasks with more precision, automation, and flexibility than is possible for traditional surgical approaches. The main type of robot assisted surgery is minimally invasive surgery, which could be automated and result in a faster healing time for the patient. The surgical robot we are particularly interested in is the da Vinci surgical system, which is developed and manufactured by Intuitive Surgical. In the current iteration of the system, the endoscopic camera arm on the da Vinci robot has to be manually controlled and calibrated by the surgeon during a surgical task, which interrupts the flow of the operation. The main goal of this capstone project is to automate the motion of the camera arm using a probabilistic model based on surgeon eye gaze data and da Vinci robot kinematic data.

Surgical robotics is a rapidly evolving field that is transforming the landscape of surgeries. Surgical robots have been shown to enhance precision, minimize invasiveness, and alleviate surgeon fatigue. One promising area of research in surgical robotics is the use of reinforcement learning to enhance the automation level. Reinforcement learning is a type of machine learning that involves training an agent to make decisions based on rewards and punishments. This literature review aims to comprehensively analyze existing research on reinforcement learning in surgical robotics. The review identified various applications of reinforcement learning in surgical robotics, including pre-operative, intra-body, and percutaneous procedures, listed the typical studies, and compared their methodologies and results. The findings show that reinforcement learning has great potential to improve the autonomy of surgical robots. Reinforcement learning can teach robots to perform complex surgical tasks, such as suturing and tissue manipulation. It can also improve the accuracy and precision of surgical robots, making them more effective at performing surgeries.

In 2016, Shademan et al reported complete in vivo, autonomous robotic anastomosis of porcine intestine using the Smart Tissue Autonomous Robot (STAR).1,2 Although conducted in a highly controlled experimental setting, STAR quantitatively outperformed human surgeons in a series of ex vivo and in vivo surgical tasks. These trials demonstrated nascent clinical viability of an autonomous soft-tissue surgical robot for the first time. Unlike conventional surgical robots which are controlled in real-time by humans and which have become commonplace in particular subspecialties, STAR was controlled by artificial intelligence (AI) algorithms, and received input from an array of visual and haptic sensors. Applications of AI to clinical data for diagnostic purposes have already begun to demonstrate capability approximating that of specialist physicians.3,4