This study proposes a dual-level analytical approach to observing human–robot interactions in a real-world public setting, specifically a science museum. Observation plays a crucial role in human–robot interaction research by enabling the capture of nuanced and context-sensitive behaviors that are often missed by post-interaction surveys or controlled laboratory experiments. Public environments such as museums pose particular challenges due to their dynamic and open-ended nature, requiring methodological approaches that balance ecological validity with analytical rigor. To address these challenges, we introduce a dual-level approach for behavioral observation, integrating statistical analysis across demographic groups with time-series modeling of individual engagement dynamics. At the group level, we analyzed engagement patterns based on age and gender, revealing significantly higher interaction levels among children and adolescents compared to adults. At the individual level, we employed temporal behavioral analysis using a Hidden Markov Model to identify sequential engagement states—low, moderate, and high—derived from time-series behavioral patterns. This approach offers both broad and detailed insights into visitor engagement, providing actionable implications for designing adaptive and socially engaging robot behaviors in complex public environments. Furthermore, it can facilitate the analysis of social robot interactions in everyday contexts and contribute to building a practical foundation for their implementation in real-world settings.

Social robots in public cultural venues, such as science museums, must engage diverse visitors through brief, one-off encounters where long-term user modeling is infeasible. This research examines immediately interpretable behavioral cues of a robot that can evoke a sense of personalization without storing or profiling individual users. First, a video-based observational study of expert and novice museum docents identified service strategies that enable socially adaptive communication. Building on these insights, three controlled laboratory studies investigated how specific cues from robots influence user perception. A video-based controlled study examined how recognition accuracy shapes users’ social impressions of the robot’s intelligence. Additional studies based on the Wizard-of-Oz (WoZ) method tested whether explanatory content aligned with participants’ background knowledge and whether explicit preference inquiry and memory-based continuity strengthened perceptions of personalization. Results showed that recognition accuracy improved social impressions, whereas knowledge alignment, explicit preference inquiry, and memory-based continuity cues increased perceived personalization. These findings demonstrate that micro-level personalization cues, interpretable within a short-term encounter, can support user-centered interaction design for social robots in public environments.

This paper introduces the investigation of the frontline service employees in order to identify and suggest the behavioral goals of the restaurant service robots. We conducted the survey-based study to examine the optimal service action selection evaluated by the restaurant service employees. In addition, there was a video-based exploratory behavior analysis to find out the different behavioral factors between expert and novice employees in the real restaurant. It was found out that the employees have the preferred service actions to perform in each service episode in restaurant setting and the service behaviors of the frontline service robot in restaurant needed to be designed to be proactive for effective service delivery.

This study proposes a dual-level analytical approach to observing human-robot interactions in a real-world public setting, specifically a science museum. Observation plays a crucial role in human-robot interaction research by enabling the capture of nuanced and context-sensitive behaviors that are often missed by post-interaction surveys or controlled laboratory experiments. Public environments such as museums pose particular challenges due to their dynamic and open-ended nature, requiring methodological approaches that balance ecological validity with analytical rigor. To address these challenges, we introduce a dual-level approach for behavioral observation, integrating statistical analysis across demographic groups with time-series modeling of individual engagement dynamics. At the group level, we analyzed engagement patterns based on age and gender, revealing significantly higher interaction levels among children and adolescents compared to adults. At the individual level, we employed temporal behavioral analysis using a Hidden Markov Model to identify sequential engagement states—low, moderate, and high—derived from time-series behavioral patterns. This approach offers both broad and detailed insights into visitor engagement, providing actionable implications for designing adaptive and socially engaging robot behaviors in complex public environments. Furthermore, it can facilitate the analysis of social robot interactions in everyday contexts and contribute to building a practical foundation for their implementation in real-world settings.