让机器人在复杂户外环境与人协同时，AR内容自动适应环境，提升可见性与操作效率。
fARfetch: Enabling Collocated AR-HRC in Large Visually Diverse Environments with VLM-Driven AR Content Adaptation
- 用视觉语言模型动态调整AR内容的颜色、大小和方向，保持远距离可视性。
- 用户实验显示任务完成时间快66%，心理负担降低43%，沮丧感下降66%。
- 适合户外巡检、远程协作等大范围复杂场景的AR人机协同应用。
增强现实（AR）可通过可视化机器人状态与意图，提升共处式人机协作效率，但在大型、视觉多样的环境（如户外）中，交互与内容可读性面临挑战，尤其在远距离或视线外时。本文提出fARfetch系统，集成三部分：(i) AR头显与机器人共享语义环境地图，通过可视化地标支持基于地标的目标指令；(ii) 上下文感知的微型世界表示，用于精细路径编辑；(iii) 基于视觉-语言模型的AR视图管理，联合调节虚拟内容的颜色、大小和朝向，以维持大范围多样环境中的可读性。系统基于Meta Quest 3头显与Unitree Go2四足机器人实现，并在真实户外30.5米尺度巡检任务中开展被试内实验（N=13）。结果表明，相比非AR基线，fARfetch任务完成时间缩短66%，心理负荷降低43%，时间压力减少34%，挫败感下降66%。自定义可读性调查证实其在大户外环境中有效维持了虚拟内容的可读性。
原文摘要 · Abstract (English)
Augmented Reality (AR) can improve collocated human-robot collaboration by making robot state and intent visible and enabling intuitive control, yet large, visually diverse environments like the outdoors challenge both interaction and content legibility, especially at long distances and beyond visual line of sight. We present fARfetch, an AR-HRC system that integrates (i) shared semantic environment mapping across an AR headset and robot that visualizes detected landmarks in AR to support landmark-grounded go-to commands, (ii) a context-aware world-in-miniature representation of the shared environment for fine-grained path authoring, and (iii) vision-language-model driven AR view management that jointly adapts virtual content color, size, and orientation to maintain legibility in large visually diverse environments. We implement fARfetch with a Meta Quest 3 headset and Unitree Go2 quadruped robot, and conduct a within-subjects user study (N=13) on a real-world large-scale (30.5m) outdoor inspection task. fARfetch yielded significantly faster completion times than a non-AR baseline (66%) and significantly lower workload in mental demand (-43%), temporal demand (-34%), and frustration (-66%). A custom legibility survey indicated fARfetch effectively maintained virtual content legibility in the large outdoor environment.
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