Exploring the Use of Robots for Diary Studies

As interest in studying in-the-wild human-robot interaction grows, there is a need for methods to collect data over time and in naturalistic or potentially private environments. HRI researchers have increasingly used the diary method for these studies, asking study participants to self-administer a structured data collection instrument, i.e., a diary, over a period of time. Although the diary method offers a unique window into settings that researchers may not have access to, they also lack the interactivity and probing that interview-based methods offer. In this paper, we explore a novel data collection method in which a robot plays the role of an interactive diary. We developed the Diary Robot system and performed inhome deployments for a week to evaluate the feasibility and effectiveness of this approach. Using traditional text-based and audio-based diaries as benchmarks, we found that robots are able to effectively elicit the intended information. We reflect on our findings, and describe scenarios where the utilization of robots in diary studies as a data collection instrument may be especially applicable.

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Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/hri61500.2025.10974118
Citations
1
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
1.28

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article

Exploring the Use of Robots for Diary Studies

Bilge Mutlu, Michael F. Xu
1 citations
Multimodal Machine Learning Applications
1.28
article

Exploring the Use of Robots for Diary Studies

Bilge Mutlu, Michael F. Xu
article en
1 citations

Abstract

As interest in studying in-the-wild human-robot interaction grows, there is a need for methods to collect data over time and in naturalistic or potentially private environments. HRI researchers have increasingly used the diary method for these studies, asking study participants to self-administer a structured data collection instrument, i.e., a diary, over a period of time. Although the diary method offers a unique window into settings that researchers may not have access to, they also lack the interactivity and probing that interview-based methods offer. In this paper, we explore a novel data collection method in which a robot plays the role of an interactive diary. We developed the Diary Robot system and performed inhome deployments for a week to evaluate the feasibility and effectiveness of this approach. Using traditional text-based and audio-based diaries as benchmarks, we found that robots are able to effectively elicit the intended information. We reflect on our findings, and describe scenarios where the utilization of robots in diary studies as a data collection instrument may be especially applicable.

University of Wisconsin–Madison (US)
National Science Foundation
Openalex Percentile: Top 20%
Multimodal Machine Learning Applications
1.28
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