With technological advancements, children increasingly interact with robots designed to mimic human-like behaviors for communication, among which gaze is particularly pivotal from early childhood. This study thus explores how children attribute and form preferences when exposed to human versus robotic gazes. The research involved 58 Italian children aged 3 to 5 years. They watched videos featuring a human and a robot each gazing at one of two objects. Subsequently, children were asked which object the gazer preferred (preference attribution) and to indicate their own preference (preference formation). Attribution of object preference was evaluated also as a function of children’s Theory of Mind (i.e., false belief) and mental state attributions to human and robot agents. Results showed that children consistently attributed preferences based on human gaze, but not robot gaze, suggesting that they interpret human gaze as a meaningful communicative signal, likely associated with intentionality. Gaze had no significant effect on children’s own preferences for either agent. Importantly, attribution of mental states to the human, but not to the robot, significantly predicted accurate preference attribution. No associations were found between performance on the false-belief task and gaze-based responses, indicating that explicit preference attribution may rely on socio-cognitive processes distinct from belief-based reasoning. These findings provide design-relevant insights for child–robot interaction, suggesting that gaze alone may not function as an effective communicative cue for young children and highlighting the importance of developmentally informed interaction strategies in robotic systems designed for early childhood.

In android science, the greater the resemblance between an android and a human, the greater is the expectation of observers for its movements to appear natural; therefore, the "human-likeness" of android motion is important. Traditionally, studies on the human-likeness of motion have addressed two separate problems: generating motion trajectories and realizing the target trajectories on hardware. However, trajectory generation often relies on subjective evaluations and lacks quantitative metrics, whereas trajectory realization has limited reproducibility owing to the physical characteristics and control performance of the android. Therefore, treating these problems separately complicates the task of achieving human-like motion. This study proposes an approach that quantitatively addresses the human-likeness of such movements under the assumption that the target trajectory can be stably reproduced. As a control method, we adopted Active Disturbance Rejection Control (ADRC), which can suppress disturbances and produce stable responses even for complex nonlinear systems. We aim to express the human-likeness of movements realized under this control by using engineering metrics. Specifically, we computed the engineering metrics from motion data obtained by varying theADRCparameters and quantified the subjective perceptions of human-likeness based on pairwise comparison experiments. The analysis of the relationship between these two sets of measures confirmed a reasonable agreement between the ranking trends of the engineering metrics and the subjective evaluations. These results indicate that the proposed engineering metrics can quantitatively describe human-like motion independent of subjective assessments, and are effective as objective and unified indicators for android motion design.

This study investigated the differences between human and robot gaze in influencing preference formation, and examined the role of Theory of Mind (ToM) abilities in this process. Human eye gaze is one of the most important sources of information for social interaction and research has demonstrated its effectiveness in influencing people's preference. With increasing technological development, we will interact with robots that can exhibit gaze behavior and influence people's preference. It is unclear whether there are any differences between humans and robots in this process. The present study aimed to analyze the role of the gaze of a robot and a human in influencing the ascription of a preference to the gazer and the participants' preference. Furthermore, we examined the role of ToM abilities in preference formation. The results showed that the gaze has a greater effect on the gazer preference compared to participants' preference regardless of the agent (human or robot). In addition, ToM abilities predict both gazer and individual preferences in the robot's condition only even though different socio-cognitive mechanisms are involved. The study suggests that adults are cognitively able to process the gaze of a robot similar to a human, recognizing the underlying mental state. However, only for the robot, different cognitive mechanisms are involved in the gazer (i.e., perspective taking) and participants' preference formation (i.e., advanced ToM).

Introduction Social robots are increasingly being integrated into children's daily lives, shaping their social interactions and learning behaviors. However, no study has empirically investigated the effect of robot-administered praise in children younger than 4 years old.Method This study focuses on the social robot CommU, a simple, approximately 30 cm tall, child-shaped robot that exerts less social pressure and helps children attend to social cues more easily. We examined whether praise from CommU is associated with task persistence in children aged 18-24 months, in comparison with human praise.Result Children showed greater task persistence in the Praise condition than in the No Praise condition, regardless of agent type (CommU vs. Human). In addition, children's task persistence was positively associated with the amount of time they spent looking at the agent.Discussion These findings suggest that praise delivered by a social robot is associated with greater task persistence in children aged 18-24 months. Additionally, the positive association between task persistence and time spent looking at the agent suggests that children's social attention may contribute to sustained engagement during the task. More broadly, the results point to the possibility that social robots may be relevant to aspects of early childhood engagement, beyond the specific task-persistence behavior examined in this study.

Academic self-esteem (ASE) plays a significant role in children's learning outcomes by influencing their engagement and performance in subjects, such as math. Although existing studies emphasize the importance of self-esteem in academic achievement, the potential of interactive technologies, particularly virtual robots, in enhancing ASE remains underexplored. This study investigates the impact of a virtual robot on children's ASE, math performance, concentration, and engagement in an e-learning environment. The study involved an experimental group (n = 12) interacting with a virtual robot integrated into the math e-learning platform, and a control group (n = 12) working in a traditional e-learning setting without robot interaction. The results demonstrated that the experimental group, which interacted with the virtual robot, exhibited significant improvements in math performance, concentration, and engagement across the three experimental sessions (sessions 1-3) compared with the control group, as indicated by both quantitative measures and qualitative feedback from participants. ASE, as well as the quantity and quality of friendships, was assessed pre- and posttest, with findings indicating greater improvements in the experimental group after the intervention. The correlation between improved math performance and higher ASE was moderate to strong. These findings underscore the potential of virtual robots as tools that positively influence the ASE and learning outcomes of children, and highlight their value for future educational settings where such technology could address achievement gaps in mathematics learning through improved self-perception.

Cybernetic avatars are hybrid interaction robots or digital representations that combine autonomous capabilities with teleoperated control. This study investigates the acceptance of cybernetic avatars, with particular emphasis on robot avatars for customer service. Specifically, we explore how acceptance varies as a function of modality (physical vs. virtual), robot appearance (e.g., android, robotic-looking, cartoonish), deployment settings (e.g., shopping malls, hotels, hospitals), and functional tasks (e.g., providing information, patrolling). To this end, we conducted a large-scale survey with over 1,000 participants in Dubai. As one of the most multicultural societies worldwide, Dubai offers a rare opportunity to capture opinions from multiple cultural clusters within a single setting simultaneously, thereby overcoming the limitations of nationally bound samples and providing a more global picture of acceptance. Overall, cybernetic avatars received a high level of acceptance, with physical robot avatars receiving higher acceptance than digital avatars. In terms of appearance, robot avatars with a highly anthropomorphic robotic appearance were the most accepted, followed by cartoonish designs and androids. Animal-like appearances received the lowest level of acceptance. Among the tasks, providing information and guidance was rated as the most valued. Shopping malls, airports, public transport stations, and museums were the settings with the highest acceptance, whereas healthcare-related spaces received lower levels of support. An analysis by community cluster revealed, among other findings, that Emirati respondents were particularly accepting of android appearances, whereas participants from the ‘Other Asia’ cluster were particularly accepting of cartoonish appearances. Our study underscores the importance of incorporating citizen feedback from the early stages of design and deployment to enhance societal acceptance of cybernetic avatars.

An important but under-explored use case for socially assistive robotics is in community formation. In this work, we conduct a novel investigation into the ability of a social robot to encourage human-human relationships among users. We conduct a between-subjects Wizard-of-Oz experiment in which we focus on the robot’s role in interaction as a determinant in the sense of connection that participants feel as a result of interacting with the robot. N=86 college-age participants were recruited to watch a video with a robot, where the robot’s group membership and behavior were adjusted to reflect different roles. We vary the robot’s group membership along three levels: the same group as the participants, engaging in the same activity as the participants, or simply being in the same room and acknowledging the participants, with a control condition of no robot present. Before, during, and after the video, the robot would speak to encourage interaction between participants and the robot when applicable. After the video was concluded, participant sense of connection was measured through a series of Likert surveys. We find that inter-user conversation increased when the robot actively participated in the video viewing and asked questions of the users compared to when the robot did not. We also find some exploratory evidence that these effects might extend to user attitudes about not only each other but also the laboratory running the experiment.

Emotional and social expression are important dimensions of socially empathic human-robot interaction. In hug gable robot contexts, the chest is not only a stable bodily contact interface with strong affective relevance, but also a natural site for presenting haptic and auditory cues together. However, existing chest-haptic designs still focus mainly on physical tactile parameters or heartbeat-like patterns. Conversely, chest haptic patterns formed by different stimulation-location combinations over time may offer richer possibilities for multimodal expression and modulation. To explore this, we implemented a parameterizable 3×3 chest-mounted vibrotactile array on a huggable robot and constructed chest haptic patterns using path type and activation area as design parameters. We conducted two experiments. Experiment 1 examined how chest haptic patterns influenced the affective perception of emotional music. Experiment 2, under a fixed activation area setting, examined how the presence of chest haptics and different path conditions influenced the social emotional evaluation of verbal expressions with empathic intentions. The results showed that, in the emotional music context, chest haptic patterns influenced the direction of emotional rating shifts and showed more pronounced modulation effects under some music conditions. In the empathic speech context, the addition of chest haptics generally improved social-emotional evaluations. These findings provide empirical support for the use of chest haptic patterns in multimodal social-emotional expression, and offer guidance for the future development of huggable robots with richer emotional and empathic expression.

This study investigates whether conversational robots can foster a sense of community beyond immediate interactions, specifically examining how different dialogue strategies influence the expansion of a sense of community from a micro-social space to a broader regional community. We conducted a longitudinal field experiment in a coworking space using a communication robot. Participants were assigned to either a "Suggested” strategy group (focusing on interest-based community connections) or a "Desired" strategy group (conveying that the participant is needed by the community). Linear mixed-effects modeling revealed a significant interaction between trial period and strategy group for coworking space self-usefulness, with the ”Suggested” strategy leading to an enhanced sense of self-usefulness toward the coworking space. Furthermore, moderated mediation analysis indicated that only the "Suggested" strategy significantly enhanced self-usefulness to the broader regional community, a process exclusively mediated by the increased self-usefulness within the coworking space. These findings indicate that robots can function as social catalysts; however, their impact on macro-level community attitudes is not direct but contingent upon the cultivation of a localized sense of belonging. This study provides empirical evidence for designing social robots as infrastructure for regional community development.

In recent years, extensive research has been conducted on avatars, and multiple studies have demonstrated their effectiveness as a medium for remote operation. While avatars are effective when teleoperated, they must also be capable of autonomous behavior in the absence of an operator. In particular, avatars whose appearance closely resembles that of a real individual need to possess conversational abilities that reflect the personality of the person being modeled. This paper presents the development of a speech generation system that produces personality-consistent utterances using a large language model (LLM) and speech synthesis technology. We call this system AvatarLLM. Through system evaluation, we examined the factors contributing to the perception of individuality. Experimental results indicated that the utterances generated by AvatarLLM were perceived as more likely reproducing the modeled individual than those of the actual person. Furthermore, we found that the perceived identity of the utterances could influence the perceived identity of the voice itself.

Social isolation among older adults has become a critical concern, as reduced opportunities for conversation and weakened family relationships negatively affect mental health. This study proposes a dialogue agent that supports older adults by fostering both a relationship with the agent and a relationship with their grandchild through sharing everyday information. The agent operates on a chatbot platform and engages in daily conversations with older adults and their grandchildren, exchanging information gathered from each party to enhance conversational engagement and social connection. We conducted a ten-day empirical experiment with 108 grandparent-grandchild pairs. The results suggest that older adults became more willing to interact with the proposed agent, which shared information about their grandchildren, and that the psychological connection between grandparents and grandchildren was strengthened. Furthermore, daily interactions with the agent were associated with reduced anxiety in both older adults and their grandchildren. These findings indicate that a dialogue agent that shares personal information can be an effective approach to supporting older adults by simultaneously offering conversational opportunities and promoting family connectedness. Overall, this study provides valuable insights into the design of dialogue agents that effectively address social isolation among older adults.

Since robots are often disregarded in public interactions, many studies have examined how nonverbal cues and dialogue strategies encourage users to initiate engagement. However, the impact of robot “movement” remains insufficiently investigated. This study examined the psychological effects of movement behavior on willingness to engage in dialogue in a scenario where a mobile guide robot leads people to a stationary robot. A field experiment in a shopping mall showed that guidance by a mobile robot significantly increased dialogue duration, whereas no correlation was found between moving distance and willingness to engage. These results suggest that physical commitment induced by guided movement may enhance user motivation, and that interaction designs leveraging movement behavior may be important for advancing the social implementation of interactive robots.

Although companion robots have demonstrated psychological benefits for older adults, most studies have focused on short-term use or institutional settings. This case series with integrated qualitative analysis describes five cases involving the long-term use of a conversational companion robot (RoBoHoN, Sharp) among community-dwelling women aged 85 to 90 with mild cognitive impairment or late-onset psychosis. After an initial exploratory phase (2-5 months) and a 2-month washout, the robot was installed in each home for 18 months. All participants operated the robot in daily life, with only intermittent light-touch support from caregivers or the team when needed. Usability assessments showed high satisfaction and ease of use. Although standardised psychological scales showed no consistent improvements, participants reported enjoying conversations with the robot. Four of the five expressed a desire to continue using the robot after the study. These findings support the feasibility and potential long-term acceptability of companion robots among cognitively challenged older adults living at home. The case series suggests such robots may foster sustained engagement even in vulnerable populations. Further studies with larger samples are needed to evaluate the psychological effects of long-term companion robot use in home settings.

Mobile robots have seamlessly integrated into our daily lives, providing various services to humans. The key to successful interaction between humans and these robots lies in human acceptance. To enhance this acceptance, humans must intuitively and easily grasp the intentions behind the locomotion of mobile robots. Just as humans communicate changes in their walking or running speed through vertical oscillation and body leaning, this study delves into the utilization of these behaviors by mobile robots to influence human perception of their locomotion speed. Through laboratory experiments and video analyses, we evaluated the impact of these human-like behaviors on human perception. Our findings reveal that increasing the robot’s vertical oscillation frequency as it approaches humans can affect the perception of an increase in locomotion speed and an intention to accelerate. Additionally, the robot’s forward-leaning posture can affect the human perception of an increase in locomotion speed. Conversely, decreasing the robot’s vertical oscillation frequency as it approaches humans can result in the perception of a decrease in locomotion speed and an intention to decelerate. However, the backward-leaning posture does not influence perceptions of the robot’s locomotion speed changes or intentions. These findings enable humans to anticipate the robot’s locomotion and intentions more intuitively and easily, thereby fostering acceptance in human-robot interactions.

We report a mixed-methods field experiment of a conversational service robot deployed under everyday staffing discretion in a live bedding store. Over 12 days we alternated three conditions-Baseline (no robot), Robot-only, and Robot+Fixture-and video-annotated the service funnel from passersby to purchase. An explanatory sequential design then used six post-experiment staff interviews to interpret the quantitative patterns. Quantitatively, the robot increased stopping per passerby (highest with the fixture), yet clerk-led downstream steps per stopper-clerk approach, store entry, assisted experience, and purchase-decreased. Interviews explained this divergence: clerks avoided interrupting ongoing robot-customer talk, struggled with ambiguous timing amid conversational latency, and noted child-centered attraction that often satisfied curiosity at the doorway. The fixture amplified visibility but also anchored encounters at the threshold, creating a well-defined micro-space where needs could "close" without moving inside. We synthesize these strands into an integrative account from the initial show of interest on the part of a customer to their entering the store and derive actionable guidance. The results advance the understanding of interactions between customers, staff members, and the robot and offer practical recommendations for deploying service robots in high-touch retail.

Recent research has focused on developing non-task-oriented dialogue agents with high anthropomorphism, aiming to sustain users' dialogue motivation over prolonged periods. Previous studies have introduced chatbots that deliberately incorporate short response delays to enhance anthropomorphism and user satisfaction. However, in the context of long-term casual conversations, the behavior of these chatbots, which reply within a few seconds, may lead to a perception of the chatbot as always on standby, lacking its own schedule or personal life, thus appearing less humanlike. To address this issue, we propose a non-real-time chatbot that interacts with users while introducing deliberate response delays on a larger time scale, ranging from a few minutes to several hours. A five-day dialogue experiment confirmed that the non-real-time chatbot was perceived as more humanlike and encouraged users to engage in extensive dialogue. These insights are invaluable for designing chatbots that can maintain casual conversations over prolonged periods.