Social Robot Scenarios for Real-World Child and Family Care Settings through Participatory Design

This paper discusses a 5-year PhD project, focused upon the implementation of social robots for general child and family care settings in the Netherlands. The project is a collaboration with general Dutch family care organisations as well as specialized child mental health care organisations. The project adapts a bottom-up, participatory design approach, where end users are included in all stages of the project. End users consist of children, parents, and family care professionals, who all have different needs, regarding the social robot behaviors as well as the participatory design methods. This paper provides suggestions to deal with these differences in designing social robots for child mental support in real-world settings

Symbiotic Child Emotional Support with Social Robots and Temporal Knowledge Graphs

In current youth-care programs, children with needs (mental health, family issues, learning disabilities, and autism) receive support from youth and family experts as one-to-one assistance at schools or hospitals. Occasionally, social robots have featured in such settings as support roles in a one-to-one interaction with the child. In this paper, we suggest the development of a symbiotic framework for real-time Emotional Support (ES) with social robots Knowledge Graphs (KG). By augmenting a domain-specific corpus from the literature on ES for children (between the age of 8 and 12) and providing scenario-driven context including the history of events, we suggest developing an experimental knowledge-aware ES framework. The framework both guides the social robot in providing ES statements to the child and assists the expert in tracking and interpreting the child's emotional state and related events over time

Co-designing a social robot for child health care

Social robots provide new opportunities to support general child healthcare programs. However, it is still unclear how social robots could be used in this context and how corresponding behaviours should be designed. To ensure satisfying implementations of such new technological solutions, it is essential to include the end-users in the designing process. We have conducted a co-design study at two primary schools based on three complementary, creative methods: Draw-write-and-tell and/or story-writing-and-telling, Theatre play, and Robot avatar programming. A total of 46 children aged 7–12 years old participated in four robot co-design workshops. The drawings, stories and theatre plays were analysed, resulting in evaluations of 10 scenarios as well as 21 new scenarios and 7 main user requirements for social robots providing mental support in general child healthcare. Evaluation of the activities highlight their stimulation of out-of-the-box thinking and the development of creative solutions (i.e. drawings/stories/theatre plays resulted in robot designs, scenarios and requirements), while children's reflections show them being enjoyable for participation. The inputs gathered during these co-design workshops will greatly influence future work on the design and application of social robots in the child healthcare domain

Lessons Learned About Designing and Conducting Studies From HRI Experts

The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees’ feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants’ responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot’s limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research

Lessons Learned About Designing and Conducting Studies From HRI Experts.

The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees' feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants' responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot's limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research

Child's Bonding and Self-Disclosing with a Robot in Family Care

This extended research abstract for the Doctoral Consortium at IDC 2021 describes a 5-year PhD project, started November 2019, on self-disclosure in child-robot interaction in the field of child and family care. The research design embraces a bottom-up participatory design approach including all stakeholders, based on qualitative as well as quantitative methods. This PhD research is guided by Dr. M.M.A. de Graaf and Prof. dr. ir. J.F.M. Masthoff

Child's Bonding and Self-Disclosing with a Robot in Family Care

This extended research abstract for the Doctoral Consortium at IDC 2021 describes a 5-year PhD project, started November 2019, on self-disclosure in child-robot interaction in the field of child and family care. The research design embraces a bottom-up participatory design approach including all stakeholders, based on qualitative as well as quantitative methods. This PhD research is guided by Dr. M.M.A. de Graaf and Prof. dr. ir. J.F.M. Masthoff

Self-Disclosure to a Robot "In-the-Wild": Category, Human Personality and Robot Identity

Self-disclosures can be valuable and sensitive parts of the human-robot interaction. This paper investigates how far human's tendency to self-disclose depends on the topic of interaction, individual's personality and perceived robot identity (i.e., human-, robot- or animal-like). Robot's (Pepper) identity was shown in its self-disclosure, interaction behaviors (gestures, sound and voice), and ’’clothing". In an"in-the- wild" study at a science festival, 80 visitors interacted with one of these robot identities. When questioned by the robot, they disclosed more about their attitudes and opinions than about other categories. Significant correlations appeared between personality characteristics and the degree of self-disclosure, as well as differences in self-disclosure categories. The different robot identities showed no effects on disclosures

Self-Disclosure to a Robot "In-the-Wild": Category, Human Personality and Robot Identity

Self-disclosures can be valuable and sensitive parts of the human-robot interaction. This paper investigates how far human's tendency to self-disclose depends on the topic of interaction, individual's personality and perceived robot identity (i.e., human-, robot-or animal-like). Robot's (Pepper) identity was shown in its self-disclosure, interaction behaviors (gestures, sound and voice), and "clothing". In an"in-the-wild"study at a science festival, 80 visitors interacted with one of these robot identities. When questioned by the robot, they disclosed more about their attitudes and opinions than about other categories. Significant correlations appeared between personality characteristics and the degree of self-disclosure, as well as differences in self-disclosure categories. The different robot identities showed no effects on disclosures

Social Robot for Health Check and Entertainment inWaiting Room: Child's Engagement and Parent's Involvement

To provide effective support in child health care, social robots' behaviors should be well-tailored to the care context and situated user needs. This research focuses on a social robot (iPal) in the waiting room for a vaccination. In an experiment, children performed the health check and hereafter, to kill the time, a game, either with the robot or a tablet. Child's behaviors and self-reports were recorded. The children seemed to be more positively engaged when interacting with the robot (higher motivation to play a game, higher interaction volume, more smiling during the health check, more gesture and/or verbal expressive behaviors, less mobile phone distraction). Further, their individual characteristics (like age and personality) and the social context (e.g., parent's presence) affected children's engagement (e.g., higher for young children) and parent's involvement (e.g., higher with the tablet group, resulting in a higher percentage of answered questions during the health check). Here, we identified an interesting trade-off: the current robot supports child engagement (distracting from the stressful vaccination), but hinders the collaboration between parent and child. In future research, we aim to improve the collaboration support of the robot