超越物体选择：任意位置的无标记基于注视的机器人放置

中文摘要 AI 辅助

基于注视的辅助操作通常支持物体选择，而任意位置放置需要头戴设备与机器人之间的精确空间对齐。但对于基于注视的操作，姿态精度未必能转化为任务精度：平移和旋转误差会共同影响转换后的注视射线，且可能相互补偿。为从面向任务的视角研究跨设备对齐，我们提出一种无标记交互框架和专用跨设备数据集；针对机器人参考点稀疏的问题，提出基于图的参考选择方法；还在统一协议下开发并评估了多种面向任务的对齐流程，引入直接测量注视指定目标空间误差的注视-表面交点误差（GSIE）。实验表明，按传统姿态指标排名靠前的对齐方法在GSIE中未必最优，证明了在任务层面评估基于注视的操作的重要性。

英文摘要

Gaze-based assistive manipulation typically supports object selection, while arbitrary-position placement requires accurate spatial alignment between the headset and robot. However, for gaze-based manipulation, pose accuracy does not necessarily translate into task accuracy: translational and rotational errors jointly affect the transformed gaze ray and may compensate for each other. To study cross-device alignment from this task-oriented perspective, we present a markerless interaction framework and a dedicated cross-device dataset. We propose Graph-based Reference Selection to address sparse robot references. We further develop and benchmark multiple task-specific alignment pipelines under a unified protocol. Specifically, we introduce Gaze--Surface Intersection Error (GSIE), which directly measures the spatial error of the gaze-specified target. Experiments show that alignment methods ranked highly by conventional pose metrics are not always optimal in GSIE, demonstrating the importance of evaluating gaze-based manipulation at the task level.

发表机构

• University of Tuebingen（蒂宾根大学）
• Nanyang Technological University（南洋理工大学）
• Mercedes-Benz（梅赛德斯-奔驰）

机构由 AI 辅助整理，请以论文原文为准。