Social robots can support children’s emotional skills development through playful interactions, yet skills like emotional self-disclosure remain underexplored. This study investigates the impact of a social robot designed to encourage emotional self-disclosure during an emotion-identification game with children aged 6-10. In a between-subjects design with 28 participants across two local schools, we compared a Reflective condition, where the robot actively encouraged emotional self-disclosure through question-asking and reciprocal sharing, to a Control condition, where the robot did not. Children in the Reflective condition engaged in emotional self-disclosure when prompted, and showed higher engagement than those in Control. Direct question-asking was more effective than reciprocal self-disclosure. Results suggested that children who perceived the robot as kinder disclosed more, whereas those who viewed it as more real disclosed less. These findings highlight the potential of social robots to foster emotional skills in children and inform the design of future child-robot interaction research.

Human-robot interactions are becoming prevalent in a varied number of fields, with trust being essential for efficient collaboration between humans and robots. Robots, just like humans, are bound to make mistakes leading to a violation of trust. Research investigating how to repair this broken trust has produced mixed results. This work investigates the effects of five communicative trust repair strategies (apology, denial, explanation, compensation, and silence) on participants’ trust in the robot, following trust violations of two kinds (moral and performance violation). In an online between-subjects experiment, participants engaged in a collaborative task with a robot that repeatedly committed trust violating acts and responded with a repair message. The findings indicate the higher severity of moral violations on moral trust and willingness to collaborate in the future, with compensation showing to be the most effective repair strategy, enhancing trust and willingness to collaborate, while also reducing discomfort. This work advances the understanding of trust relationships in collaborative HRI contexts.

Chatbots are increasingly used to provide social support for individuals with mental health challenges. However, a systematic analysis of the types and directionality of support within chatbot use remains lacking. This study establishes a framework for understanding reciprocal social support exchanges in human-chatbot relationships, focusing on the popular chatbot, Replika. By analyzing 496 posts and 20,494 comments from the largest Replika community on Reddit, we identified 27 support subcategories, organized into five main types (functional, informational, emotional, esteem, and network) and two directions (chatbot-receiving and chatbot-giving). Our findings reveal significant yet controversial issues, such as subscription services and chatbot-displayed affection. Notably, "user teaching chatbot"emerged as a core aspect of the human-chatbot relationship, covering how users actively guide and refine the chatbot's learning or algorithm. This study constructs a novel social support framework for chatbot use, highlighting the potential for reciprocal support exchanges between users and chatbots.

As robots are prone to make errors that undermine trust, effective trust repair strategies are essential in effective human-robot collaboration. Our lab study evaluates three trust repair strategies -apology, denial, and compensation- following two types of trust violations: competence-based and integritybased. Consistent with prior research, integrity-based violations reduced moral trust more, while competence-based violations impacted performance trust. Denial caused greater discomfort than apology or compensation across both violation types. Dispositional trust influenced repair strategies effectiveness, particularly in willingness to engage and re-engage. Notably, individuals with high dispositional trust were more receptive to apologies. These findings underscore the need to consider individual trust differences, suggesting robots should assess human trust disposition to effectively foster continued collaboration.

Humanlike design of social robots can potentially reproduce societal biases. This paper presents an online video experiment (n = 194) examining the stereotyping effects of speech act (assertive vs. affiliative speech) on people’s evaluation of gendered robots (masculine vs. feminine) in terms of warmth, competence, and discomfort. Results show that feminine robots are rated higher in competence than masculine robots, regardless of speech act, and assertive robots are rated higher in competence than affiliative robots, regardless of robot gender. Additionally, women rate robots as more competent than men do. The results for warmth and discomfort are insignificant. This study emphasizes the need for theory-driven experiments addressing robot gendering and highlights the importance of avoiding the reinforcement of gender bias in social robot design.

People’s ascription of intentional agency to robots necessitates understanding how, when, and why people attribute robot behavior to underlying intentional states. While many studies explored mind attribution to robots including its determinants and consequences, little attention has been given to the attribution of second-order mental states, such as a robot’s beliefs about people’s intentions during interactions. In an online study (n = 155), participants watched a video of a humanoid robot tracking a ball hidden under one of two cups. 19% of participants predicted that the robot could correctly locate the ball after it was displaced twice by a person deliberately distracting the robot, indicating implicit attribution of second-order beliefs to the robot. These implicit attributions influenced participants’ actions in a subsequent interactive game with a virtual counterpart of the robot but did not affect their explicit assessments of the robot’s second-order reasoning. In contrast, observing the robot demonstrate second-order reasoning by correctly identifying the ball’s location affected participants’ explicit attributions but not their behavior in the interactive game. This reveals a complex interplay between implicit and explicit attribution processes in how people interpret robot behavior.

Children encounter high levels of stress and anxiety before receiving medical treatment, such as a vaccination. This paper explores the effect of emotional gesturing in socially assistive robots (SARs) on children's observed and self-reported engagement, as well as self-reported anxiety, fear, and trust during a group vaccination. A total of 249 children interacted with the social robot iPal before and after receiving the vaccine. Our results show an overall positive effect of adding emotional gestures to a SAR's interaction behavior leading to increased engagement and lower anxiety, while increased engagement also resulted in trusting the robot more. Thus, adding emotional gestures during child-robot interaction is a powerful way to improve the child's experience during a group vaccination day.

Social robots can support children in their socio-emotional development [38]. To improve the cooperation between a child and a social robot, a good relationship is vital. Self-disclosure is an essential element for building personal relationships. Yet, knowledge about the effects of self-disclosure in child-robot interactions is still lacking. To investigate effects of robot persona, child personality, and self-disclosure category on self-disclosure in child-robot interaction, we have conducted a field study at a science festival in which children had a conversation with a robot that either behaved human-like or robot-like. The results show a significant difference in the amount of self-disclosure (in conversation duration) between the two robot personas. Additionally, significant relationships were found between conscientiousness and extraversion and amount of self-disclosure (in word count). The participant disclosed significantly more about the category `Attitudes and Opinions’ than about ‘School’. Finally, a thematic analysis shows that the content of the conversations can be categorised in five plus one themes. Between robot personas, the content of the conversations did not differ in terms of conversation themes. However, in both conditions, we found that children generally feel comfortable sharing unpleasant experiences about present themes (such as COVID) in a first encounter with a robot.

Discrimination and bias are pressing issues of many AI and robotics applications. These outcomes may derive from limited datasets that do not fully represent society as a whole or from the AI scientific community's western-male configuration bias. Although being a pressing issue, understanding how robotic systems can replicate and amplify inequalities and injustice among underrepresented communities is still in its infancy among social science and technical communities. This workshop contributes to filling this gap by exploring the research question: What do diversity and inclusion mean in the context of Human-Robot Interaction (HRI)? Here, attention is directed to three different levels of HRI: the technical, the community, and the target user level. Overall, this workshop will focus on the idea that AI systems can be created to be more attuned to inclusive societal needs, respect fundamental rights, and represent contemporary values in modern societies by integrating diversity and inclusion considerations.

The topic of mental state attribution to robots has been approached by researchers from a variety of disciplines, including psychology, neuroscience, computer science, and philosophy. As a consequence, the empirical studies that have been conducted so far exhibit considerable diversity in terms of how the phenomenon is described and how it is approached from a theoretical and methodological standpoint. This literature review addresses the need for a shared scientific understanding of mental state attribution to robots by systematically and comprehensively collating conceptions, methods, and findings from 155 empirical studies across multiple disciplines. The findings of the review include that: (1) the terminology used to describe mental state attribution to robots is diverse but largely homogenous in usage; (2) the tendency to attribute mental states to robots is determined by factors such as the age and motivation of the human as well as the behavior, appearance, and identity of the robot; (3) there is a computer < robot < human pattern in the tendency to attribute mental states that appears to be moderated by the presence of socially interactive behavior; (4) there are conflicting findings in the empirical literature that stem from different sources of evidence, including self-report and non-verbal behavioral or neurological data. The review contributes toward more cumulative research on the topic and opens up for a transdisciplinary discussion about the nature of the phenomenon and what types of research methods are appropriate for investigation.

Robots will increasingly collaborate with human partners necessitating research into how robots negotiate negative collaborative outcomes. This study investigates the effect of blame attribution on trust assessments in human-robot collaboration. Participants (n = 60) collaboratively played a game with a humanoid robot in one of four conditions in a 2 (blame correctness: correct vs. incorrect) by 2 (blame target: human vs. robot) between-subjects experiment. Results show that people evaluate a robot more positively when it blames itself for collaborative failures, especially, it seems, in the case of incorrect self-blame. Our findings indicate a need to further research on effective communication strategies for robots that need to negotiate collaborative failures without compromising the trust relationships with its human partner.

introduction Open AccessIntroduction to the Special Issue on Explainable Robotic Systems Share on Authors: Maartje M. A. De Graaf Utrecht University Utrecht UniversityView Profile , Anca Dragan University of California, Berkeley University of California, BerkeleyView Profile , Bertram F. Malle Brown University Brown UniversityView Profile , Tom Ziemke Linkoping University Linkoping UniversityView Profile Authors Info & Claims ACM Transactions on Human-Robot InteractionVolume 10Issue 3July 2021 Article No.: 22pp 1–4https://doi.org/10.1145/3461597Online:11 July 2021Publication History 0citation320DownloadsMetricsTotal Citations0Total Downloads320Last 12 Months320Last 6 weeks80 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Human-robot interaction (HRI) research aims to design natural interactions between humans and robots. Intonation, a social signaling function in human speech investigated thoroughly in linguistics, has not yet been studied in HRI. This study investigates the effect of robot speech intonation in four conditions (no intonation, focus intonation, end-of-utterance intonation, or combined intonation) on conversational naturalness, social engagement, and people's humanlike perception of the robot collecting objective and subjective data of participant conversations (n = 120). Our results showed that humanlike intonation partially improved subjective naturalness but not observed fluency, and that intonation partially improved social engagement but did not affect humanlike perceptions of the robot. Given that our results mainly differed from our hypotheses based on human speech intonation, we discuss the implications and provide suggestions for future research to further investigate conversational naturalness in robot speech intonation.

Although both HRI and Communication Science often trace their origins to the transdisciplinary cybernetics of the 20th century, they have since developed in relative isolation, with scant scholarly exchange concerning the similarities and differences in their assumptions, insights, and approaches. The purpose of this half-day workshop is to explore the ways in which traditional communication theory and Human-Machine Communication theory from the discipline of Communication Science can be applied and be utilized in HRI studies.

It has long been assumed that when people observe robots they intuitively ascribe mind and intentionality to them, just as they do to humans. However, much of this evidence relies on experimenter-provided questions or self-reported judgments. We propose a new way of investigating people's mental state ascriptions to robots by carefully studying explanations of robot behavior. Since people's explanations of human behavior are deeply grounded in assumptions of mind and intentional agency, explanations of robot behavior can reveal whether such assumptions similarly apply to robots. We designed stimulus behaviors that were representative of a variety of robots in diverse contexts and ensured that people saw the behaviors as equally intentional, desirable, and surprising across both human and robot agents. We provided 121 participants with verbal descriptions of these behaviors and asked them to explain in their own words why the agent (human or robot) had performed them. To systematically analyze the verbal data, we used a theoretically grounded classification method to identify core explanation types. We found that people use the same conceptual toolbox of behavior explanations for both human and robot agents, robustly indicating inferences of intentionality and mind. But people applied specific explanatory tools at somewhat different rates and in somewhat different ways for robots, revealing specific expectations people hold when explaining robot behaviors.

In addition to the aggregated analyses across ten intentional behaviors, reported in the main paper, we also broke the behaviors down by surprise, desirability, and currentness (see Table 1).

Robots are being increasingly developed as social actors, entering public and personal spaces such as airports, shopping malls, care centres, and even homes, and using human or animal-like social techniques to work with people. Some even aim to engineer social situations, or are designed specifically for an emotional response (e.g., comforting a person). However, if we consider these robots as social interventions, then it is important to recognize that the robots design - its behavior, its application, its appearance, even its marketing image - will have an impact on the society and in the spaces it enters. While in some cases this may be a positive effect, social robots can also contribute negatively, e.g., reinforcing gender stereotypes or promoting ageist views. This full-day workshop aims to offer a forum for Human-Robot Interaction (HRI) researchers to explore this issue, and to work toward potential opportunities for the field. Ultimately, we want to promote robots for social good that can contribute to positive social changes for socio-political issues (e.g., ageism, feminism, homelessness, environmental issues). The political aspects of technologies have long been scrutinized in related areas such as Science and Technology Studies (STS) and Human-Computer Interaction (HCI). In particular, critical design explicitly targets the design of technologies that can contribute to our understanding of how technology can impact society. This workshop aims to strengthen this discussion in the HRI community, with the goal of working toward initial recommendations for how HRI designers can include elements of critical design in their work.

Many independent studies in social robotics and human–robot interaction have gained knowledge on various factors that affect people’s perceptions of and behaviors toward robots. However, only a few of those studies aimed to develop models of social robot acceptance integrating a wider range of such factors. With the rise of robotic technologies for everyday environments, such comprehensive research on relevant acceptance factors is increasingly necessary. This article presents a conceptual model of social robot acceptance with a strong theoretical base, which has been tested among the general Dutch population (n = 1,168) using structural equation modeling. The results show a strong role of normative believes that both directly and indirectly affect the anticipated acceptance of social robots for domestic purposes. Moreover, the data show that, at least at this stage of diffusion within society, people seem somewhat reluctant to accept social behaviors from robots. The current findings of our study and their implications serve to push the field of acceptable social robotics forward. For the societal acceptance of social robots, it is vital to include the opinions of future users at an early stage of development. This way future designs can be better adapted to the preferences of potential users.

Research on why people refuse or abandon the use of technology in general, and robots specifically, is still scarce. Consequently, the academic understanding of people's underlying reasons for non-use remains weak. Thus, vital information about the design of these robots including their acceptance and refusal or abandonment by its users is needed. We placed 70 autonomous robots within people's homes for a period of six months and collected reasons for refusal and abandonment through questionnaires and interviews. Based on our findings, the challenge for robot designers is to create robots that are enjoyable and easy to use to capture users in the short-term, and functionally-relevant to keep those users in the longer-term. Understanding the thoughts and motives behind non-use may help to identify obstacles for acceptance, and therefore enable developers to better adapt technological designs to the benefit of the users.

With an expected growth of robots in our future society, we believe that potential implications for robot applications should be addressed. Therefore, we conducted an online questionnaire among the general Dutch population (n= 1162) to map the societal impact of robots by identifying potential benefits and disadvantages of future robot applications. People differentiate between several applications, and more realistic applications were also rated more positively. Overall, people associate a future robot society with the positive consequences of efficiency, decrease of casualties, and convenience, and the negative consequences of job loss and robots' lack of social skills. Our qualitative approach provides an in-depth evaluation of potential future robot applications, which could prompt guidelines for the development of acceptable robots.

The acceptability of robots in homes does not depend solely on the practical benefits they may provide, but also on complex relationships between cognitive, affective and emotional components of people's associations of and attitudes towards robots. This important area of research mainly relies on explicit measures, and alternative measures are rather unexplored. We therefore studied both implicit and explicit associations of robots, and found inconsistent findings between implicit and explicit measures. Our findings speak in favor of the proposition that people are actually more negative about robots than they consciously express. Since associations play an important role when people form attitudes towards robots we stress that caution when researchers and designers solely rely on explicit measures in their research.

Robotics researchers foresee that robots will become ubiquitous in our natural environments, such as our homes. For a successful diffusion of social robots, it is important to study the user acceptance of such robots. In an online survey, we have investigated the acceptance of three different possible roles for domestic social robots and the preferred appearance. The results show that, although most people prefer a humanoid robot for domestic purposes, the role for which a social robot is build affects the choice for a robotic appearance made by potential future users. When comparing the acceptance of the three different roles, people evaluate the companion robot more negatively on the different acceptance variables. Implications of these results are discussed.

People tend to treat robots as social actors and assign personality attributes to them. This study investigates the influence of expectation setting on the users' attribution of personality traits to the robot and their impressions of that robot. Results show that personality attribution is depending on people's prior expectations of the interaction. Moreover, people evaluate a robot better when they assigned it with a complementary personality and when they had high prior expectations of that robot.

This study identifies the design preferences of robots for domestic use. In an online survey, participants rated 16 robot pictures on several evaluation criteria. Results show that overall anthropomorphic robots are more positively rated than either zoomorphic, caricatured or functional robots. Moreover, negative feelings towards robot (e.g. negative attitudes and anxiety towards robots) negatively affected the design evaluations. Providing future users with positive information about domestic robots could improve their feelings towards robots, which, in turn, raises their ratings of robotic designs.

This paper examines the relation between an interaction with a robot and peoples' attitudes and emotion towards robots. In our study, participants have had an acquaintance talk with a social robot and both their general attitude and anxiety towards social robots were measured before and after the interaction. This study has found mixed results as compared to earlier studies. However, the utility of negative attitude and anxiety to explain human behavior in interactions with robots is supported. Furthermore, a human-robot interaction (HRI) changes people's attitudes and anxiety towards robots. Thus, from a design perspective, it seems important to further investigate which aspects of robots evoke what type of emotions and how this influences the overall evaluation of robots.