Previous work with non-social human-robot interaction has found no links between trust and reliance [1]. The current study tested the question: Can social interactions moderate trust-reliance relationship? Human-robot interactions may share similar characteristics to social and emotional interactions between humans. We investigated how social and emotional human-robot interactions moderate the trust-reliance relationship and impacts perceived stress coping abilities. In the experimental condition, social and emotional interactions were used to guide the dialogue between a participant and a virtual robot in order to promote team building. In the matched control condition, the interactions were information-focused, without social or emotional interaction. We show that social interaction moderated the effect of trust on reliance such that higher trust led to greater reliance on the robot. The experimental condition also had higher perceived stress coping abilities. These findings contribute to the existing literature and suggest that creating deeper social and emotional interactions with a robot teammate can facilitate human-robot partnership.

As research on robotic tutors increases, it becomes more relevant to understand whether and how robots will be able to keep students engaged over time. In this paper, we propose an algorithm to monitor engagement in small groups of children and trigger disengagement repair interventions when necessary. We implemented this algorithm in a scenario where two robot actors play out interactive narratives around emotional words and conducted a field study where 72 children interacted with the robots three times in one of the following conditions: control (no disengagement repair), targeted (interventions addressing the child with the highest disengagement level) and general (interventions addressing the whole group). Surprisingly, children in the control condition had higher narrative recall than in the two experimental conditions, but no significant differences were found in the emotional interpretation of the narratives. When comparing the two different types of disengagement repair strategies, participants who received targeted interventions had higher story recall and emotional understanding, and their valence after disengagement repair interventions increased over time.

Physiological sensors can be instrumental in facilitating complex behavioral and social interactions between humans and robots. However, there is a limited understanding of the psychological implications of incorporating such sensors in the context of human-robot interaction. We show that perception of sensors' role can act as an individual difference factor that can implicitly influence trust and reliance on a robot. We argue that some people may believe in the ability of physiological sensors to facilitate mentalization (or understanding) of humans. We refer to this tendency as mentalizing propensity. Such implicit differences in mentalizing propensity may influence participants' trust and reliance on a robot, as found in the current study. We also found that trust can mediate the influence of mentalizing propensity on reliance. Furthermore, we show that social interactions with a robot influenced participants' mentalizing propensity. The current work shows that implicit understanding of sensors' role in human-robot interaction context is an important attribute to assess in future studies on trust and reliance.

Robot assistive technology is becoming increasingly prevalent. Despite the growing body of research in this area, the role of type of interaction (i.e., small groups versus individual interactions) on effectiveness of interventions is still unclear. In this paper, we explore a new direction for socially assistive robotics, where multiple robotic characters interact with children in an interactive storytelling scenario. We conducted a between-subjects repeated interaction study where a single child or a group of three children interacted with the robots in an interactive narrative scenario. Results show that although the individual condition increased participant's story recall abilities compared to the group condition, the emotional interpretation of the story content seemed more dependent on the difficulty level rather than the study condition. Our findings suggest that, despite the type of interaction, interactive narratives with multiple robots are a promising approach to foster children's development of social-related skills.

Changes in type of interaction (e.g., individual vs. group interactions) can potentially impact data-driven models developed for social robots. In this paper, we provide a first investigation in the effects of changing group size in data-driven models for HRI, by analyzing how a model trained on data collected from participants interacting individually performs in test data collected from group interactions, and vice-versa. Another model combining data from both individual and group interactions is also investigated. We perform these experiments in the context of predicting disengagement behaviors in children interacting with two social robots. Our results show that a model trained with group data generalizes better to individual participants than the other way around. The mixed model seems a good compromise, but it does not achieve the performance levels of the models trained for a specific type of interaction.