Task Matters: Investigating Human Questioning Behavior in Different Household Service for Learning by Asking Robots
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912334849507328 |
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| author | Hu, Yuanda Jiani, Hou Junyu, Zhang Ge, Yate Sun, Xiaohua Guo, Weiwei |
| author_facet | Hu, Yuanda Jiani, Hou Junyu, Zhang Ge, Yate Sun, Xiaohua Guo, Weiwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Task Matters: Investigating Human Questioning Behavior in Different Household Service for Learning by Asking Robots Hu, Yuanda Jiani, Hou Junyu, Zhang Ge, Yate Sun, Xiaohua Guo, Weiwei Human-Computer Interaction Robotics 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. |
| title | Task Matters: Investigating Human Questioning Behavior in Different Household Service for Learning by Asking Robots |
| topic | Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2504.13916 |