# Group-Robot Interaction in the Wild: An Exploratory Field Study in Semi-public Spaces

Elisabetta Zibetti, Raphael Lorenzo-Louis, Fabio Amadio, Julien Wacquez, Jeffrey Becker, Bertrand Luvison, Serena Ivaldi

► To cite this version:

Elisabetta Zibetti, Raphael Lorenzo-Louis, Fabio Amadio, Julien Wacquez, Jeffrey Becker, et al. Group-Robot Interaction in the Wild: An Exploratory Field Study in Semi-public Spaces. 2026. [hal-05478495v2](https://hal.science/hal-05478495v2)

HAL Id: hal-05478495

https://hal.science/hal-05478495v2

Preprint submitted on 27 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Group-Robot Interaction
## in the Wild: An Exploratory Field Study in Semi-public Spaces

Elisabetta Zibetti

Université Paris 8, CHArt
F-93526 Paris, France
Inria, CNRS, UL, Loria, HUCEBOT
F-54600 Villers-les-Nancy, France
ezibetti@univ-paris8.fr

Raphael Lorenzo-Louis

Inria, CNRS, UL, Loria, HUCEBOT
F-54600 Villers-les-Nancy, France
CEA, List
F-91120 Palaiseau, France
raphael.lorenzo@inria.fr

Fabio Amadio

Inria, CNRS, UL, Loria, HUCEBOT
F-54600 Villers-les-Nancy, France
fabio.amadio@inria.fr

Julien Wacquez

ETIS (UMR8051), CYU, ENSEA, CNRS
F-95000 Cergy, France
julien.wacquez@gmail.com

Joffrey Becker

ETIS (UMR8051), CYU, ENSEA, CNRS
F-95000 Cergy, France
joffrey.becker@ensea.fr

Bertrand Luvison

University Paris-Saclay, CEA, List
F-91120 Palaiseau, France
bertrand.luvison@cea.fr

Serena Ivaldi

Inria, CNRS, UL, Loria, HUCEBOT
F-54600 Villers-les-Nancy, France
serena.ivaldi@inria.fr

<figure><img src="image_2.png" /><figcaption>Figure 1: Environment setup (left) and example of group dynamics leading to a group-robot interaction (right).</figcaption></figure>

## Abstract

How does interaction with robots differ between spontaneously formed groups and individuals? Despite increasing robot deployment in public spaces, this question remains understudied in real-world settings. We conducted a field study deploying a service stationary robot in semi-public office spaces, tracking 221 individuals (42 alone, 179 in groups) across 95 interaction opportunities. Cookies were placed on accessible trays, creating a low-barrier functional interaction opportunity (taking a cookie) while allowing observation of spontaneous social behaviors. Groups demonstrated significantly higher engagement: functional interactions and social gestures. Within groups, leader presence amplified social engagement threefold. These findings are consistent with descriptive norm theory: group presence and leader behavior were associated with increased social engagement, though context-specific factors may moderate these effects. Results highlight the potential value of group

detection for robots in multi-user environments, and demonstrate the feasibility of integrating psychological theory with automated tracking to study spontaneous human-robot encounters in the wild.

## CCS Concepts

* Human-centered computing → Field studies; • Computer systems organization → Robotics; • Computing methodologies → Image and video acquisition.

## Keywords

Group HRI, social influence, conformity, field study, multi-party interaction
# 1 Introduction

Service Robots are moving from controlled laboratory settings to real-world environments, serving in restaurants, providing direction in malls or in airports, encountering not isolated individuals but spontaneously formed and ad hoc groups. Thus, research has been also focusing on real-world social settings, studying both dyadic and multi-party HRI [2, 3, 7, 14, 23, 31]. Deploying robots in semi-public spaces brings additional research challenges [7, 24], particularly for anticipating and adapting to human behavior in multi-party contexts [4, 22]. Current research in group HRI is still largely in an exploratory phase [23], while solutions to technical group-related problems, including joint engagement estimation [18], social group detection [13], or gaze estimation [25], are emerging. Vision-Language Models (VLMs) offer novel capabilities in an holistic approach for spatial understanding and behavioral pattern recognition at the individual or group level [16]. However, two critical gaps remain. First, computer-vision-based systems can accurately detect which behaviors occur but rarely explain why they emerge in specific social contexts [27, 30]. Second, to enable genuine anticipation multi-party HRI, not merely reactive behavior, these systems require grounding in social-psychological principles that govern group dynamics in semi-public spaces [21] as well as anticipation capabilities [1]. Understanding how group presence, conformity mechanisms, and leader-follower dynamics influence robot encounters can inform both algorithmic development (e.g., group detection, engagement prediction) and behavioral design (e.g., whom to address, when to initiate interaction). Recent multi-party HRI work addressed these issues [14, 22, 26], demonstrating that group composition and intra-group relationships substantially affect engagement with robotic agents. Yet fundamental questions remain: Do groups systematically interact differently with robots compared to individuals? How do within-group social influence mechanisms (conformity, leader-follower dynamics) manifest in spontaneous encounters? Answering these questions provides both empirical grounding for technical anticipation systems and theoretical insights into social influence mechanisms in HRI contexts. This paper presents an exploratory field study combining automated person tracking with behavioral video coding to examine social influence effects in naturally-occurring human-robot encounters within a semi-public office building. We address two primary research questions:

**RQ1:** In real-world environments, do groups demonstrate higher rates of functional interactions and social gestures with a service robot compared to individuals?

**RQ2:** Does within-group social influence (leader-follower dynamics) affect individual interaction behavior? Specifically.

**RQ2a:** Do groups with identifiable leaders exhibit higher rates of functional interactions than groups without clear leadership?

**RQ2b:** Does leader presence increase social gesture frequency among group members?

We provide: (i) Empirical evidence of group versus individual interaction patterns with a robot in a real-world social scenario, from examining approximately 221 tracked people (among those, 151 functionally interacted with the robot) during 1.16h in two semi-public space locations (office area and cafeteria entrance); (ii) Practical insights identifying behavioral features (group size, leader presence) relevant for developing anticipatory robot behaviors in multi-user environments integrating person tracking with theory-driven behavioral coding grounded in psychology, sociology and anthropology theory and methods.

# 2 Background

## 2.1 Descriptive Norms and Social Influence in Group Contexts

Social psychological research established that group presence fundamentally alters individual behavior through informational influence (believing others possess correct information) and normative influence (desire for social acceptance) [12]. Classical conformity studies demonstrated these effects in controlled settings [5, 6], while sociological work emphasizes imitation as a fundamental mechanism of collective behavior, whereby practices and affects circulate within groups through processes of modeling and counter-imitation [29]. Focus theory of normative conduct provides a particularly relevant framework for understanding robot encounters [9]. It distinguishes between descriptive norms (perceptions of what others actually do) and injunctive norms (perceptions of what others approve). Descriptive norms are particularly powerful in ambiguous situations: they motivate action by providing evidence of what is likely to be effective and adaptive behavior. When individuals observe others performing an action, particularly multiple people engaging in the same behavior, this creates automatic imitative response tendencies [10, 11]. In HRI contexts, this has important implications: the mere presence of co-present others creates descriptive norms about appropriate robot interaction [7]. One of the challenges for human interactants is to define the framework for an interaction that is often perceived as interspecific [31]. When one group member approaches a robot, they establish a behavioral precedent that signals to others that such interaction is normative and appropriate.

## 2.2 Group Dynamics in HRI

Emerging research on multi-party HRI reveals that group composition and intra-group relationships influence engagement with robotic agents [14, 26]. [14] found that entitative groups (cohesive units like families and friends) exhibited more positive attitudes and longer interactions with a guidance robot in a shopping mall compared to less cohesive groups, establishing that group norms shape interaction patterns when encountering unfamiliar technology [19]. These findings align with [9] argument that descriptive norms are especially powerful in ambiguous situations.

[22] recently highlighted key computational challenges in group HRI, including detecting group presence, tracking variable-sized groups, and anticipating group behavior based on interpersonal relationships and spatial formations. [30] developed a sophisticated computational approach for analyzing micro-social interactions in a university campus, but it shows two main limitations: first, their temporal sample is very limited (22.5 minutes), whereas longitudinal observation is needed to distinguish stable patterns from novelty effects; second, their approach is exclusively computer-vision-based, while social psychological frameworks are needed to explain why observed behaviors emerge. Current literature lacks empirical data on how naturally-occurring groups interact with robots in real-life semi-public spaces over extended periods [20]. To address these gaps we placed a custom service robot in an office building and combined
automated tracking with theory-driven behavioral coding grounded
in social influence theory.

# 3 Method

## 3.1 Robotic platform

<figure><img src="image_3.png" /><figcaption>Figure 2: Front and side view of the service robot Shelfy.</figcaption></figure>

We designed Shelfy, a mobile service robot for human-proximate
tasks in public spaces, equipped with a full sensor suite for recording
interaction data. The robot remained stationary during data collec-
tion, featuring an interactive display with a smiling face and blinking
eyes to attract people's attention (Figure 2). Cookies were placed on
the top tray, creating a low-barrier functional interaction opportu-
nity while allowing observation of spontaneous social behaviors.

## 3.2 Study Setting, Data Collection and Coding

**Data collection and automated tracking:** Data were collected in a semi-public office building frequented by employees and occasional visitors. The robot was positioned at nine locations over a 20-day period, recording interactions during standard working hours (9:00–18:00). All procedures followed ethical protocols approved by Inria's Ethics Committee (COERLE Protocol #465).

Participants were informed in advance, via email and on-site sig-
nage, about the data collection and their right to withdraw. No ques-
tionnaires were administered in order to preserve naturalistic behav-
ior and no demographics data were collected. All videos and sound
recordings were stored locally and processed (detection, tracking) us-
ing Inria's compute facilities. Upon request (per RGPD compliance),
full episodes were deleted: face recognition and manual verification
were used to spot episodes where requesters' tracks appeared. A total
of 905 seconds of recordings were deleted following 3 requests. Video
data were stored on encrypted servers with restricted access. All
identifying information was anonymized prior to coding. Selected
anonymized clips were sent via a secure link to 3 independent coders.

The total dataset contains 71 hours of recordings collected over
two months to capture routine patterns and reduce novelty effects.
The raw dataset was subdivided into *sequences*, where each sequence

begins 10 seconds before a person is detected in the frame (approx-
imately 7m in front of the robot) and ends after a 10-second period
with no detections. Each sequence constitutes a single unit of analy-
sis, representing interactions opportunities. The cumulative duration
of these sequences is 11.3 hours, encompassing 4,300 tracked peo-
ple. An automated detection, segmentation, and tracking pipeline
was applied to equirectangular panoramic images from a 360° cam-
era mounted on the robot, enabling robust tracking of passersby
and users, including under occlusion. From the 71-hour dataset we
selected two strategic locations expected to maximize group obser-
vation: (i) *Cafeteria entrance* (*Cafet*): a highly social context with
drink- and meal-related foot traffic. (ii) *Office area entrance* (*Office*):
a professional context with work-related circulation. This yielded
1.16 hours across 95 sequences.

**Manual video coding:** Three independent coders analyzed video recordings – tagged as occurring at the cafeteria entrance or office area entrance – to extract behavioral variables not captured by automated systems. Coding focused on (i) *Group identification*: Individuals walking in close formation, speaking together, or displaying coordinated behavior were coded as groups. (ii) *Group sizes*: Ranged from 2 (individual) to 7 members. (iii) *Social gestures*: Whether tracked individuals performed behaviors indicating social engagement with the robot, including face-to-face gaze, waving, smiling before or after taking cookies, (binary: yes/no). (iv) *Within-group dynamics*: For groups, coders identified whether a "leader" (first member to approach/interact) was present, and whether other members ("followers") subsequently interacted. Inter-coder reliability was substantial across all variables: group identification (Fleiss' $\kappa = .95$), social gesture occurrence ($\kappa = .79$), and leader identification ($\kappa = .92$).

**Dependent variables** were: (i) *Presence of a group* (yes/no). (ii) *Functional interaction occurrence* (approached/took cookie: yes/no - transformed into numerical variable 1/0) and (iii) *total number in each sequence*. (iv) *Social gesture occurrence* (yes/no - transformed into numerical variable 1/0) and (v) total number in each sequence (vi) *Within-group conformity* (for groups: proportion of members who interacted after leader). (vii) *Location context*.

# 4 Results

## 4.1 Description of Data / Observation

Our field observations revealed four recurrent patterns of behavior
toward the robot, each attributing a different status or relevance to
it. Taken together, these behaviors highlight the ambiguity of the
robot's perceived ontology in everyday encounters [8]:

(1) **Ignoring the robot:** Some people simply crossed the robot's camera field without acknowledging it, ignoring it as irrelevant to their trajectory (a typical pattern in passing-through environments).

(2) **Treating the robot as furniture:** Others approached the tray to take food, but displayed no social cues.

(3) **Acknowledging the robot:** A subset of participants took an item and produced a brief gesture of thanks (a glance or a wave).

(4) **Testing or probing the robot:** Some conducted informal "investigations" (for instance by manipulating objects, waving at the camera, inspecting the tray, or moving around) to infer the robot's capabilities, likely assuming that it might be interactive.

Table 1 summarizes the recorded interactions.
<table><caption>**Table 1:** Number of sequences (#Seq; unit of analysis) by location and social configuration (individuals or in-group), and corresponding numbers of tracked individuals (#Tracks). Functional (Func) and social (Soc) interactions are reported as aggregated counts per sequence.</caption><thead><tr><th></th><th>#Seq</th><th>#Tracks</th><th>Func</th><th>Soc</th></tr></thead><tbody><tr><td rowspan="2">Individuals</td><td>Cafet</td><td>13</td><td>15</td><td>9</td><td>6</td></tr><tr><td>Office</td><td>27</td><td>27</td><td>26</td><td>3</td></tr><tr><td rowspan="2">Groups</td><td>Cafet</td><td>33</td><td>105</td><td>63</td><td>36</td></tr><tr><td>Office</td><td>22</td><td>74</td><td>53</td><td>28</td></tr></tbody></table>

## 4.2 First exploratory analysis and interpretation

### RQ1: Social Influence in Group Contexts: Group vs. Individual Interaction Patterns

Over the selected 95 sequences, the system tracked 221 individuals: 42 individuals alone (40 sequences) and 179 individuals in groups (55 group sequences). Repeated passersby who may appear across sequences were treated as independent due to anonymization.

**Functional interactions:** Groups demonstrated significantly higher interaction number (M=2.02, SD=1.41) compared to individuals (M=1.10, SD=0.71), F(1,91)=14.8, p<.001, η²=.136. This pattern remained consistent across both locations, with no significant interaction effect between group presence and location, F(1,91)=1.73, p=.191.

**Social gestures:** Groups substantially exhibited more social gestures (M=1.15, SD=1.37) than individuals (M=0.25, SD=0.49), F(1, 91)=13.00, p<.001, η²=.122. No significant location effect on interaction was observed.

**Likelihood of any interaction:** Sequence-level χ² analyses revealed location-specific patterns. In office sequences, the occurrences of functional interaction was more likely in individual than group contexts (100% vs. 81%, χ²(1)=5.61, p=.018), an effect not observed in cafeteria sequences (χ²(1)=0.54, p=.461) nor in the pooled dataset (χ²(1)=1.74, p=.187). Among group sequences, functional interaction number correlated positively with group size (ρ=.487, p<.001) and social gesture number (ρ=.317, p=.002), suggesting coordinated engagement. Overall, group sequences showed higher social-expressive behavior relative to functional engagement, consistent with descriptive norm and social facilitation accounts. However, alternative explanations warrant consideration: the office location's higher individual engagement may reflect contextual factors (e.g., cookie visibility, traffic patterns) rather than group dynamics. The greater variability in group social gestures may stem from differences in group cohesion, though this remains speculative without direct measurement.

### RQ2: Within-Group Social Influence

Among 55 group sequences, 30 (54.5%) included an identifiable leader (first to approach). **Functional interactions** did not differ significantly between leader-present (M=2.13, SD=1.38) and leader-absent groups (M=1.88, SD=1.45; U=329, p=.420). However, **social gestures** were significantly higher in leader-present groups (M=1.73, SD=1.51) than leader-absent groups (M=0.44, SD=0.71; U=178, p<.001), with greater variability in the former (Levene's p<.05). This dissociation –leader presence affecting social-expressive

but not functional behavior– is consistent with informational influence accounts, though our operationalization of leadership (first to approach) may conflate social influence with incidental positioning. The higher variability among leader-present groups may reflect heterogeneity in group cohesion or leader legitimacy, though these factors were not directly measured.

## 5 Conclusions, Limitations and Future Works

This exploratory field study examined group versus individual interaction patterns with a service robot in semi-public spaces. Our findings are consistent with descriptive norm theory [9] and informational influence mechanisms [12, 29]: group sequences showed higher rates of functional interactions and social gestures (RQ1), and leader presence was associated with increased social-expressive behavior (RQ2). However, the pattern was not uniform (individual engagement exceeded group engagement in the office location) suggesting context-specific factors may moderate these effects.

**Practical Implications:** Our findings suggest that robots deployed in multi-user environments may benefit from detecting group presence and early engagement cues to adapt interaction strategies accordingly (e.g., recognizing initial engagement as a signal for broader group-directed interaction). Our correlational findings suggest that positive engagement by one group member is associated with broader group engagement, though causal mechanisms remain to be tested experimentally.

**Limitations:** Our dataset, while larger than many field studies (1.16 hours, 221 individuals, 95 sequences) may reflect context-specific normative expectations that differ from other public spaces. Our operationalization of leadership (first to approach) is a simplification that may conflate social influence with incidental positioning or be an emergent property of the social norms regulating group activity (people prefer to follow someone rather than leave them out) [15]. The observational design cannot establish causality, and potential confounds (cookie availability, time-of-day, repeated passersby) were not controlled. Additionally, individuals within groups represent nested observations; future analyses should employ mixed-effects models to address non-independence.

**Future work directions:** (1) Longitudinal deployment (minimum 2 months) [17, 28] to distinguish novelty from stable patterns; (2) Cross-context replications examining whether group patterns generalize across museums, malls, and other public spaces; (3) Experimental manipulation of robot behavior to test causal mechanisms; (4) Integration of behavioral tracking with post-interaction surveys [30]. Despite these limitations, this study demonstrates the feasibility and value of combining automated tracking with behavioral coding to study naturally-occurring group HRI. As robots increasingly inhabit spaces where humans move in social units, understanding group dynamics becomes essential for successful robot integration.

## Acknowledgments

This work was funded by ANR in the France2030 program through project PEPR O2R AS3 (ANR-22-EXOD-0007).
References

[1] Gabriele Abbate, Alessandro Giusti, Viktor Schmuck, Oya Celiktutan, and Antonio Paolillo. 2024. Self-supervised prediction of the intention to interact with a service robot. *Robotics and Autonomous Systems* 171 (2024), 104568. doi:10.1016/j.robot.2023.104568

[2] Manith Adikari, Angelo Cangelosi, and Randy Gomez. 2023. Social Robot Mediator for Multiparty Interaction. *arXiv preprint arXiv:2310.13508* (2023). doi:10.48550/arXiv.2310.13508

[3] Virginie André and Yann Boniface. 2018. Quelques considérations interactionnelles autour d'une expérience robotique. In *WACAI 2018-Workshop sur les" *Affects, Compagnons Artificiels et Interactions*".

[4] Simone Arreghini, Gabriele Abbate, Alessandro Giusti, and Antonio Paolillo. 2024. A service robot in the wild: Analysis of users intentions, robot behaviors, and their impact on the interaction. In *2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*. IEEE, 2536–2541. doi:10.1109/IROS58592.2024.10801875

[5] Solomon E Asch. 1956. Studies of independence and conformity: I. A minority of one against a unanimous majority. *Psychological monographs: General and applied* 70, 9 (1956), 1. doi:10.1037/h0093718

[6] Solomon E Asch. 2016. Effects of group pressure upon the modification and distortion of judgments. In *Organizational influence processes*. Routledge, 295–303.

[7] Joffrey Becker. 2015. Le robot qui aimait l'art. In *8e Colloque International GeCSO, Gestion des Connaissances dans la Société et les Organisations*.

[8] Joffrey Becker. 2023. Artificial lives, analogies and symbolic thought: an anthropological insight on robots and AI. *Studies in History and Philosophy of Science* 99 (2023), 89–96. doi:10.1016/j.shpsa.2023.04.001

[9] Robert B Cialdini. 2007. Descriptive social norms as underappreciated sources of social control. *Psychometrika* 72, 2 (2007), 263–268. doi:10.1007/s11336-006-1560-6

[10] Robert B Cialdini, Raymond R Reno, and Carl A Kallgren. 1990. A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. *Journal of personality and social psychology* 58, 6 (1990), 1015. doi:10.1037/0022-3514.58.6.1015

[11] Emiel Cracco, Haeeun Lee, Goedele van Belle, Lisa Quenon, Patrick Haggard, Bruno Rossion, and Guido Orgs. 2021. EEG Frequency Tagging Reveals the Integration of Form and Motion Cues into the Perception of Group Movement. *Cerebral Cortex* 32, 13 (11 2021), 2843–2857. doi:10.1093/cercor/bhab385

[12] Morton Deutsch and Harold B Gerard. 1955. A study of normative and informational social influences upon individual judgment. *The journal of abnormal and social psychology* 51, 3 (1955), 629. doi:10.1037/h0046408

[13] Mahsa Ehsanpour, Fatemeh Saleh, Silvio Savarese, Ian Reid, and Hamid Rezatofighi. 2022. Jrdb-act: A large-scale dataset for spatio-temporal action, social group and activity detection. In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 20983–20992.

[14] Marlena R Fraune, Selma Šabanović, and Takayuki Kanda. 2019. Human group presence, group characteristics, and group norms affect human-robot interaction in naturalistic settings. *Frontiers in Robotics and AI* 6 (2019), 48. doi:10.3389/frobt.2019.00048

[15] Erving Goffman. 1967. Interaction Ritual-Essays on Face-to-Face works. London: Cox & Wyman (1967).

[16] Simindokht Jahangard, Zhixi Cai, Shiki Wen, and Hamid Rezatofighi. 2024. Jrdb-social: A multifaceted robotic dataset for understanding of context and dynamics of human interactions within social groups. In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 22087–22097.

[17] Takayuki Kanda, Rumi Sato, Naoki Saiwaki, and Hiroshi Ishiguro. 2007. A Two-Month Field Trial in an Elementary School for Long-Term Human-Robot Interaction. *IEEE Transactions on Robotics* 23, 5 (2007), 962–971. doi:10.1109/TRO.2007.904904

[18] Yubin Kim, Huili Chen, Sharifa Alghowinem, Cynthia Breazeal, and Hae Won Park. 2023. Joint Engagement Classification using Video Augmentation Techniques for Multi-person HRI in the wild. In *Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems* (London, United Kingdom) (AAMAS '23). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 698–707.

[19] Steven Lawrence, Melanie Jouaiti, Jesse Hoey, Chrystopher L. Nehaniv, and Kerstin Dautenhahn. 2025. The Role of Social Norms in Human-Robot Interaction: A Systematic Review. *J. Hum.-Robot Interact.* 14, 3, Article 56 (June 2025), 44 pages. doi:10.1145/3722120

[20] Aliaa Moualla. 2020. *Un robot au Musée: Apprentissage cognitif et conduite esthétique*. Ph.D. Dissertation. Université de Cergy-Pontoise. http://www.theses.fr/2020CYUN1002

[21] Ana Müller and Anja Richert. 2024. Egocentric Robots in a Human-Centric World? Exploring Group-Robot-Interaction in Public Spaces. *arXiv preprint arXiv:2407.18009* (2024). doi:10.48550/arXiv.2407.18009

[22] Ana Müller and Anja Richert. 2025. The Space Between Us: A Methodological Framework for Researching Bonding and Proxemics in Situated Group-Agent Interactions. *arXiv preprint arXiv:2506.11829* (2025). doi:10.48550/arXiv.2506.11829

[23] Massimiliano Nigro, Emmanuel Akinrintoyo, Nicole Salomons, and Micol Spitale. 2025. Social Group Human-Robot Interaction: A Scoping Review of Computational Challenges. In *2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI)*. IEEE, 468–478.

[24] Raquel Oliveira, Patricia Arriaga, Filipa Correia, and Ana Paiva. 2019. The stereotype content model applied to human-robot interactions in groups. In *2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)*. IEEE, 123–132. doi:10.1109/HRI.2019.8673171

[25] Fiona Ryan, Ajay Bati, Sangmin Lee, Daniel Bolya, Judy Hoffman, and James M Rehg. 2025. Gaze-Ile: Gaze target estimation via large-scale learned encoders. In *Proceedings of the Computer Vision and Pattern Recognition Conference*. 28874–28884.

[26] Sarah Sebo, Brett Stoll, Brian Scassellati, and Malte F. Jung. 2020. Robots in Groups and Teams: A Literature Review. *Proc. ACM Hum.-Comput. Interact.* 4, CSCW2, Article 176 (Oct. 2020), 36 pages. doi:10.1145/3415247

[27] Jie Su, Jianglan Huang, Linbo Qing, Xiaohai He, and Honggang Chen. 2022. A new approach for social group detection based on spatio-temporal interpersonal distance measurement. *Heliyon* 8, 10 (2022). doi:10.1016/j.heliyon.2022.e11038

[28] JaYoung Sung, Henrik I. Christensen, and Rebecca E. Grinter. 2009. Robots in the wild: understanding long-term use. In *Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction* (La Jolla, California, USA) (HRI '09). Association for Computing Machinery, New York, NY, USA, 45–52. doi:10.1145/1514095.1514106

[29] Gabriel Tarde. 1890. Les lois de l'imitation; Etude sociologique. *The American Journal of Psychology* 3, 2 (1890), 267.

[30] Jeroen van Ameijde, Sifan Cheng, Ka Shut Leung, Wenting Zhang, and Jiahua Dong. 2025. Decoding micro-social interactions in public space: a computer-vision-based method. *Computational Urban Science* 5, 1 (2025), 55. doi:10.1007/s43762-025-00211-y

[31] Denis Vidal and Philippe Gaussier. 2014. Un robot comme personne. Ontologies comparée et expérimentale au musée du quai Branly. *Terrain. Anthropologie & sciences humaines* 62 (2014), 152–165.