fARfetch: 大規模で視覚的に多様な環境におけるVLM駆動ARコンテンツ適応による共同配置型AR-HRCの実現
fARfetch: Enabling Collocated AR-HRC in Large Visually Diverse Environments with VLM-Driven AR Content Adaptation
屋外などの大規模で視覚的に多様な環境でのAR-HRCを改善するため、共有セマンティックマッピング、文脈認識型ワールドインミニチュア、VLMによるARビュー管理を統合したシステムを提案し、実環境でのユーザスタディにより有効性を示した。
著者: Christian Fronk, Hanting Ye, David Hunt, Miroslav Pajic, Maria Gorlatova
分類: cs.RO, cs.CV, cs.HC
原文アブストラクト
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.