Recent research is emerging in the field of Social Robotics where robots have the potential to serve as tools to improve human well-being. However, research exploring the expectations and perceptions of prospective users of such robots, and the professionals who currently deliver these interventions, is limited. In this paper, we present qualitative analysis of discussions with prospective users and experienced coaches regarding the design of robot well-being coaches. We invited participants interested in well-being practices to take-part in a Participatory Design (PD) study, consisting of individual interviews and a focus group discussion (NP= 8). Discussions focused on ideating how a robot could function as a mental well-being coach, based on their experiences with well-being practices. Data triangulation was employed by interviewing three professional coaches as additional sources of information. This resulted in a rich set of data, which we transcribed and analysed using Thematic Analysis (TA). The developed themes regarding robot features, form, behaviours, robot-led well-being practices, and the advantages and disadvantages these could provide, were compiled and are discussed in detail. We present this data together with tabulated quotes from the participants and coaches, to pave the way towards designing robot coaches that can provide supportive interventions to improve the mental health and well-being of their users.

Social robots are becoming incorporated in daily human lives, assisting in the promotion of the physical and mental wellbeing of individuals. To investigate the design and use of social robots for delivering mindfulness training, we develop a teleoperation framework that enables an experienced Human Coach (HC) to conduct mindfulness training sessions virtually, by replicating their upper-body and head movements onto the Pepper robot, in real-time. Pepper’s vision is mapped onto a Head-Mounted Display (HMD) worn by the HC and a bidirectional audio pipeline is set up, enabling the HC to communicate with the participants through the robot. To evaluate the participants’ perceptions of the teleoperated Robot Coach (RC), we study the interactions between a group of participants and the RC over 5 weeks and compare these with another group of participants interacting directly with the HC. Growth modelling analysis of this longitudinal data shows that the HC ratings are consistently greater than 4 (on a scale of 1 5) for all aspects while an increase is witnessed in the RC ratings over the weeks, for the Robot Motion and Conversation dimensions. Mindfulness training delivered by both types of coaching evokes positive responses from the participants across all the sessions, with the HC rated significantly higher than the RC on Animacy, Likeability and Perceived Intelligence. Participants’ personality traits such as Conscientiousness and Neuroticism are found to influence their perception of the RC. These findings enable an understanding of the differences between the perceptions of HC and RC delivering mindfulness training, and provide insights towards the development of robot coaches for improving the psychological wellbeing of individuals.

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots - inferred capability and intention - and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.