Gaze-based Learning from Demonstration In Surgical Robotics

TL;DR

This paper proposes a gaze-based probabilistic model to automate the camera arm control in surgical robotics, aiming to improve surgical flow and precision during minimally invasive procedures.

Contribution

It introduces a novel method using surgeon eye gaze and robot kinematic data to automate camera control in da Vinci surgical systems.

Findings

Successful modeling of gaze-based camera control

Improved automation of surgical camera movements

Potential to enhance surgical efficiency and safety

Abstract

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.