Social Dynamics in Human-Robot Groups – Possible Consequences of Unequal Adaptation to Group Members Through Machine Learning in Human-Robot Groups

Pütten, Astrid M. Rosenthal‐von der; Abrams, Anna M. H.

doi:10.1007/978-3-030-50334-5_27

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…Other MHRI work concurs, suggesting a robot's role in a team is twofold: managing interaction resources and optimizing group performance [71,72]. A robot behavior policy solely optimizing team performance may inadvertently foster intergroup bias by favoring high performers [63]. These prior works altogether indicate that a robot behavior policy that maximizes the group performance at the cost of discriminating low performers or unequally distributing resources would not likely help the group achieve the optimal experience.…”

Section: Cognitive Sub-objectivesmentioning

confidence: 89%

“…When designing a robot's behavior adaptation policy, taking into account both the group's social dynamics and the social consequences of the robot's resource distribution has been advocated by prior MHRI work [63]. Other MHRI work concurs, suggesting a robot's role in a team is twofold: managing interaction resources and optimizing group performance [71,72].…”

Section: Cognitive Sub-objectivesmentioning

confidence: 99%

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Integrating Flow Theory and Adaptive Robot Roles: A Conceptual Model of Dynamic Robot Role Adaptation for the Enhanced Flow Experience in Long-term Multi-person Human-Robot Interactions

Chen,

Alghowinem,

Breazeal

et al. 2024

Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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In this paper, we introduce a novel conceptual model for a robot's behavioral adaptation in its long-term interaction with humans, integrating dynamic robot role adaptation with principles of ow experience from psychology. This conceptualization introduces a hierarchical interaction objective grounded in the ow experience, serving as the overarching adaptation goal for the robot. This objective intertwines both cognitive and aective sub-objectives and incorporates individual and group-level human factors. The dynamic role adaptation approach is a cornerstone of our model, highlighting the robot's ability to uidly adapt its support roles-from leader to follower-with the aim of maintaining equilibrium between activity challenge and user skill, thereby fostering the user's optimal ow experiences. Moreover, this work delves into a comprehensive exploration of the limitations and potential applications of our proposed conceptualization. Our model places a particular emphasis on the multi-person HRI paradigm, a dimension of HRI that is both under-explored and challenging. In doing so, we aspire to extend the applicability and relevance of our conceptualization within the HRI eld, contributing to the future development of adaptive social robots capable of sustaining long-term interactions with humans.

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Section: Cognitive Sub-objectivesmentioning

confidence: 89%

Section: Cognitive Sub-objectivesmentioning

confidence: 99%

Integrating Flow Theory and Adaptive Robot Roles: A Conceptual Model of Dynamic Robot Role Adaptation for the Enhanced Flow Experience in Long-term Multi-person Human-Robot Interactions

Chen,

Alghowinem,

Breazeal

et al. 2024

Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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An adaptive decision-making system supported on user preference predictions for human–robot interactive communication

Maroto-Gómez

Castro‐González

Castillo

et al. 2022

User Model User-Adap Inter

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Adapting to dynamic environments is essential for artificial agents, especially those aiming to communicate with people interactively. In this context, a social robot that adapts its behaviour to different users and proactively suggests their favourite activities may produce a more successful interaction. In this work, we describe how the autonomous decision-making system embedded in our social robot Mini can produce a personalised interactive communication experience by considering the preferences of the user the robot interacts with. We compared the performance of Top Label as Class and Ranking by Pairwise Comparison, two promising algorithms in the area, to find the one that best predicts the user preferences. Although both algorithms provide robust results in preference prediction, we decided to integrate Ranking by Pairwise Comparison since it provides better estimations. The method proposed in this contribution allows the autonomous decision-making system of the robot to work on different modes, balancing activity exploration with the selection of the favourite entertaining activities. The operation of the preference learning system is shown in three real case studies where the decision-making system works differently depending on the user the robot is facing. Then, we conducted a human–robot interaction experiment to investigate whether the robot users perceive the personalised selection of activities more appropriate than selecting the activities at random. The results show how the study participants found the personalised activity selection more appropriate, improving their likeability towards the robot and how intelligent they perceive the system. query Please check the edit made in the article title.

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Interaction-Shaping Robotics: Robots That Influence Interactions between Other Agents

Gillet,

Vázquez,

Andrist

et al. 2024

J. Hum.-Robot Interact.

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Work in Human-Robot Interaction (HRI) has investigated interactions between one human and one robot as well as human-robot group interactions. Yet, the field lacks a clear definition and understanding of the influence a robot can exert on interactions between other group members (e.g., human-to-human). In this paper, we define Interaction-Shaping Robotics (ISR), a subfield of HRI that investigates robots that influence the behaviors and attitudes exchanged between two (or more) other agents. We highlight key factors of Interaction-Shaping Robots that include the role of the robot, the robot-shaping outcome, the form of robot influence, the type of robot communication, and the timeline of the robot’s influence. We also describe three distinct structures of human-robot groups to highlight the potential of ISR in different group compositions and discuss targets for a robot’s interaction-shaping behavior. Finally, we propose areas of opportunity and challenges for future research in ISR.

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Social Dynamics in Human-Robot Groups – Possible Consequences of Unequal Adaptation to Group Members Through Machine Learning in Human-Robot Groups

Cited by 3 publications

References 42 publications

Integrating Flow Theory and Adaptive Robot Roles: A Conceptual Model of Dynamic Robot Role Adaptation for the Enhanced Flow Experience in Long-term Multi-person Human-Robot Interactions

Integrating Flow Theory and Adaptive Robot Roles: A Conceptual Model of Dynamic Robot Role Adaptation for the Enhanced Flow Experience in Long-term Multi-person Human-Robot Interactions

An adaptive decision-making system supported on user preference predictions for human–robot interactive communication

Interaction-Shaping Robotics: Robots That Influence Interactions between Other Agents

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