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What Could a Social Mediator Robot Do? Lessons from Real-World Mediation Scenarios (2306.17379v1)

Published 30 Jun 2023 in cs.RO and cs.HC

Abstract: The use of social robots as instruments for social mediation has been gaining traction in the field of Human-Robot Interaction (HRI). So far, the design of such robots and their behaviors is often driven by technological platforms and experimental setups in controlled laboratory environments. To address complex social relationships in the real world, it is crucial to consider the actual needs and consequences of the situations found therein. This includes understanding when a mediator is necessary, what specific role such a robot could play, and how it moderates human social dynamics. In this paper, we discuss six relevant roles for robotic mediators that we identified by investigating a collection of videos showing realistic group situations. We further discuss mediation behaviors and target measures to evaluate the success of such interventions. We hope that our findings can inspire future research on robot-assisted social mediation by highlighting a wider set of mediation applications than those found in prior studies. Specifically, we aim to inform the categorization and selection of interaction scenarios that reflect real situations, where a mediation robot can have a positive and meaningful impact on group dynamics.

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References (76)
  1. H. Jokinen, “Social mediation-working towards inclusion from amidst exclusion,” in 49th Societas Ethica Annual Conference 2012; Theme: Ethics and Migration; August 23-26; 2012; Lucian Blaga University Sibiu; Romania, no. 097.   Linköping University Electronic Press, 2014, pp. 135–146.
  2. S. Viller, “The Group Facilitator: A CSCW Perspective,” in 2nd European Conference on Computer-Supported Cooperative Work (ECSCW 1991).   Springer, Dordrecht, 1991, pp. 81–95.
  3. G. De Abreu and E. Elbers, “The social mediation of learning in multiethnic schools: Introduction,” European Journal of Psychology of Education, vol. 20, no. 1, pp. 3–11, 2005.
  4. M. César and I. Oliveira, “The curriculum as a tool for inclusive participation: Students’ voices in a case study in a portuguese multicultural school,” European Journal of Psychology of Education, vol. 20, no. 1, pp. 29–43, 2005.
  5. A. G. Sutcliffe, V. Gonzalez, J. Binder, and G. Nevarez, “Social mediating technologies: Social affordances and functionalities,” International Journal of Human-Computer Interaction, vol. 27, no. 11, pp. 1037–1065, 2011.
  6. H. W. Park, I. Grover, S. Spaulding, L. Gomez, and C. Breazeal, “A model-free affective reinforcement learning approach to personalization of an autonomous social robot companion for early literacy education,” in 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), vol. 33, no. 01, 2019, pp. 687–694.
  7. I. Leite, A. Pereira, G. Castellano, S. Mascarenhas, C. Martinho, and A. Paiva, “Modelling empathy in social robotic companions,” in 19th International Conference on User Modeling, Adaptation and Personalization (UMAP 2011).   Springer, 2011, pp. 135–147.
  8. G. Gordon, S. Spaulding, J. K. Westlund, J. J. Lee, L. Plummer, M. Martinez, M. Das, and C. Breazeal, “Affective personalization of a social robot tutor for children’s second language skills,” in 30th AAAI Conference on Artificial Intelligence (AAAI 2016), vol. 30, no. 1, 2016.
  9. J. Broekens, M. Heerink, H. Rosendal et al., “Assistive social robots in elderly care: a review,” Gerontechnology, vol. 8, no. 2, pp. 94–103, 2009.
  10. I. Pedersen, S. Reid, and K. Aspevig, “Developing social robots for aging populations: A literature review of recent academic sources,” Sociology Compass, vol. 12, no. 6, p. e12585, 2018.
  11. M. P. Aylett, R. Gomez, E. Sandry, and S. Sabanovic, “Unsocial Robots: How Western Culture Dooms Consumer Social Robots to a Society of One,” in 41st ACM Conference on Human Factors in Computing Systems (CHI 2023): Extended Abstracts.   ACM, apr 2023, p. 427.
  12. G. Palestra, G. Varni, M. Chetouani, and F. Esposito, “A multimodal and multilevel system for robotics treatment of autism in children,” in International Workshop on Social Learning and Multimodal Interaction for Designing Artificial Agents (DAA 2016), 2016, p. 3.
  13. P. Marti, A. Pollini, A. Rullo, L. Giusti, and E. Grönvall, “Creative interactive play for disabled children,” in 8th International Conference on Interactive Design and Children (IDC 2009), 2009, pp. 313–316.
  14. A. W. H. Yee, T. Y. Kee, D. K. Limbu, A. T. H. Jian, T. A. Dung, and A. W. C. Yuen, “Developing a robotic platform to play with pre-school autistic children in a classroom environment,” in Workshop at SIGGRAPH Asia (WASA 2012), 2012, pp. 81–86.
  15. H. Lehmann, I. Iacono, B. Robins, P. Marti, and K. Dautenhahn, “‘Make it move’ playing cause and effect games with a robot companion for children with cognitive disabilities,” in 29th European Conference on Cognitive Ergonomics (ECCE 2011), 2011, pp. 105–112.
  16. S. Pliasa and N. Fachantidis, “Can a robot be an efficient mediator in promoting dyadic activities among children with autism spectrum disorders and children of typical development?” in 9th Balkan Conference on Informatics (BCI 2019), 2019, pp. 1–6.
  17. A. M. Abrams and A. M. Rosenthal-von der Pütten, “I–c–e framework: Concepts for group dynamics research in human-robot interaction: Revisiting theory from social psychology on ingroup identification (i), cohesion (c) and entitativity (e),” International Journal of Social Robotics, vol. 12, pp. 1213–1229, 2020.
  18. E. Short and M. J. Matarić, “Robot moderation of a collaborative game: Towards socially assistive robotics in group interactions,” in 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2017).   IEEE, 2017, pp. 385–390.
  19. S. Sebo, B. Stoll, B. Scassellati, and M. F. Jung, “Robots in Groups and Teams: A Literature Review,” Proceedings of the ACM on Human-Computer Interaction, vol. 4, no. CSCW2, pp. 1–36, 2020.
  20. H. Javed and N. Jamali, “Modeling Group Dynamics for Personalized Robot-Mediated Interactions,” in 18th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2023) - Workshop on Social Robots Personalisation: at the crossroads between Engineering and Humanities, 2023.
  21. K. H. Greenaway, E. K. Kalokerinos, and L. A. Williams, “Context is everything (in emotion research),” Social and Personality Psychology Compass, vol. 12, no. 6, p. e12393, 2018.
  22. B. Mesquita and M. Boiger, “Emotions in context: A sociodynamic model of emotions,” Emotion Review, vol. 6, no. 4, pp. 298–302, 2014.
  23. G. Sharma, S. Ghosh, and A. Dhall, “Automatic group level affect and cohesion prediction in videos,” in 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW 2019).   IEEE, 2019, pp. 161–167.
  24. J. S. Smith, C. Chao, and A. L. Thomaz, “Real-time changes to social dynamics in human-robot turn-taking,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015).   IEEE, 2015, pp. 3024–3029.
  25. V. Vonikakis, Y. Yazici, V. D. Nguyen, and S. Winkler, “Group happiness assessment using geometric features and dataset balancing,” in 18th ACM International Conference on Multimodal Interaction (ICMI 2016), 2016, pp. 479–486.
  26. D. A. Coker and J. K. Burgoon, “The Nature of Conversational Involvement and Nonverbal Encoding Patterns,” Human Communication Research, vol. 13, no. 4, pp. 463–494, 1987.
  27. H. Tennent, S. Shen, and M. Jung, “Micbot: A peripheral robotic object to shape conversational dynamics and team performance,” in 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2019).   IEEE, 2019, pp. 133–142.
  28. H. Hung and D. Gatica-Perez, “Estimating cohesion in small groups using audio-visual nonverbal behavior,” IEEE Transactions on Multimedia, vol. 12, no. 6, pp. 563–575, 2010.
  29. C. K. De Dreu and L. R. Weingart, “Task versus relationship conflict, team performance, and team member satisfaction: a meta-analysis.” Journal of Applied Psychology, vol. 88, no. 4, p. 741, 2003.
  30. J. R. Curhan, H. A. Elfenbein, and H. Xu, “What do people value when they negotiate? mapping the domain of subjective value in negotiation.” Journal of Personality and Social Psychology, vol. 91, no. 3, p. 493, 2006.
  31. D. Bohus and E. Horvitz, “Facilitating multiparty dialog with gaze, gesture, and speech,” in International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI 2010).   ACM, 2010, p. 5.
  32. M. T. Parreira, S. Gillet, M. Vázquez, and I. Leite, “Design Implications for Effective Robot Gaze Behaviors in Multiparty Interactions,” in 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2022), 2022, pp. 976–980.
  33. A. Shamekhi and T. W. Bickmore, “A multimodal robot-driven meeting facilitation system for group decision-making sessions,” in International Conference on Multimodal Interaction (ICMI 2019).   ACM, 2019, pp. 279–290.
  34. H. Erel, D. Trayman, C. Levy, A. Manor, M. Mikulincer, and O. Zuckerman, “Enhancing Emotional Support: The Effect of a Robotic Object on Human–Human Support Quality,” International Journal of Social Robotics, vol. 14, pp. 257–276, 2021.
  35. K. A. Jehn, “A multimethod examination of the benefits and detriments of intragroup conflict,” Administrative Science Quarterly, pp. 256–282, 1995.
  36. M. F. Jung, “Coupling interactions and performance: Predicting team performance from thin slices of conflict,” ACM Transactions on Computer-Human Interaction (TOCHI), vol. 23, no. 3, pp. 1–32, 2016.
  37. L. Brawley, A. Carron, and W. Widmeyer, “The development of an instrument to assess cohesion in sport teams: The group environment questionnaire,” Journal of Sport Psychology, vol. 7, no. 3, pp. 244–266, 1985.
  38. C. W. Leach, M. Van Zomeren, S. Zebel, M. L. Vliek, S. F. Pennekamp, B. Doosje, J. W. Ouwerkerk, and R. Spears, “Group-level self-definition and self-investment: a hierarchical (multicomponent) model of in-group identification.” Journal of Personality and Social Psychology, vol. 95, no. 1, p. 144, 2008.
  39. S. M. Danes, R. D. Leichtentritt, M. E. Metz, and C. Huddleston-Casas, “Effects of conflict styles and conflict severity on quality of life of men and women in family businesses,” Journal of Family and Economic Issues, vol. 21, pp. 259–286, 2000.
  40. S. Kim, S. H. Yella, and F. Valente, “Automatic Detection of Conflict Escalation in Spoken Conversations,” in 13th Annual Conference of the International Speech Communication Association (INTERSPEECH 2012), 2012, pp. 1167–1170.
  41. R. J. Lewicki, S. E. Weiss, and D. Lewin, “Models of conflict, negotiation and third party intervention: A review and synthesis,” Journal of Organizational Behavior, vol. 13, no. 3, pp. 209–252, 1992.
  42. J. H. Manson, G. A. Bryant, M. M. Gervais, and M. A. Kline, “Convergence of speech rate in conversation predicts cooperation,” Evolution and Human Behavior, vol. 34, no. 6, pp. 419–426, 2013.
  43. P. Wollstadt and M. Krüger, “Quantifying cooperation between artificial agents using synergistic information,” in IEEE Symposium Series on Computational Intelligence (SSCI 2022).   IEEE, dec 2022, pp. 1044–1051.
  44. D. Druckman, L. Adrian, M. F. Damholdt, M. Filzmoser, S. T. Koszegi, J. Seibt, and C. Vestergaard, “Who is best at mediating a social conflict? comparing robots, screens and humans,” Group Decision and Negotiation, vol. 30, pp. 395–426, 2021.
  45. S. Shen, P. Slovak, and M. F. Jung, ““Stop. I See a Conflict Happening.” A Robot Mediator for Young Children’s Interpersonal Conflict Resolution,” in 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2018), 2018, pp. 69–77.
  46. M. F. Jung, N. Martelaro, and P. J. Hinds, “Using robots to moderate team conflict: the case of repairing violations,” in 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2015), 2015, pp. 229–236.
  47. D. T. Campbell, “Common fate, similarity, and other indices of the status of aggregates of persons as social entities,” Behavioral Science, vol. 3, no. 1, p. 14, 1958.
  48. B. McEvily and M. Tortoriello, “Measuring trust in organisational research: Review and recommendations,” Journal of Trust Research, vol. 1, no. 1, pp. 23–63, 2011.
  49. R. Klimoski and S. Mohammed, “Team Mental Model: Construct or Metaphor?” Journal of Management, vol. 20, no. 2, pp. 403–437, 2016.
  50. D. Utami, T. W. Bickmore, and L. J. Kruger, “A robotic couples counselor for promoting positive communication,” in 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2017).   IEEE, 2017, pp. 248–255.
  51. R. Stoican, A. Cangelosi, C. Goerick, and T. H. Weisswange, “Learning Human-Robot Interactions to improve Human-Human Collaboration,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) - Workshop on Human Theory of Machines and Machine Theory of Mind for Human-Agent Teams (TOM4HAT), 2022.
  52. A. Rosatelli, E. Gedik, and H. Hung, “Detecting F-formations & Roles in Crowded Social Scenes with Wearables: Combining Proxemics & Dynamics using LSTMs,” in 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW 2019).   IEEE, 2019, pp. 147–153.
  53. B. Lickel, D. L. Hamilton, A. Lewis, S. J. Sherman, G. Wieczorkowska, and A. N. Uhles, “Varieties of groups and the perception of group entitativity,” Journal of Personality and Social Psychology, vol. 78, no. 2, pp. 223–245, 2000.
  54. Y. Matsuyama, I. Akiba, S. Fujie, and T. Kobayashi, “Four-participant group conversation: A facilitation robot controlling engagement density as the fourth participant,” Computer Speech & Language, vol. 33, no. 1, pp. 1–24, sep 2015.
  55. B. Mutlu, T. Shiwa, T. Kanda, H. Ishiguro, and N. Hagita, “Footing in human-robot conversations: how robots might shape participant roles using gaze cues,” in 4th ACM/IEEE International Conference on Human Robot Interaction (HRI 2009).   ACM, 2009, p. 61.
  56. S. Gillet, W. van den Bos, and I. Leite, “A social robot mediator to foster collaboration and inclusion among children,” in Robotics: Science and Systems XVI (RSS 2020).   MIT Press, 2020.
  57. I. Neto, F. Correia, F. Rocha, P. Piedade, A. Paiva, and H. Nicolau, “The robot made us hear each other: Fostering inclusive conversations among mixed-visual ability children,” in 18th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2023), 2023, pp. 13–23.
  58. M. L. Traeger, S. S. Sebo, M. Jung, B. Scassellati, and N. A. Christakis, “Vulnerable robots positively shape human conversational dynamics in a human–robot team,” Proceedings of the National Academy of Sciences, vol. 117, no. 12, pp. 6370–6375, 2020.
  59. S. Strohkorb Sebo, L. L. Dong, N. Chang, and B. Scassellati, “Strategies for the inclusion of human members within human-robot teams,” in 15th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2020).   IEEE Computer Society, 2020, pp. 309–317.
  60. L. Terveen and D. W. McDonald, “Social matching: A framework and research agenda,” ACM Transactions on Computer-Human Interaction (TOCHI), vol. 12, no. 3, pp. 401–434, 2005.
  61. N. Einecke and T. H. Weisswange, “Detecting Availability to Facilitate Social Communication,” in 39th IEEE Conference on Robotics and Automation (ICRA 2022): Workshop on Exploring the Roles of Robots for Embodied Mediation, 2022.
  62. M. Terry, E. D. Mynatt, K. Ryall, and D. Leigh, “Social net: using patterns of physical proximity over time to infer shared interests,” in SIGCHI Conference on Human Factors in Computing Systems (CHI 2002): Extended Abstracts.   ACM, 2002, pp. 816–817.
  63. B. Xu, T. C. W. Yuan, S. R. Fussell, and D. Cosley, “SoBot: Facilitating conversation using social media data and a social agent,” in 17th ACM Conference on Computer Supported Cooperative Work (CSCW 2014): Companion.   ACM, 2014, pp. 41–44.
  64. D. Shin, S. Yoon, S. Kim, and J. Lee, “BlahBlahBot: Facilitating Conversation between Strangers using a Chatbot with ML-infused Personalized Topic Suggestion,” in ACM Conference on Human Factors in Computing Systems (CHI 2021): Extended Abstracts.   ACM, 2021, p. 409.
  65. A. W. Zhang, T.-H. Lin, X. Zhao, and S. Sebo, “Ice-Breaking Technology: Robots and Computers Can Foster Meaningful Connections between Strangers through In-Person Conversations,” in ACM Conference on Human Factors in Computing Systems (CHI 2023).   ACM, 2023.
  66. T. Ono, M. Imai, and T. Etani, “Robot-Mediated Communications: Robots promoting matchmaking between humans,” in 8th IEEE International Workshop on Robot and Human Interaction (RO-MAN 1999).   IEEE, 1999, pp. 237–241.
  67. R. M. Emerson, “Power-dependence relations,” American Sociological Review, pp. 31–41, 1962.
  68. J. R. Larson Jr, P. G. Foster-Fishman, and T. M. Franz, “Leadership style and the discussion of shared and unshared information in decision-making groups,” Personality and Social Psychology Bulletin, vol. 24, no. 5, pp. 482–495, 1998.
  69. S. Escalera, O. Pujol, P. Radeva, J. Vitria, and M. T. Anguera, “Automatic detection of dominance and expected interest,” EURASIP Journal on Advances in Signal Processing, vol. 2010, pp. 1–12, 2010.
  70. D. B. Jayagopi, H. Hung, C. Yeo, and D. Gatica-Perez, “Modeling dominance in group conversations using nonverbal activity cues,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 3, pp. 501–513, 2009.
  71. N. E. Dunbar and J. K. Burgoon, “Perceptions of power and interactional dominance in interpersonal relationships,” Journal of Social and Personal Relationships, vol. 22, no. 2, pp. 207–233, 2005.
  72. G. P. Huber and K. Lewis, “Cross-Understanding: Implications for Group Cognition and Performance,” Academy of Management Review, vol. 35, no. 1, pp. 6–26, 2017.
  73. G. Skantze, “Predicting and regulating participation equality in human-robot conversations: Effects of age and gender,” in 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2017).   IEEE, 2017, pp. 196–204.
  74. S. Gillet, R. Cumbal, A. Pereira, J. Lopes, O. Engwall, and I. Leite, “Robot gaze can mediate participation imbalance in groups with different skill levels,” in 16th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2021), 2021, pp. 303–311.
  75. M. Dietrich and T. H. Weisswange, “Exploring Privacy Implications for Domestic Robot Mediators,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) - Workshop on Robot Trust for Symbiotic Societies (RTSS 2022), 2022.
  76. J. Welge and M. Hassenzahl, “Better than human: About the psychological superpowers of robots,” in 8th International Conference on Social Robotics (ICSR 2016).   Springer, 2016, pp. 993–1002.
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Authors (7)
  1. Thomas H. Weisswange (7 papers)
  2. Hifza Javed (8 papers)
  3. Manuel Dietrich (3 papers)
  4. Tuan Vu Pham (1 paper)
  5. Maria Teresa Parreira (13 papers)
  6. Michael Sack (1 paper)
  7. Nawid Jamali (16 papers)
Citations (5)
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