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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.

HRI is an interdisciplinary field that requires researchers to be knowledgeable in broad areas ranging from social sciences to engineering. Study design is a multifaceted aspect of HRI that is hard to develop and perfect. Thus, the second edition of the 'Workshop Your Study Design' workshop aims to improve the quality of future HRI studies by training researchers and boosting the accessibility of HRI as a field. Participants will have the opportunity to receive guidance and feedback on their study from an expert mentor. Researchers from all avenues of HRI will be invited to submit a 2-4 page paper on an HRI study they are currently designing, including a brief introduction and a complete methods section. Accepted submissions will be discussed in small groups led by mentors with relevant expertise. Prior to the workshop, papers will be shared within each group. Participants will be encouraged to read other submissions. During the workshop, attendees will work within their menteementor groups to discuss each paper and provide feedback. There will also be a session where mentors lead mini discussions on topics important to study design, such as balancing qualitative and quantitative design, power analysis, and research ethics. The workshop will end with a session where all participants can share important lessons that they learned with fellow attendees.

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.

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.

Robots will increasingly collaborate with human partners necessitating research into how robots negotiate negative collaborative outcomes. This study investigates the effect of blame attribution on trust assessments in human-robot collaboration. Participants (n = 60) collaboratively played a game with a humanoid robot in one of four conditions in a 2 (blame correctness: correct vs. incorrect) by 2 (blame target: human vs. robot) between-subjects experiment. Results show that people evaluate a robot more positively when it blames itself for collaborative failures, especially, it seems, in the case of incorrect self-blame. Our findings indicate a need to further research on effective communication strategies for robots that need to negotiate collaborative failures without compromising the trust relationships with its human partner.

Child and family care professionals in the Netherlands are facing challenges including high workloads. Technological support could be beneficial in this context, e.g. for education, motivation and guidance of the children. For example, the Dutch Child and Family Center explores the possibilities of social robot assistance in their regular care pathways. To study the use of social robots in this broad context, we started by drafting three example scenarios based on the expertise of child care professionals. During an exploration phase, we are identifying key design and application requirements through focus groups with child care professionals and parents. Later stages of our research, the testing phase, will focus on testing these requirements via scenario-based design and child-robot interaction experiments in real-world contexts to further shape the application of social robots in various child and family care settings.