不同家务任务需不同提问策略，机器人应据此调整问法。
Task Matters: Investigating Human Questioning Behavior in Different Household Service for Learning by Asking Robots
- 通过分析用户在冰箱整理和调酒中的提问，发现任务类型影响问题类型与时间顺序。
- 目标导向任务中早期询问偏好，流程导向任务中持续追问步骤与偏好。
- 为机器人设计自适应提问策略提供实证依据，适合人机协作研究者参考。
学习通过提问（LBA）使机器人能在执行任务时识别知识缺口，并通过针对性提问获取缺失信息。然而，不同任务通常需要不同类型的问题，如何相应调整提问策略仍缺乏研究。本文通过28名参与者参与的人类-人类实验，探究在两种典型家庭服务任务——目标导向任务（冰箱整理）与流程导向任务（调酒）中的提问行为。采用包含获取知识、认知过程和问题形式三个维度的结构化框架分析提问。结果表明，参与者会根据任务结构调整问题类型及其时间顺序：目标导向任务中更早提出关于用户偏好的问题；流程导向任务中则持续并行地询问操作步骤与偏好。这些发现为开发任务敏感型提问策略提供了可操作的洞见，有助于提升机器人在人机协作中获取信息的效率与个性化水平。
原文摘要 · Abstract (English)
Learning by Asking (LBA) enables robots to identify knowledge gaps during task execution and acquire the missing information by asking targeted questions. However, different tasks often require different types of questions, and how to adapt questioning strategies accordingly remains underexplored. This paper investigates human questioning behavior in two representative household service tasks: a Goal-Oriented task (refrigerator organization) and a Process-Oriented task (cocktail mixing). Through a human-human study involving 28 participants, we analyze the questions asked using a structured framework that encodes each question along three dimensions: acquired knowledge, cognitive process, and question form. Our results reveal that participants adapt both question types and their temporal ordering based on task structure. Goal-Oriented tasks elicited early inquiries about user preferences, while Process-Oriented tasks led to ongoing, parallel questioning of procedural steps and preferences. These findings offer actionable insights for developing task-sensitive questioning strategies in LBA-enabled robots for more effective and personalized human-robot collaboration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。