发表机构
Southern University of Science and Technology（南方科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EgoAsk利用智能眼镜将个性化物体教学融入日常活动，通过主动提问降低用户教学负担，为家庭机器人教学系统设计提供启示。
AI 中文摘要
与了解自己物品和日常习惯的用户不同，家庭机器人难以自动获取这种个性化物体知识，需要依赖用户来教导它们。用户主动发起的教学要求用户安排专门的教学时段并决定教什么，即使他们不确定机器人需要学习什么。我们提出了EgoAsk，一个基于智能眼镜的系统，它主动将个性化物体教学嵌入到日常活动中。EgoAsk将用户的第一人称视角分享给机器人，识别个性化物体知识中的缺口，并分析正在进行的活动以提出与情境相关的问题，从而支持未来的家庭协助。为了考察教学主动性和提问时机如何影响用户的教学体验，我们进行了一项有18名参与者参与的受试者内研究，发现机器人主动提问时报告的知识缺口监控负担更低，且使用EgoAsk时对情境重建的需求更少。这些发现刻画了教学负担和时机偏好，为自我中心机器人教学系统提供了设计启示。
英文摘要
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.
