Public attitudes toward robots are often shaped by indirect exposure (e.g., media, staged demos), leaving open how direct, hands-on experience influences acceptance. In this study, we investigate how interacting with Boston Dynamics’ Spot, an agile, state-of-the-art quadruped robot, in a public pop-up booth affects perceptions of comfort and suitability across everyday and high-stakes environments. In a walk-up, 10-week pop-up booth, participants (N=753) completed pre–post surveys before and after driving Spot within curated Drive Scenes (Factory, Home, Hospital, Outdoor/Disaster). Measures captured comfort encountering robots and perceived suitability across Rated Contexts (RCs), affective reactions, and open-ended reflections. Hands-on control significantly increased comfort across all RCs, with the largest gains in Outdoor/Disaster, and increased perceived suitability—most in Home/Office/Hospital where baselines were lower. Improvements generalized beyond the experienced Drive Scene to other contexts. Age, gender, and prior familiarity moderated baseline levels and some changes, but hands-on exposure raised scores for all groups and attenuated several gaps. Thematic analysis showed memorable moments tied to locomotion, terrain adaptation, and expressive tilt; imagined roles consistently emphasized domestic assistance (e.g., cleaning, mobility), with entertainment/play and companionship emerging post-interaction. Together, these results demonstrate that brief, agency-granting encounters with a high-capability quadruped can broaden where people see robots as appropriate and diversify envisioned roles, offering a scalable model for public-facing HRI that fosters comfort, enthusiasm, and acceptance.

Hang Jiang, Xiajie Zhang, Robert Mahari, Daniel Kessler, Eric Ma, Tal August, Irene Li, Alex Pentland, Yoon Kim, Deb Roy, Jad Kabbara. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Pedagogical agent research has yielded fruitful results in both academic skill learning and meta-cognitive skill acquisition, often studied in instructional or peer-to-peer paradigms. In the past decades, child-centric pedagogical research, which emphasizes the learner's active participation in learning with self-motivation, curiosity, and exploration, has attracted scholarly attention. Studies show that combining child-driven pedagogy with appropriate adult guidance leads to efficient learning and a strengthened feeling of self-efficacy. However, research on using social robots for guidance in child-driven learning still remains open and under-explored. In our study, we focus on children's exploration as the vehicle in literacy learning and develop a social robot companion that provides guidance to encourage and motivate children to explore during a storybook reading interaction. To investigate the effect of the robot's explorative guidance, we compare it against a control condition in which children have full autonomy to explore and read the storybooks. We conduct a between-subjects study with 31 children aged 4 to 6, and the result shows that children who receive explorative guidance from the social robot exhibit a growing trend of self-exploration. Further, children's self-exploration in the explorative guidance condition is found correlated to their learning outcome. We conclude the study with recommendations for designing social agents to guide children's exploration and future research directions in child-centric AI-assisted pedagogy.

Prior work in affect-aware educational robots has often relied on a common belief that the relationship between student affect and learning is independent of agent behaviors (child's/robot's) or unidirectional (positive/negative but not both) throughout the entire student-robot interaction. We argue that the student affect-learning relationship should be interpreted in two contexts: (1) social learning paradigm and (2) sub-events within child-robot interaction. In our paper, we examine two different social learning paradigms where children interact with a robot that acts either as a tutor or a tutee. Sub-events within child-robot interaction are defined as task-related events occurring in specific phases of an interaction (e.g., when the child/robot gets a wrong answer). We examine sub-events at a macro level (entire interaction) and a micro level (within specific sub-events). In this paper, we provide an in-depth correlation analysis of children's facial affect and vocabulary learning. We found that children's affective displays became more predictive of their vocabulary learning when children interacted with a tutee robot who did not scaffold their learning. Additionally, children's affect displayed during micro-level events was more predictive of their learning than during macro-level events. Last, we found that the affect-learning relationship is not unidirectional, but rather is modulated by context, i.e., several affective states facilitated student learning when displayed in some sub-events but inhibited learning when displayed in others. These findings indicate that both social learning paradigm and sub-events within interaction modulate student affect-learning relationship.