AI & ML
Beyond Activation: Gaze Invocation with Visible Status for an Embodied AR Assistant in Co-Located Collaboration
arXiv cs.HC0 views
arXiv:2610.09284v1 Announce Type: new Abstract: Wake words face an inherent trade-off: higher sensitivity reduces missed commands but increases accidental activations. In co-located augmented reality (AR), the system must also determine whether the user is speaking to the assistant or to a nearby person. We introduce a new perspective on this problem: combining gaze-based address with an embodied assistant, visible listening status, and turn management across activation, continued interaction, and release. In our implementation, users look at an assistant anchored in the scene and see activation progress and listening status on its body. Sustained gaze opens a local interaction channel; speech and playback keep it open when attention returns to the task; and inactivity closes it. We evaluated this design with 25 participants. After selecting the assistant's placement and dwell duration, each participant worked with a partner to plan a trip while using the assistant. The study recorded 500 interaction outcomes, including 496 assistant-directed utterance attempts. Gaze acquisition completed before speech for 490 of these attempts (98.8\%): 467 began while the channel remained open, whereas 23 began after it had been released. The other 6 attempts began before acquisition completed. Among 102 reviewed attempts in which gaze left after acquisition but before speech, the configured retention policy kept the channel open for 79 and released it before 23. The results show that an embodied target with visible status can support clear entry into an assistant interaction while revealing a different problem at release: after users look back to their work, they may not see that the assistant has stopped listening. We contribute the implemented gaze-invocation design and design implications for communicating assistant state after visual attention moves elsewhere.
Source:https://arxiv.org/abs/2610.09284PreviousAI-Assisted Submissions in Online Research Are Rare and Highly Concentrated but Routinely ApprovedNextMany Brains, One Geometry: A Shared Visual-Semantic Space for Cross-Dataset fMRI Decoding
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