fARfetch: Enabling Collocated AR-HRC in Large Visually Diverse Environments with VLM-Driven AR Content Adaptation

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.

ARTOO-DARTU: Studying AR-HRC With AR Obstruction Mitigation During a Warehouse Task

Human-robot collaboration (HRC) often requires robot intentions and internal states to be conveyed to users for task efficiency and safety. Recently, augmented reality (AR) situated analytics provide such real-time robot feedback in HRC contexts. However, AR situated analytics can obstruct important real-world elements, posing safety and usability risks, especially when content is dynamically positioned relative to movements of mobile robots in a warehouse HRC scenario. In this paper, we introduce the Augmented Reality Technique Of Obstruction Deterrence while Aiding Robotic Teaming for Users (ARTOO-DARTU), an AR system tailored specifically for warehouse HRC that enables real-time robot situated analytics and control while preserving visibility of the real world through an obstruction detection and mitigation pipeline (ODM) that is uniquely suited for AR-HRC. To evaluate ARTOO-DARTU, we developed Pocket MonstARs, a controlled gamified abstraction of HRC warehouse inventory picking in which virtual monsters serve as proxies for pick targets, while labeled and object-marked boxes preserve the real-world identification demands of the picking task. In a 34-participant user study, we found that our designed AR situated analytics yielded a 46% increase in efficiency on the overall HRC task, but only when the ODM was active. Participants with the ODM active were also 61% faster on subtasks requiring visibility of the real world. Our findings demonstrate that, when paired with our developed ODM to prevent real-world obstructions, the situated analytics in ARTOO-DARTU can significantly enhance efficiency and user experience in AR-HRC warehouse scenarios.

Will You Be Aware? Eye Tracking-Based Modeling of Situational Awareness in Augmented Reality

Augmented Reality (AR) systems, while enhancing task performance through real-time guidance, pose risks of inducing cognitive tunneling-a hyperfocus on virtual content that compromises situational awareness (SA) in safety-critical scenarios. This paper investigates SA in AR-guided cardiopulmonary resuscitation (CPR), where responders must balance effective compressions with vigilance to unpredictable hazards (e.g., patient vomiting). We developed an AR app on a Magic Leap 2 that overlays real-time CPR feedback (compression depth and rate) and conducted a user study with simulated unexpected incidents (e.g., bleeding) to evaluate SA, in which SA metrics were collected via observation and questionnaires administered during freeze-probe events. Eye tracking analysis revealed that higher SA levels were associated with greater saccadic amplitude and velocity, and with reduced proportion and frequency of fixations on virtual content. To predict SA, we propose FixGraphPool, a graph neural network that structures gaze events (fixations, saccades) into spatiotemporal graphs, effectively capturing dynamic attentional patterns. Our model achieved 83.0% accuracy (F1=81.0%), outperforming feature-based machine learning and state-of-the-art time-series models by leveraging domain knowledge and spatial-temporal information encoded in ET data. These findings demonstrate the potential of eye tracking for SA modeling in AR and highlight its utility in designing AR systems that ensure user safety and situational awareness.