用动态校准提升机器人眼内取异物精度，实现无需深度感知的自主操作。
RCM-ACT: Imitation Learning with Dynamic RCM Calibration for Autonomous Intraocular Foreign Body Removal
- 通过动态校准解决器械运动不一致问题，结合分块动作变换器架构。
- 在未标定显微镜下完成任务，抓取偏差仅0.686毫米，成功率达55%。
- 适合研发智能眼科手术机器人，尤其擅长高精度微创操作场景。
眼内异物取出需在狭小空间内达到毫米级精度，现有机器人系统多依赖人工遥操作，学习曲线陡峭。为应对自主操控中的运动学不确定性，特别是可变运动缩放与远程中心点（RCM）偏移问题，本文提出RCM-ACT框架，用于眼内环状异物的自主抓取与定位。该方法融合动态RCM校准以解决因器械差异引起的坐标系不一致，并设计了结合动作分块变换器与全流程运动重对齐的架构。模型仅基于人工眼模型中专家示范的立体视觉数据和器械运动学训练，无需显式深度感知即可完成抓取与定位任务。实验验证了在未标定显微条件下端到端自主性的成功实现，平均三维抓取偏差为0.686毫米，20次任务中成功11次。结果表明该框架为复杂眼内手术智能化提供了可行路径。
原文摘要 · Abstract (English)
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 Remote Center of Motion (RCM) point variation, we propose RCM-ACT, an imitation learning framework for autonomous intraocular foreign body ring manipulation. Our approach integrates RCM dynamic calibration to resolve coordinate system inconsistencies caused by intraocular instrument variation and introduces the RCM-ACT architecture, which combines action chunking transformers with episode-level kinematic realignment. Trained solely on stereo visual data and instrument kinematics from expert demonstrations in an artificial eye model, RCM-ACT successfully completes ring grasping and positioning tasks without explicit depth sensing. Experimental validation demonstrates the successful implementation of end-to-end autonomy under uncalibrated microscopy conditions, achieving a mean 3-D Euclidean grasp deviation of 0.686 mm and 11/20 full-task successes. The results provide a viable framework for developing intelligent eye surgical systems capable of complex intraocular procedures.
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