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.

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.

While Wizard-of-Oz (WOz) techniques are frequently used to supplement a machine’s abilities, extending this approach to human entities can increase experimental control in studies comparing evaluations of humans and machines in the same role. This article describes the design, implementation, and use of a WOz system for facilitating controlled verbal interactions between children and robot or human interviewers. A collaborative interface allows multiple remote wizards to combine participant responses with interaction-specific goals in order to direct a robot or human interviewer’s behavior in a consistent manner. While robot interviewers are controlled directly, human interviewers receive direction through a tablet device or via a projection system concealed from participants. In addition to the system’s technical design, we describe the division of responsibilities between wizards and insights from using the system across three extensive interview studies to facilitate a total of 217 interactions with children.

After playing a five to seven minute character guessing game with a Nao robot, children answered questions about their perceptions of the robot's abilities. Responses from interactions with 30 children, ages eight to twelve, showed that when the robot made an attempt at guessing the participant's character, rather than being stumped and unable to guess, the robot was more likely to be perceived as being able to understand the participant's feelings and able to provide advice. Regardless of their game experience, boys were more likely than girls to feel they could have discussions with the robot about things they could not talk to other people about. This article provides details associated with the implementation of a game used to guess a character the children selected; a twelve question verbally-administered survey that examined their perceptions of the robot; quantitative and qualitative results from the study; and a discussion of the implications, limitations, and future directions of this research.

Intelligent tutoring systems, electronic fitness bands, and assistive robots all contribute toward increasing the wellness of humans by providing different forms of support. As a class of machines focused on increasing the physical and psychological well-being of users emerges, new design considerations and questions arise. We are exploring the potential of prosocial machines via social robotic systems focused on enhancing a user's psychological well-being through positive interventions. This article briefly summarizes our initial approach to understanding prosocial machines via a positive psychology based intervention focused on comparing the ability of different systems to induce measurable increases in a human's level of hopefulness.

This article describes the results of a study that compares disclosure occurrences of bullying from children (ages 8 to 12) to either a human or a social robot. Results from an orally administered questionnaire to 60 children, split evenly between human and robotic interviewers, revealed that few significant differences in reporting were encountered between interviewer types. Overall 9 of 60 (15%) of participants reported being bullied in the past month. Participants were significantly more likely to report that fellow students were teased about their looks to the robot interviewer in comparison to the human interviewer. In addition to the examination of these results, a discussion of lessons learned for future studies of this nature are provided.

Sky Writer is a collaborative communication medium that augments the traditional display of a UAV pilot and allows other stakeholders to communicate their needs and intentions to the pilot. UAV pilots engaging in time-critical missions, such as urban disaster responses, often must allocate most of their cognitive capacity towards flight tasks, making communication and collaboration with other stakeholders difficult or dangerous. Sky Writer addresses the needs of stakeholders while requiring minimal cognitive effort from the UAV pilot. The application presents stakeholders with an interface that provides contextual flight information and a live video stream of the flight. Stakeholders are able to sketch directly on the video stream or use a spotlight indicator that is mirrored across all displays in the system, including the pilot's display. The application can be used in any modern web browser and works with traditional and touch devices. Concept experimentation performed at Disaster City with two pilots indicated that the spotlight feature was particularly useful while the UAV was in motion, and the sketching features were most useful while the UAV was stationary. The system will be tested with professional responders soon to determine its efficacy in a simulated response, and to inform the ongoing design process.

This paper introduces and empirically evaluates two scaling functions to alter a robot's physical movements based on proximity to a human. Previous research has focused on individual aspects of proxemics, like the appropriate distance to maintain from a human, but has not explored autonomous methods to adapt robot behavior as proximity changes. This paper proposes that robots in a social role should modify their behavior using a continuous function mapped to proximity. The method developed calculates a gain value from proximity readings, which is used to shape the execution of active behaviors on the robot. In order to identify the effects of different mappings from proximity to gain value, two different scaling functions were implemented on an affective search and rescue robot. The findings from a 72 participant study, in a high-fidelity mock disaster site, are examined with attention given to a new measure to determine proxemic awareness. The results indicated that for attributes of intelligence, likability, proxemic awareness, and submissiveness, a logarithmic-based scaling function is preferred over a linear-based scaling function, and over no scaling function. In areas of participant comfort and participant stress, the results indicated both logarithmic and linear scaling functions were preferred to no scaling.

Humans regulate their social behavior based on proximity to other social actors. Likewise, when a robot fulfills the role of a social actor it too should regulate its interaction based on proximity. This paper describes work in progress to establish methods for autonomous modification of social behavior based on proximity and to quantify human preferences between methods of scaling a robot's social behaviors based on distance from a human. The preliminary results of a 72 participant human study examine the reaction to scaling with linear methods and perception-based methods. Results indicate significantly higher ratings in multiple areas (comfort, natural movement, safety, self-control, intelligence, likability, submissiveness (p<.05) when using a perception-based scaling function, as opposed to a linear or no scaling function. Work in progress is analyzing the biometric measures collected.

This video describes the Survivor Buddy social medium robot.

This paper describes an open source speech translator toolkit created as part of the "Survivor Buddy" project which allows written or spoken word from multiple independent controllers to be translated into either a single synthetic voice, synthetic voices for each controller, or unchanged natural voice of each controller. The human controllers can work over the internet or be physically co-located with the Survivor Buddy. The toolkit is expected to be of use for exploring voice in general human-robot interaction.

Designing and constructing affective robots on schedule and within costs is especially challenging because of the qualitative, artistic nature of affective expressions. Detailed affective design principles do not exist, forcing an iterative design process. This paper describes a three step design process created for the Survivor Buddy project that engages artists in the design process and allows animation to guide physical implementation. The process combines creative design of believable agents unconstrained by costs with traditional design decision matrices. The paper provides a case study comparing the resulting design of the Survivor Buddy 2.0 robot with the original (Survivor Buddy 1.0). The multi-disciplinary methodology produced a more pleasing and expressive robot that was 50% less expensive, 78% lighter, and up to 700% faster within the same amount of design time. This methodology is expected to contribute to reducing risk in designing cost effective affective robots and robots in general.

This paper describes the Survivor Buddy human-robot interaction project and how it was used by four middle-school girls to illustrate the scientific process for an episode of "SciGirls", a Public Broadcast System science reality show. Survivor Buddy is a four degree of freedom robot head, with the face being a MIMO 740 multi-media touch screen monitor. It is being used to explore consistency and trust in the use of robots as social mediums, where robots serve as intermediaries between dependents (e.g., trapped survivors) and the outside world (doctors, rescuers, family members). While the SciGirl experimentation was neither statistically significant nor rigorously controlled, the experience makes three contributions. It introduces the Survivor Buddy project and social medium role, it illustrates that human-robot interaction is an appealing way to make robotics more accessible to the general public, and raises interesting questions about the existence of a minimum set of degrees of freedom for sufficient expressiveness, the relative importance of voice versus non-verbal affect, and the range and intensity of robot motions.