The field of human-human-robot interaction (HHRI) uses social robots to positively influence how humans interact with each other. This objective requires models of human understanding that consider multiple humans in an interaction as a collective entity and represent the group dynamics that exist within it. Understanding group dynamics is important because these can influence the behaviors, attitudes, and opinions of each individual within the group, as well as the group as a whole. Such an understanding is also useful when personalizing an interaction between a robot and the humans in its environment, where a group-level model can facilitate the design of robot behaviors that are tailored to a given group, the dynamics that exist within it, and the specific needs and preferences of the individual interactants. In this paper, we highlight the need for group-level models of human understanding in human-human-robot interaction research and how these can be useful in developing personalization techniques. We survey existing models of group dynamics and categorize them into models of social dominance, affect, social cohesion, and conflict resolution. We highlight the important features these models utilize, evaluate their potential to capture interpersonal aspects of a social interaction, and highlight their value for personalization techniques. Finally, we identify directions for future work, and make a case for models of relational affect as an approach that can better capture group-level understanding of human-human interactions and be useful in personalizing human-human-robot interactions.

翻译：人-人-机器人交互（HHRI）领域利用社交机器人积极影响人类之间的互动方式。这一目标要求建立的人类理解模型需将交互中的多个个体视为集体实体，并表征其中存在的群体动态。理解群体动态至关重要，因为它能够影响群体内每个个体的行为、态度和观点，以及群体整体。这种理解在个性化机器人与其环境中的人类之间的交互时同样有用——群体层面的模型有助于设计针对特定群体、群体内部动态以及个体参与者具体需求和偏好的机器人行为。本文强调了在人-人-机器人交互研究中建立人类理解群体层面模型的必要性，并阐明这些模型如何有助于开发个性化技术。我们综述了现有群体动态模型，将其归类为社会支配模型、情感模型、社会凝聚力模型和冲突解决模型。我们重点分析了这些模型采用的关键特征，评估了它们捕捉社交互动中人际关系的潜力，并强调了它们在个性化技术中的价值。最后，我们指出了未来研究方向，并论证了关系情感模型作为一种能够更好地捕捉人-人交互群体层面理解、并有助于实现人-人-机器人交互个性化的方法。