Feeling socially connected is important for personal well-being, yet many older adults report increasing loneliness and decreasing social connections. We explored how robots and their behaviors can support group interactions and foster social participation among older adults in a community center setting over repeated interactions. We developed a semi-autonomous collaborative and discussion-based variant of the game "With Other Words" for groups of three to four older adults and two robots. A facilitator robot (Furhat) mediated discussions using gaze and verbal support, while a guesser robot (Misty) attempted to guess the words that group members described 'with other words'. We invited 34 older adults aged 65+ to play the game in groups of three or four, three times over two to five weeks. An explorative mixed-method analysis, combining quantitative metrics with Ethnomethodological Conversation Analysis (EMCA), shows that robot gaze and verbal behaviors as well as negotiations around "wrangling" the guesser robot encouraged participation in the game. Further, verbal supporting behaviors elicited shared laughter but also led to breakdowns. While no direct significant improvement in social connectedness was observed, this work contributes to our understanding of how robot behaviors might shape interactions among older adults.

Group interactions are a natural part of our daily life, and as robots become more integrated into society, they must be able to socially interact with multiple people at the same time. However, group human-robot interaction (HRI) poses unique computational challenges often overlooked in the current HRI literature. We conducted a scoping review including 44 group HRI papers from the last decade (2015–2024). From these papers, we extracted variables related to perception and behaviour generation challenges, as well as factors related to the environment, group, and robot capabilities that influence these challenges. Our findings show that key computational challenges in perception included detection of groups, engagement, and conversation information, while challenges in behaviour generation involved developing approaching and conversational behaviours. We also identified research gaps, such as improving detection of subgroups and interpersonal relationships, and recommended future work in group HRI to help researchers address these computational challenges.

Whisper fails to correctly transcribe dementia speech because persons with dementia (PwDs) often exhibit irregular speech patterns and disfluencies such as pauses, repetitions, and fragmented sentences. It was trained on standard speech and may have had little or no exposure to dementia-affected speech. However, correct transcription is vital for dementia speech for cost-effective diagnosis and the development of assistive technology. In this work, we fine-tune Whisper with the open-source dementia speech dataset (DementiaBank) and our in-house dataset to improve its word error rate (WER). The fine-tuning also includes filler words to ascertain the filler inclusion rate (FIR) and F1 score. The fine-tuned models significantly outperformed the off-the-shelf models. The medium-sized model achieved a WER of 0.24, outperforming previous work. Similarly, there was a notable generalisability to unseen data and speech patterns.

Individual cognitive stimulation therapy (iCST) is a non-pharmacological intervention for improving the cognition and quality of life of persons with dementia (PwDs); however, its effectiveness is limited by low adherence to delivery by their family members. In this work, we present the user-centered design and evaluation of a novel socially assistive robotic system to provide iCST therapy to PwDs in their homes for long-term use. We consulted with 16 dementia caregivers and professionals. Through these consultations, we gathered design guidelines and developed the prototype. The prototype was validated by testing it with three dementia professionals and five PwDs. The evaluation revealed PwDs enjoyed using the system and are willing to adopt its use over the long term. One shortcoming was the system’s speech-to-text capabilities, where it frequently failed to understand the PwDs.

In the past two decades, the field of social robotics has undergone significant growth, witnessing a surge in long-term human–robot interaction (HRI) studies. This review paper provides an in-depth analysis of 120 long-term HRI studies conducted between 2003 and 2023, spanning 7 major domains including education, entertainment, and physical and mental health. We define “long-term” as studies deploying social robots with the same users for more than three sessions across 3 consecutive days, aiming to employ a comprehensive approach and identify trends in this dynamic field. Our analysis explores various aspects of these studies, from participant demographics to the characteristics of the HRI and engagement measures. The findings reveal promising trends, such as diverse age group representation, a strong focus on real-world contexts, and autonomous robot operation. We also identify gaps, notably the limited representation of studies involving teenagers and those studying workplace settings. By presenting this overview, we aim to empower the HRI community to address challenges, refine methodologies, and foster innovation in the domain of long-term HRI.

A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.

In peer tutoring, the learner is taught by a colleague rather than by a traditional tutor. This strategy has been shown to be effective in human tutoring, where students have higher learning gains when taught by a peer instead of a traditional tutor. Similar results have been shown in child-robot interactions studies, where a peer robot was more effective than a tutor robot at teaching children. In this work, we compare skill increase and perception of a peer robot to a tutor robot when teaching adults. We designed a system in which a robot provides personalized help to adults in electronic circuit construction. We compare the number of learned skills and preferences of a peer robot to a tutor robot. Participants in both conditions improved their circuit skills after interacting with the robot. There were no significant differences in number of skills learned between conditions. However, participants with low prior domain knowledge learned significantly more with a peer robot than a tutor robot. Furthermore, the peer robot was perceived as friendlier, more social, smarter, and more respectful than the tutor robot, regardless of initial skill level.

Regular exercise provides many mental and physical health benefits. However, when exercises are done incorrectly, it can lead to injuries. Because the COVID-19 pandemic made it challenging to exercise in communal spaces, the growth of virtual fitness programs was accelerated, putting people at risk of sustaining exercise-related injuries as they received little to no feedback on their exercising techniques. Co-located robots could be one potential enhancement to virtual training programs as they can cause higher learning gains, more compliance, and more enjoyment than non-co-located robots. In this study, we compare the effects of a physically present robot by having a person exercise either with a robot (robot condition) or a video of a robot displayed on a tablet (tablet condition). Participants (N=25) had an exercise system in their homes for two weeks. Participants who exercised with the co-located robot made fewer mistakes than those who exercised with the video-displayed robot. Furthermore, participants in the robot condition reported a higher fitness increase and more motivation to exercise than participants in the tablet condition.

Creating an accurate model of a user's skills is necessary for intelligent tutoring systems. Without an accurate model, sample problems or tasks must be selected haphazardly by the tutor. Once an accurate model has been trained, the tutor can selectively focus on training essential or deficient skills. Prior work offers mechanisms for optimizing the training of a single skill or for multiple skills when individual tasks involve testing only a single skill at a time, but not for multiple skills when individual tasks can contain evidence for multiple skills. In this paper, we present a system that estimates user skill models for multiple skills by selecting tasks which maximize the information gain across the entire skill model. We compare our system's policy against several baselines and an optimal policy in both simulated and real tasks. Our system outperforms baselines and performs almost on par with the optimal policy.

Studies have shown that people conform their answers to match those of group members even when they believe the group’s answer to be wrong [2]. In this experiment, we test whether people conform to groups of robots and whether the robots cause informational conformity (believing the group to be correct), normative conformity (feeling peer pressure), or both. We conducted an experiment in which participants (N = 63) played a subjective game with three robots. We measured humans’ conformity to robots by how many times participants changed their preliminary answers to match the group of robots’ in their final answer. Participants in conditions that were given more information about the robots’ answers conformed significantly more than those who were given less, indicating that informational conformity is present. Participants in conditions where they were aware they were a minority in their answers conformed more than those who were unaware they were a minority. Additionally, they also report feeling more pressure to change their answers from the robots, and the amount of pressure they reported was correlated to the frequency they conformed, indicating normative conformity. Therefore, we conclude that robots can cause both informational and normative conformity in people.

Inspired by the benefits of human prosocial behavior, we explore whether prosocial behavior can be extended to a Human-Robot Interaction (HRI) context. More specifically, we study whether robots can induce prosocial behavior in humans through a 1x2 between-subjects user study (N=30) in which a confederate abused a robot. Through this study, we investigated whether the emotional reactions of a group of bystander robots could motivate a human to intervene in response to robot abuse. Our results show that participants were more likely to prosocially intervene when the bystander robots expressed sadness in response to the abuse as opposed to when they ignored these events, despite participants reporting similar perception of robot mistreatment and levels of empathy for the abused robot. Our findings demonstrate possible effects of group social influence through emotional cues by robots in human-robot interaction. They reveal a need for further research regarding human prosocial behavior within HRI.

Asch's [2] conformity experiment has shown that people are prone to adjusting their view to match those of group members even when they believe the answer of the group to be wrong. Previous studies have attempted to replicate Asch's experiment with a group of robots but have failed to observe conformity [7, 25]. One explanation can be made using Hodges and Geyers work [17], in which they propose that people consider distinct criteria (truth, trust, and social solidarity) when deciding whether to conform to others. In order to study how trust and truth affect conformity, we propose an experiment in which participants play a game with three robots, in which there are no objective answers. We measured how many times participants changed their preliminary answers to match the group of robots' in their final answer. We conducted a between-subjects study (N = 30) in which there were two conditions: one in which participants saw the group of robots' preliminary answer before deciding their final answer, and a control condition in which they did not know the robots' preliminary answer. Participants in the experimental condition conformed significantly more (29%) than participants in the control condition (6%). Therefore we have shown that groups of robots can cause people to conform to them. Additionally trust plays a role in conformity: initially, participants conformed to robots at a similar rate to Asch's participants, however, many participants stop conforming later in the game when trust is lost due to the robots choosing an incorrect answer.

In their expanding role as tutors, home and healthcare assistants, robots must effectively interact with individuals of varying ability and temperament. Indeed, deploying robots in long-term social engagements will almost certainly require robots to reliably detect and adapt to changes in the demeanor of social partners to promote trust and more productive collaboration. However, the recognition of emotional state typically relies on the interpretation of very subtle cues, often varying from one person to the next. In addition, while facial expressions, body posture and features of speech have been used to detect affective changes, the robustness of these measures is often hindered by cultural and age differences. Recently, infrared thermography has shown promise in detecting guilt, fear and stress, indicating that it may be a viable sensing modality for improved human-robot interaction. In this study, we evaluated the efficacy of using a far infrared (FIR) camera for detecting robot-elicited affective response compared to video-elicited affective response by tracking thermal changes in five areas of the face. Further, we analyzed localized changes in the face to assess whether thermal and electrodermal responses to emotions elicited by traditional video techniques and by robots are similar. Finally, we performed principal component analysis to reduce the dimensionality of data and evaluated the performance using machine learning techniques for classifying thermal data by emotion state, resulting in a thermal classifier with a performance accuracy of 77.5%.

As research on robotic tutors increases, it becomes more relevant to understand whether and how robots will be able to keep students engaged over time. In this paper, we propose an algorithm to monitor engagement in small groups of children and trigger disengagement repair interventions when necessary. We implemented this algorithm in a scenario where two robot actors play out interactive narratives around emotional words and conducted a field study where 72 children interacted with the robots three times in one of the following conditions: control (no disengagement repair), targeted (interventions addressing the child with the highest disengagement level) and general (interventions addressing the whole group). Surprisingly, children in the control condition had higher narrative recall than in the two experimental conditions, but no significant differences were found in the emotional interpretation of the narratives. When comparing the two different types of disengagement repair strategies, participants who received targeted interventions had higher story recall and emotional understanding, and their valence after disengagement repair interventions increased over time.

Robot assistive technology is becoming increasingly prevalent. Despite the growing body of research in this area, the role of type of interaction (i.e., small groups versus individual interactions) on effectiveness of interventions is still unclear. In this paper, we explore a new direction for socially assistive robotics, where multiple robotic characters interact with children in an interactive storytelling scenario. We conducted a between-subjects repeated interaction study where a single child or a group of three children interacted with the robots in an interactive narrative scenario. Results show that although the individual condition increased participant's story recall abilities compared to the group condition, the emotional interpretation of the story content seemed more dependent on the difficulty level rather than the study condition. Our findings suggest that, despite the type of interaction, interactive narratives with multiple robots are a promising approach to foster children's development of social-related skills.

Changes in type of interaction (e.g., individual vs. group interactions) can potentially impact data-driven models developed for social robots. In this paper, we provide a first investigation in the effects of changing group size in data-driven models for HRI, by analyzing how a model trained on data collected from participants interacting individually performs in test data collected from group interactions, and vice-versa. Another model combining data from both individual and group interactions is also investigated. We perform these experiments in the context of predicting disengagement behaviors in children interacting with two social robots. Our results show that a model trained with group data generalizes better to individual participants than the other way around. The mixed model seems a good compromise, but it does not achieve the performance levels of the models trained for a specific type of interaction.