Theatre-based design methods have become recognized as highly effective for robot interaction design. Yet there are many domains in which it would be inappropriate or ineffective for designers to role-play stakeholders, such as when working with vulnerable populations. In such cases, researchers typically engage in participatory methods, so those populations can directly contribute to the design process. We see a key design gap created by this tension: How might community members be effectively involved in theatre-based design methods? In this work, we bring together academics and practitioners across HRI, Theatre, Drama Therapy, and Applied Improvisation to address this challenge, and present Improvisational Participatory Storming (IPS) --- a novel Theatre-based Participatory Design Method that is uniquely well suited for Human-Robot Interaction. In presenting IPS, we make seven key contributions. Specifically, we identify (1) a concrete three-section structure for IPS workshops; (2) a novel reuse of Tabletop Role-Playing Game safety tools to mitigate risks in IPS activities; (3) key design objectives to be met through IPS; (4) context-specific constraints that inform which theatre-based design activities to use to meet those objectives; (5) seven key roles in which participants may participate in IPS activities; (6) key dimensions of IPS activities; and (7) three ways that IPS activities can be sequenced to scaffold participation.

Socially assistive robots can be used as therapeutic technologies to address depression symptoms. Through three sets of workshops with individuals living with depression and clinicians, we developed design guidelines for a personalized therapeutic robot for adults living with depression. Building on the design of Therabot, workshop participants discussed various aspects of the robot's design, sensors, behaviors, and a robot connected mobile phone app. Similarities among participants and workshops included a preference for a soft textured exterior and natural colors and sounds. There were also differences - clinicians wanted the robot to be able to call for aid, while participants with depression differed in their degree of comfort in sharing data collected by the robot with clinicians.

Past research with participants in the United States showed that, in competitive group tasks, they have more positive attitudes and behaviors toward robots on their team over humans in another team. Here we present a study in which two Japanese students and two robots, placed in a randomly assigned group, compete with another student-and-robot team in a digital game. We explored participants’ moral behavior towards the robots, measured through their assignment of loud noise blasts to human and robot participants, and their perceptions of and attitudes towards the robots. We then compared this data to that which was collected within the United States. Results indicated that participants in Japan favored their ingroup humans and robots over outgroup agents and differentiated ingroup members more than outgroup members, as within the US. Japanese participants also anthropomorphized robots more than US participants and treated them more positively than US participants.

Using socially assistive robots (SARs) as specialized companions for those living with depression to manage symptoms provides a unique opportunity for exploration of robotic systems as comfort objects. Moreover, the robotic components allow for specialized behavioral responses to particular stimuli, as preferred by the user. We have conducted semi-structured interviews with 10 participants about the zoomorphic robot’s Therabot™ desired behaviors and focus groups with five additional participants regarding the preferred sensors within the Therabot™ system. In this paper, using the data from interviews and focus groups, we explore SAR input and output for depression management. While participants overall expected the robot to respond in much similar ways as a well-trained service animal, they expressed interest in the robot understanding unique information about the environment and the user, such as when the user might need interaction.

Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer’s, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people’s daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments? To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (United States and South Korea) with SARs deployed in each user’s home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and SAR. Models built on that data were capable of achieving nearly 84% accuracy for detecting specific interaction modalities (AUC 0.885) when trained/tested on the same location, though suffered significant performance drops when applied to a different location. Further analysis and participant interviews showed that was likely due to differences in home living environments in the US and Korea. The results suggest that our ability to create adaptable behaviors for robotic pets may be dependent on the human-robot interaction (HRI) data available for modeling.

Social robot co-design requires aiding users as they imagine these novel devices within their everyday lives and enabling designers to understand and address users’ experiences. This paper presents the exploratory development and evaluation of a role-playing game aimed at identifying the desired features and uses of a social robot that can assist people diagnosed with depression. Participants (n = 16) played the game as a character with depression, designed a companion robot for that character, and chose reactions to daily challenges. Though participants initially selected robot capabilities based on their own needs, after the game they identified alternative designs that would better address daily challenges faced by individuals with depression. We discuss aspects of the game that allowed participants to understand how various robot characteristics can address the experience of depression and suggest how role-playing games can support users and designers in identifying beneficial features and uses of emerging robotic technologies.