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We present FLEX-SDK: an open-source software development kit that allows creating a social robot from two simple tablet screens. FLEX-SDK involves tools for designing the robot face and its facial expressions, creating screens for input/output interactions, controlling the robot through a Wizard-of-Oz interface, and scripting autonomous interactions through a simple text-based programming interface. We demonstrate how this system can be used to replicate an interaction study and we present nine case studies involving controlled experiments, observational studies, participatory design sessions, and outreach activities in which our tools were used by researchers and participants to create and interact with social robots. We discuss common observations and lessons learned from these case studies. Our work demonstrates the potential of FLEX-SDK to lower the barrier to entry for Human-Robot Interaction research. 

Adolescents isolated at home during the COVID19 pandemic lockdown are more likely to feel lonely and in need of social connection. Social robots may provide a much needed social interaction without the risk of contracting an infection. In this paper, we detail our co-design process used to engage adolescents in the design of a social robot prototype intended to broadly support their mental health. Data gathered from our four week design study of nine remote sessions and interviews with 16 adolescents suggested the following design requirements for a home robot: (1) be able to enact a set of roles including a coach, companion, and confidant; (2) amplify human-to-human connection by supporting peer relationships; (3) account for data privacy and device ownership. Design materials are available in open-access, contributing to best practices for the field of Human-Robot Interaction. 

Social robots may be a promising social-emotional tool to support adolescent mental health. However, how might interactions with a social robot in a school setting be perceived by teens? From previous studies, we gathered qualitative data suggesting a design tension between teens wanting both public and private interactions with our social robot, EMAR. In our current study, we explored interactions between a social robot and a small group of adolescents in a semi-private, school library setting. We found: (1) Some teens preferred to have a friend present while they engaged with the social robot, (2) Teens found comfort in being physically visible, but audibly private during interactions, and finally (3) Strangers in the school environment were not disruptive of the teens' robot interactions, but unexpectedly friends were. After presenting these findings, we briefly discuss how these qualitative data can be situated and our next steps for further exploration. 

Social robots have been used to support mental health. In this work, we explored their potential as community-based tools. Visualizing mood data patterns of a community with a social robot might help the community raise awareness about the emotions people feel and affecting factors from life events. This could potentially lead to adaptation of suitable coping skills enhancing the sense of belonging and support among community members. We present preliminary findings and ongoing plans for this human-robot interaction (HRI) research work on data visualizations supporting community mental health. In a two-day study, twelve participants recruited from a university community engaged with a robot displaying mood data. Given the feedback from the study, we improved the data visualization in the robot to increase accessibility, universality, and usefulness of such visualizations. In the future, we plan on conducting studies with this improved version and deploying a social robot for a community setting. 

Understanding people's attitudes towards robots and how those attitudes are affected by exposure to robots is essential to the effective design and development of social robots. Although researchers have been studying attitudes towards robots among adults and even children for more than a decade, little has been explored assessing attitudes among teens-a highly vulnerable population that presents unique opportunities and challenges for social robots. Our work aims to close this gap. In this paper we present findings from several participatory robot interaction and design sessions with 136 teenagers who completed a modified version of the Negative Attitudes Towards Robots Scale (NARS) before participation in a robot interaction. Our data reveal that most teens are 1) highly optimistic about the helpfulness of robots, 2) do not feel nervous talking with a robot, but also 3) do not trust a robot with their data. Ninety teens also completed a post-interaction survey and reported a significant change in the motional attitudes subscale of the NARS. We discuss the implications of our findings on the design of social robots for teens.