Implicit Neural Field Guidance for Teleoperated Robot-assisted Surgery

Abstract

Teleoperated techniques enable remote human-robot interaction and have been widely accepted in robot-assisted surgeries. However, it is still hard to guarantee the safety of teleoperated surgery due to the imperfect input commands limited by remote perception, preventing teleoperated surgery from being widely used. We propose a new framework to avoid the collision of surgery robots and human tissue caused by inaccurate inputs. We directly take the medical volume data and propose to use the implicit neural field to guide teleoperated robot-assisted surgery. With guidance, the trajectory of the robot manipulator is optimized to safely work inside a narrow workspace. We evaluated our method in several aspects and conducted a real-world experiment on a head phantom. Experimental results show that our proposed method can effectively avoid the collision between the surgical tool and the human tissue during teleoperation.