The social robotics market is appealing yet challenging. Though social robots are built few remain on the market for long. Many reasons account for their short lifespan with costs and context-specificity ranking high amount them. In this work, we designed, fabricated, and developed FLEXI, a social robot embodiment kit that enabled unlimited customization, making it applicable for a broad range of use cases. The hardware and software of FLEXI were entirely developed by this research team from scratch. FLEXI includes a rich set of materials and attachment pieces to allow for a diverse range of hardware customizations that ensure the embodiment is appropriate for specific customer/researcher projects. It also includes an open-source end-user programming interface to lower the barrier of robotics access to interdisciplinary teams that populate the field of Human-Robot Interaction. We present an iterative development of this cost-effective kit through the lenses of case studies, conceptual research, and soft deployment of FLEXI in three application scenarios: community-support, mental health, and education. Additionally, we provide in open-access the full list of materials and a tutorial to fabricate FLEXI, making it accessible to any maker space, research lab, or workshop space interested in working with or learning about social robots.

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.

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 fnrther exploration.

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.

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.

Repeated stress during adolescence has been shown to decrease cognitive function while increasing rates of anxiety and depression. Eighty-one percent of teens report stress stemming from their school environment and yet, schools are struggling to manage the increase of teen mental health needs. Virtual-reality (VR) has been shown effective at treating post-traumatic stress disorder, phobias, and pain due to its immersive experience. The following paper presents a participatory, human-centered design process utilizing teens as co-designers to develop a VR interaction intended to reduce stress.

Virtual reality (VR) offers potential as a prototyping tool for human-robot interaction. We explored a way to utilize human-centered design (HCD) methodology to develop a collaborative VR game for understanding teens' perceptions of, and interactions with, social robots. Our paper features three stages of the design process for teen-robot interaction in VR; ideation, prototyping, and the game development. In the ideation stage, we identified three important design principles: collaboration, customization, and robot characterization. In the prototyping stage, we developed a card game, conducted gameplay, and confirmed our design principles. Finally, we developed a low-fidelity VR game and received teens' feedback. This exploratory study highlights the potential of VR, both for collaborative robot design and teen-robot interaction studies.

Design requirements can be gathered through a variety of ways; however, engaging teen audiences in design process can be challenging. We present a novel method for engaging teens in design through a social robot design challenge. Groups of teens participated in the challenge to prototype a social robot that would live in their high school and help address stress, a persistent and pervasive problem for this age group. In this paper, we present our methods and share preliminary findings.

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.