用眼动控制机械臂完成日常任务，准确率达97.9%。
Eye-Tracking-Driven Control in Daily Task Assistance for Assistive Robotic Arms
- 结合任务图标与特征匹配，实现眼动到物体选择的精准映射。
- 在眼动-机械臂协同配置下，任务选择准确率高达97.9%。
- 开源框架支持新任务扩展，适合严重肢体残疾者独立生活使用。
共享控制通过降低用户负担、提升机器人自主性，改善人机交互。当前基于眼动的控制方法存在3D注视估计不准、多任务间注视意图难以区分等问题。本文提出一种眼动驱动控制框架，帮助重度肢体残疾者独立完成日常任务。系统采用任务图标作为标记物，结合特征匹配技术，在眼动-机械臂配置下传输所选物体数据，无需预先知道用户与物体的相对位置。实验显示，该框架在97.9%的测量中正确识别了物体与任务选择。评估中发现的问题已记录为经验教训，并开放源代码。由于集成了先进目标检测模型，系统可灵活适配新任务与新物体。
原文摘要 · Abstract (English)
Shared control improves Human-Robot Interaction by reducing the user's workload and increasing the robot's autonomy. It allows robots to perform tasks under the user's supervision. Current eye-tracking-driven approaches face several challenges. These include accuracy issues in 3D gaze estimation and difficulty interpreting gaze when differentiating between multiple tasks. We present an eye-tracking-driven control framework, aimed at enabling individuals with severe physical disabilities to perform daily tasks independently. Our system uses task pictograms as fiducial markers combined with a feature matching approach that transmits data of the selected object to accomplish necessary task related measurements with an eye-in-hand configuration. This eye-tracking control does not require knowledge of the user's position in relation to the object. The framework correctly interpreted object and task selection in up to 97.9% of measurements. Issues were found in the evaluation, that were improved and shared as lessons learned. The open-source framework can be adapted to new tasks and objects due to the integration of state-of-the-art object detection models.
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