Social robots in the home bring new privacy risks and concerns for older adults. Yet, current technology privacy mechanisms typically use a one-time and universal consent mechanism (e.g., user agreement checkbox, browser cookie setting, etc.), lacking consideration of how privacy is holistically experienced. Designing for privacy requires a multidimensional approach to support how older adults experience privacy. To investigate older adult-centered privacy mechanisms for social robots, we conducted two participatory design (PD) workshops at local assisted living facilities. Our findings from these workshops suggest that older adults do not treat privacy as static, but as a temporal and situational practice that requires continuous negotiations and revisions. We subsequently conducted a post-PD speculative design (SD) process that extracted three design features for privacy—aware social robots-privacy profiles, real-time privacy feedback, and data ownership tools—that can support older adults’ multidimensional privacy experiences.

We present AgentCoach, an LLM-powered system that provides adaptive feedback for motor skill learning from tutorial videos. The system works by extracting key coaching points (CPs) and compiling CP-specific evaluators that map each cue to measurable kinematic parameters. This process allows AgentCoach to connect high-level semantic meaning with low-level postural estimation for accurate, context-aware evaluation. During practice, learners receive concise visual diagnostics of their mistakes paired with prescriptive verbal feedback that adapts based on their performance history. We technically validate the CP extraction and evaluator compilation across a wide range of common sports and exercise videos. A user study confirms the system’s usability and shows the system’s potential effectiveness of its adaptive feedback across multiple skills.

Recent research in affective robots has recognized their potential in supporting human well-being. Due to rapidly developing affective and artificial intelligence technologies, this field of research has undergone explosive expansion and advancement in recent years. In order to develop a deeper understanding of recent advancements, we present a systematic review of the past 10 years of research in affective robotics for wellbeing. In this review, we identify the domains of well-being that have been studied, the methods used to investigate affective robots for well-being, and how these have evolved over time. We also examine the evolution of the multifaceted research topic from three lenses: technical, design, and ethical. Finally, we discuss future opportunities for research based on the gaps we have identified in our review – proposing pathways to take affective robotics from the past and present to the future. The results of our review are of interest to human-robot interaction and affective computing researchers, as well as clinicians and well-being professionals who may wish to examine and incorporate affective robotics in their practices.

As human-robot interactions become more social, a robot's personality plays an increasingly vital role in shaping user experience and its overall effectiveness. In this study, we examine the impact of three distinct robot personalities on user experiences during well-being exercises: a Baseline Personality that aligns with user expectations, a High Extraversion Personality, and a High Neuroticism Personality. These personalities were manifested through the robot's dialogue, which were generated using a large language model (LLM) guided by key behavioral characteristics from the Big 5 personality traits. In a between-subjects user study (N = 66), where each participant interacted with one distinct robot personality, we found that both the High Extraversion and High Neuroticism Robot Personalities significantly enhanced participants' emotional states (arousal, control, and valence). The High Extraversion Robot Personality was also rated as the most enjoyable to interact with. Additionally, evidence suggested that participants' personality traits moderated the effectiveness of specific robot personalities in eliciting positive outcomes from well-being exercises. Our findings highlight the potential benefits of designing robot personalities that deviate from users' expectations, thereby enriching human-robot interactions.

While the efficacy of social robots in supporting people in learning tasks has been extensively investigated, their potential impact in assisting students in self-studying contexts has not been investigated much. This study explores how a social robot can act as a peer study companion for college students during self-study tasks by delivering task-oriented goal reminder and positive emotional support. We conducted an exploratory Wizard-of-Oz study to explore how these robotic support behaviors impacted students’ perceived focus, productivity, and engagement in comparison to a robot that only provided physical presence (control). Our study results suggest that participants in the goal reminder and the emotional support conditions reported greater ease of use, with the goal reminder condition additionally showing a higher willingness to use the robot in future study sessions. Participants’ satisfaction with the robot was correlated with their perception of the robot as a social other, and this perception was found to be a predictor for their level of goal achievement in the self-study task. These findings highlight the potential of socially assistive robots to support self-study through both functional and emotional engagement.

As social robots get more deeply integrated into our everyday lives, they will be expected to engage in meaningful conversations and exhibit socio-emotionally intelligent listening behaviors when interacting with people. Active listening and backchanneling could be one way to enhance robots’ communicative capabilities and enhance their effectiveness in eliciting deeper self-disclosure, providing a sense of empathy, and forming positive rapport and relationships with people. Thus, we developed an LLM-powered social robot that can exhibit contextually appropriate sentiment-based backchanneling and active listening behaviors (active listening+backchanneling) and compared its efficacy in eliciting people’s self-disclosure in comparison to robots that do not exhibit any of these listening behaviors (control) and a robot that only exhibits backchanneling behavior (backchanneling-only). Through our experimental study with sixty-five participants, we found the participants who conversed with the active listening robot perceived the interactions more positively, in which they exhibited the highest self-disclosures, and reported the strongest sense of being listened to. The results of our study suggest that the implementation of active listening behaviors in social robots has the potential to improve human-robot communication and could further contribute to the building of deeper human-robot relationships and rapport.

The number of older adults who are homebound with depressive symptoms is increasing. Due to their homebound status, they have limited access to trained mental healthcare support, which leaves this support often to untrained family caregivers. To increase access, a growing interest is placed on using technology-mediated solutions, such as voice-assisted intelligent personal assistants (VIPAs), to deliver mental health services to older adults. To better understand how older adults and family caregivers intend to interact with a VIPA for mental health interventions, we conducted a participatory design study during which 6 older adults and 7 caregivers designed VIPA-human dialogues for various scenarios. Using conversation style preferences as a starting point, we present aspects of human-likeness older adults and family caregivers perceived as helpful or uncanny, specifically in the context of the delivery of mental health interventions, which helps inform potential roles VIPAs can play in mental healthcare for older adults.

Social support plays a crucial role in managing and enhancing one's mental health and well-being. In order to explore the role of a robot's companion-like behavior on its therapeutic interventions, we conducted an eight-week-long deployment study with seventy participants to compare the impact of (1) acontrol robot with only assistant-like skills, (2) acoach-like robot with additional instructive positive psychology interventions, and (3) acompanion-like robot that delivered the same interventions in a peer-like and supportive manner. The companion-like robot was shown to be the most effective in building a positive therapeutic alliance with people, enhancing participants' well-being and readiness for change. Our work offers valuable insights into how companion AI agents could further enhance the efficacy of the mental health interventions by strengthening their therapeutic alliance with people for long-term mental health support.

Socially assistive robots can support people in making behavior changes by socially engaging in or moderating certain behaviors, such as physical exercise and snacking. However, there has not been much work on designing social robots that aim to support both social engagement and behavior moderation, i.e., offering social interactions for engaging in behaviors without over-engagement. This work explores how social robots can moderate alcohol consumption while socially engaging them in a solitary drinking context. As alcohol consumption can have benefits when done in moderation, this companion robot aims to guide the user toward moderate drinking by using social engagement (i.e., creating an enjoyable atmosphere) and drinking moderation (i.e., regulating the drinking pace). Our preliminary user study (n=20) reveals that the robot is perceived as a friendly companion, and its human-likeness is partly attributed to the robot's intervention. Most participants followed the robot's guidance and perceived it as an intelligent friend due to its social interactions and behavior tracking features. We discuss the benefit of physical interactions for social engagement, utilizing interaction rituals for enjoyable but moderate commensality, and ethical considerations in solitary drinking contexts.

Despite the increase in awareness and support for mental health, college students' mental health is reported to decline every year in many countries. Several interactive technologies for mental health have been proposed and are aiming to make therapeutic service more accessible, but most of them only provide one-way passive contents for their users, such as psycho-education, health monitoring, and clinical assessment. We present a robotic coach that not only delivers interactive positive psychology interventions but also provides other useful skills to build rapport with college students. Results from our on-campus housing deployment feasibility study showed that the robotic intervention showed significant association with increases in students' psychological well-being, mood, and motivation to change. We further found that students' personality traits were associated with the intervention outcomes as well as their working alliance with the robot and their satisfaction with the interventions. Also, students' working alliance with the robot was shown to be associated with their pre-to-post change in motivation for better well-being. Analyses on students' behavioral cues showed that several verbal and nonverbal behaviors were associated with the change in self-reported intervention outcomes. The qualitative analyses on the post-study interview suggest that the robotic coach's companionship made a positive impression on students, but also revealed areas for improvement in the design of the robotic coach. Results from our feasibility study give insight into how learning users' traits and recognizing behavioral cues can help an AI agent provide personalized intervention experiences for better mental health outcomes.

Self-disclosure is an important part of mental health treatment process. As interactive technologies are becoming more widely available, many AI agents for mental health prompt their users to self-disclose as part of the intervention activities. However, most existing works focus on linguistic features to classify self-disclosure behavior, and do not utilize other multi-modal behavioral cues. We present analyses of people's non-verbal cues (vocal acoustic features, head orientation and body gestures/movements) exhibited during self-disclosure tasks based on the human-robot interaction data collected in our previous work. Results from the classification experiments suggest that prosody, head pose, and body postures can be independently used to detect self-disclosure behavior with high accuracy (up to 81%). Moreover, positive emotions, high engagement, self-soothing and positive attitudes behavioral cues were found to be positively correlated to self-disclosure. Insights from our work can help build a self-disclosure detection model that can be used in real time during multi-modal interactions between humans and AI agents.

Intent recognition models, which match a written or spoken input's class in order to guide an interaction, are an essential part of modern voice user interfaces, chatbots, and social robots. However, getting enough data to train these models can be very expensive and challenging, especially when designing novel applications such as real-world human-robot interactions. In this work, we first investigate how much training data is needed for high performance in an intent classification task. We train and evaluate BiLSTM and BERT models on various subsets of the ATIS and Snips datasets. We find that only 25 training examples per intent are required for our BERT model to achieve 94% intent accuracy compared to 98% with the entire datasets, challenging the belief that large amounts of labeled data are required for high performance in intent recognition. We apply this knowledge to train models for a real-world HRI application, character strength recognition during a positive psychology interaction with a social robot, and evaluate against the Character Strength dataset collected in our previous HRI study. Our real-world HRI application results also confirm that our model can produce 76% intent accuracy with 25 examples per intent compared to 80% with 100 examples. In a real-world scenario, the difference is only one additional error per 25 classifications. Finally, we investigate the limitations of our minimal data models and offer suggestions on developing high quality datasets. We conclude with practical guidelines for training BERT intent recognition models with minimal training data and make our code and evaluation framework available for others to replicate our results and easily develop models for their own applications.

A significant number of college students suffer from mental health issues that impact their physical, social, and occupational outcomes. Various scalable technologies have been proposed in order to mitigate the negative impact of mental health disorders. However, the evaluation for these technologies, if done at all, often reports mixed results on improving users' mental health. We need to better understand the factors that align a user's attributes and needs with technology-based interventions for positive outcomes. In psychotherapy theory, therapeutic alliance and rapport between a therapist and a client is regarded as the basis for therapeutic success. In prior works, social robots have shown the potential to build rapport and a working alliance with users in various settings. In this work, we explore the use of a social robot coach to deliver positive psychology interventions to college students living in on-campus dormitories. We recruited 35 college students to participate in our study and deployed a social robot coach in their room. The robot delivered daily positive psychology sessions among other useful skills like delivering the weather forecast, scheduling reminders, etc. We found a statistically significant improvement in participants' psychological wellbeing, mood, and readiness to change behavior for improved wellbeing after they completed the study. Furthermore, students' personality traits were found to have a significant association with intervention efficacy. Analysis of the post-study interview revealed students' appreciation of the robot's companionship and their concerns for privacy.

Conversational AI agents are proliferating, embodying a range of devices such as smart speakers, smart displays, robots, cars, and more. We can envision a future where a personal conversational agent could migrate across different form factors and environments to always accompany and assist its user to support a far more continuous, personalized and collaborative experience. This opens the question of what properties of a conversational AI agent migrates across forms, and how it would impact user perception. To explore this, we developed a Migratable AI system where a user's information and/or the agent's identity can be preserved as it migrates across form factors to help its user with a task. We validated the system by designing a 2x2 between-subjects study to explore the effects of information migration and identity migration on user perceptions of trust, competence, likeability and social presence. Our results suggest that identity migration had a positive effect on trust, competence and social presence, while information migration had a positive effect on trust, competence and likeability. Overall, users report highest trust, competence, likeability and social presence towards the conversational agent when both identity and information were migrated across embodiments.

Most hospitals make efforts to provide socio-emotional support for patients and their families during care. In order to expand the service provided by certified child life specialists, we created a social robot and a virtual avatar that augment part of the care CCLS offers to patients by engaging pediatric patients in playful interactions and promoting their socio-emotional wellbeing. We ran a randomized controlled trial in a form of a Wizard-of-Oz study at a local pediatric hospital to study how three different interactive media (a plush teddy bear, a virtual agent on a screen, and a social robot) influence the pediatric patient's affect, joyful play, and social interactions with others. Behavioral analyses of verbal utterance transcriptions and children's physical behavior revealed that the social robot is most effective in producing socially energetic conversations as well as increasing positivity and promoting multi-party interactions. The virtual avatar was socially engaging but children tended to attend more exclusively to a virtual avatar and were less responsive to others. The plush toy was least engaging of the three interventions, but children touched it the most. Based on these findings, we recommend use cases for each agent appropriate for individual pediatric patients' health conditions and needs. These analyses of behavioral data suggest the benefit of deploying a physically embodied social robot in pediatric inpatient-care contexts on young patients'; social and emotional wellbeing.

Children and their parents may undergo challenging experiences when admitted for in-patient care at pediatric hospitals. While most pediatric hospitals make an effort to provide socio-emotional support for patients and their families during care, such as with child life services, gaps still exist between professional resource supply and patient demand. There is an opportunity to apply interactive companion-like technologies as a way to augment and extend professional care teams. To explore the opportunity of social robots to augment child life services, we performed a randomized clinical trial at a local pediatric hospital to investigate how three different companion-like interventions (a plush toy, a virtual character on a screen, and a social robot) affected child-patients physical activity and social engagement - both linked to positive patient outcomes. We recorded video of patients, families and a certified child life specialist with each intervention to gather behavioral data. Our results suggest that children are the most physically and verbally engaged when interacting with the physically co-present social robot over time than the other two interventions. A post-study interview with child life specialists reveals their perspective on potential opportunities for social robots (and other companion-like interventions) to assist them with providing education, diversion, and companionship in the pediatric inpatient care context.

We developed a smartphone application that detects users' affect and provides personalized positive psychology interventions in order to enhance users' psychological wellbeing. Users' emotional states were measured by analyzing facial expressions and the sentiment of SMS messages. A virtual character in the application prompted users to verbally journal about their day by providing three positive psychology interventions. The system used a Markov Decision Process (MDP) model and a State-Action-Reward-State-Action (SARSA) algorithm to learn users' preferences about the positive psychology interventions. Nine participants were recruited for an experimental study to test the application. They used it daily for three weeks. The interactive journaling activity increased participants' arousal and valence levels immediately following each interaction, and we saw a trend toward improved self-acceptance levels over the three week period. The interaction duration increased significantly throughout the study as well. The qualitative analysis on journal entries showed that the application users explored and reflected on various aspects of themselves by looking at daily events, and found novel appreciation for and meanings in their daily routine.

Tega is a new expressive “squash and stretch”, Android-based social robot platform, designed to enable long-term interactions with children.

We present the design of the Huggable robot that can playfully interact with children and provide socio-emotional support for them in pediatric care context. Our design takes into consideration that many young patients are nervous, intimidated, and are socio-emotionally vulnerable at hospitals. The Huggable robot has a childish and furry look be perceived friendly and can perform swift and smooth motions. It uses a smart phone device for its computational power and internal sensors. The robot's haptic sensors perceive physical touch and can use the information in meaningful ways. The modular arm component allows easy sensor replacement and increases the usability of the Huggable robot for various pediatric care services. From a preliminary pilot user study with two healthy and two ill children, all participants enjoyed playing with the robot but the two children with medical conditions showed caring and empathetic behaviors than the two health children. We learned various types of physical touch occurred during the child-robot interaction, and will continue to develop more intelligent haptic sensory system for the Huggable robot to better assist and support child patients' socio-emotional needs.

Research from the past two decades indicates that preschool is a critical time for children's oral language and vocabulary development, which in turn is a primary predictor of later academic success. However, given the inherently social nature of language learning, it is difficult to develop scalable interventions for young children. Here, we present one solution in the form of robotic learning companions, using the DragonBot platform. Designed as interactive, social characters, these robots combine the flexibility and personalization afforded by educational software with a crucial social context, as peers and conversation partners. They can supplement teachers and caregivers, allowing remote operation as well as the potential for autonomously participating with children in language learning activities. Our aim is to demonstrate the efficacy of the DragonBot platform as an engaging, social, learning companion.