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arXiv · 2609.16766

EgoAsk: Egocentric Teaching of Personalized Object Knowledge for Household Robots

Yuanda Hu·Wenbin Zuo·Yiting Shen·Tianle Chen·Hector Fabio Calero Tobar·Yate Ge·Xiaohua Sun·Weiwei Guo
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Abstract

Unlike users, who know their own belongings and routines, household robots cannot easily acquire such personalized object knowledge automatically and depend on users to teach them. User-initiated teaching requires users to arrange dedicated teaching sessions and decide what to teach, even when they are unsure what the robot needs to learn. We introduce EgoAsk, a smart-glasses-based system that proactively embeds personalized object teaching into everyday activities. EgoAsk shares the user's first-person view with the robot, identifies gaps in personalized object knowledge, and analyzes ongoing activity to ask context-relevant questions that support future household assistance. To examine how teaching initiative and question timing affect users' teaching experiences, we conducted a within-subjects study with 18 participants and found lower reported knowledge-gap monitoring burden with robot-initiated questioning and less need for context reconstruction with EgoAsk. These findings characterize teaching burdens and timing preferences, offering design implications for egocentric robot-teaching systems.

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Yuanda Hu, Wenbin Zuo, Yiting Shen, Tianle Chen, Hector Fabio Calero Tobar, Yate Ge, Xiaohua Sun, Weiwei Guo. 2026-09-15. EgoAsk: Egocentric Teaching of Personalized Object Knowledge for Household Robots. https://arxiv.org/abs/2609.16766

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