AutoRing: Imitation Learning--based Autonomous Intraocular Foreign Body Removal Manipulation with Eye Surgical Robot

Abstract

Intraocular foreign body removal demands millimeter-level precision in confined intraocular spaces, yet existing robotic systems predominantly rely on manual teleoperation with steep learning curves. To address the challenges of autonomous manipulation (particularly kinematic uncertainties from variable motion scaling and variation of the Remote Center of Motion (RCM) point), we propose AutoRing, an imitation learning framework for autonomous intraocular foreign body ring manipulation. Our approach integrates dynamic RCM calibration to resolve coordinate-system inconsistencies caused by intraocular instrument variation and introduces the RCM-ACT architecture, which combines action-chunking transformers with real-time kinematic realignment. Trained solely on stereo visual data and instrument kinematics from expert demonstrations in a biomimetic eye model, AutoRing successfully completes ring grasping and positioning tasks without explicit depth sensing. Experimental validation demonstrates end-to-end autonomy under uncalibrated microscopy conditions. The results provide a viable framework for developing intelligent eye-surgical systems capable of complex intraocular procedures.