This study examined an important debate in Human-Robot Interaction (HRI) research: the suitability of non-physically non-collocated robots instead of physically collocated robots for HRI research. This meta-analysis ($\mathrm{N}=34$studies) examined the equivalence of physically and non-physically collocated robots in HRI research, focusing on anthropomorphism, social presence, and user engagement. No significant differences were found, suggesting that non-physical representations are viable alternatives. However, observed heterogeneity indicates potential moderating factors (e.g., task complexity, user characteristics, design features) warranting further investigation. These findings inform choices in resource-constrained environments.

As robots become more integrated into various sectors, understanding human-robot interaction (HRI) dynamics, particularly trust repair, is crucial for successful collaboration. For this paper, the authors conducted a meta-analysis of 22 HRI trust repair studies with 3,763 participants to evaluate the effectiveness of strategies for restoring trust after breaches relative to offering no repair. The analysis identified three key findings: (1) strategies are differentially effective, showing limited success in restoring trustworthiness; (2) the overall impact on repairing trust is marginal, with a small effect size; and (3) apologies and explanations are the most effective strategies for trust repair. These insights enrich HRI literature by providing a comprehensive evaluation of trust repair mechanisms, offering valuable guidance for future research and practical improvements in human-robot collaboration.

The rapid deployment of security robots across our society calls for further examination of their acceptance. This study explored human acceptance of security robots by theoretically extending the technology acceptance model to include the impact of autonomy and risk. To accomplish this, an online experiment involving 236 participants was conducted. Participants were randomly assigned to watch a video introducing a security robot operating at an autonomy level of low, moderate, or high, and presenting either a low or high risk to humans. This resulted in a 3 (autonomy) × 2 (risk) between-subjects design. The findings suggest that increased perceived usefulness, perceived ease of use, and trust enhance acceptance, while higher robot autonomy tends to decrease acceptance. Additionally, the physical risk associated with security robots moderates the relationship between autonomy and acceptance. Based on these results, this paper offer recommendations for future research on security robots.

Robots are inherently imperfect, and collaborating with an error-prone robotic teammate can deteriorate perceptions of trust and the willingness of users to continue working with the robot. Evidence-based trust repair strategies can be implemented into a robot’s design to mitigate the decline of trust in human-robot relationships following errors. It is not yet clear what trust repair strategies are most effective. To address this shortcoming, this study investigates two novel trust repair strategies: offered and automatic physical repair. A between-subjects repeated measures study was performed to determine the extent to which each type of physical trust repair was successful in restoring participants’ perceptions of trust. The results indicated that, where the no-repair condition experienced a significant decrease in trust score, only the automatic repair was consistently successful in bypassing the trust decline. Detailed analysis showed that participants from the offered repair condition did not view the robot as providing the appropriate information, meaning that the offer itself may have confused them. Participants’ response rate to the MultiDimensional Measure of Trust also revealed that users were less willing to associate moral terms with robotic teammates, though this hesitancy may reduce over time. These results contribute to research on human-robot trust repair by uncovering that physical repair is effective when it is automatic, but not when it is offered. This finding will help to further elucidate what repair strategies work to mitigate trust decline and thus help inform robot design.

Robot co-workers, like human co-workers, make mistakes that undermine trust. Yet, trust is just as important in promoting human-robot collaboration as it is in promoting human-human collaboration. In addition, individuals can signif-icantly differ in their attitudes toward robots, which can also impact or hinder their trust in robots. To better understand how individual attitude can influence trust repair strategies, we propose a theoretical model that draws from the theory of cognitive dissonance. To empirically verify this model, we conducted a between-subjects experiment with 100 participants assigned to one of four repair strategies (apologies, denials, explanations, or promises) over three trust violations. Individual attitudes did moderate the efficacy of repair strategies and this effect differed over successive trust violations. Specifically, repair strategies were most effective relative to individual attitude during the second of the three trust violations, and promises were the trust repair strategy most impacted by an individual's attitude.

Trust is vital for effective human-robot teams. Trust is unstable, however, and it changes over time, with decreases in trust occurring when robots make mistakes. In such cases, certain strategies identified in the human-human literature can be deployed to repair trust, including apologies, denials, explanations, and promises. Whether these strategies work in the human-robot domain, however, remains largely unknown. This is primarily because of the fragmented and dispersed state of the current literature on trust repair in HRI. As a result, this paper brings together studies on trust repair in HRI and presents a more cohesive view of when apologies, denials, explanations, and promises have been seen to repair trust. In doing so, this paper also highlights possible gaps and proposes future work. This contributes to the literature in several ways but primarily provides a starting point for future research and recommendations for studies seeking to determine how trust can be repaired in HRI.

Human personality has been identified as a predictor of robot acceptance in the human–robot interaction (HRI) literature. Despite this, the HRI literature has provided mixed support for this assertion. To better understand the relationship between human personality and robot acceptance, this paper conducts a meta-analysis of 26 studies. Results found a positive relationship between human personality and robot acceptance. However, this relationship varied greatly by the specific personality trait along with the study sample’s age, gender diversity, task, and global region. This meta-analysis also identified gaps in the literature. Namely, additional studies are needed that investigate both the big five personality traits and other personality traits, examine a more diverse age range, and utilize samples from previously unexamined regions of the globe.

Collaborative work between humans and robots holds great potential but, such potential is diminished should humans fail to accept robots as collaborators. One solution is to design robots to have a similar personality to their human collaborators. Typically, this is done by matching the human’s and robot’s personality using one or more of the Big Five Personality (BFI) traits. The results of this matching, however, have been mixed. This makes it difficult to know whether personality similarity promotes robot acceptance. To address this shortcoming, we conducted a systematic quantitative meta- analysis of 13 studies. Overall, the results support the assertion that matching personalities between humans and robots promotes robot acceptance.

Trust is vital to promoting human and robot collaboration, but like human teammates, robots make mistakes that undermine trust. As a result, a human’s perception of his or her robot teammate’s trustworthiness can dramatically decrease [1], [2], [3], [4]. Trustworthiness consists of three distinct dimensions: ability (i.e. competency), benevolence (i.e. concern for the trustor) and integrity (i.e. honesty) [5], [6]. Taken together, decreases in trustworthiness decreases trust in the robot [7]. To address this, we conducted a 2 (high vs. low anthropomorphism) x 4 (trust repair strategies) between-subjects experiment. Preliminary results of the first 164 participants (between 19 and 24 per cell) highlight which repair strategies are effective relative to ability, integrity and benevolence and the robot’s anthropomorphism. Overall, this paper contributes to the HRI trust repair literature.

Automated Vehicle (AV) buses hold great potential, yet it is not clear if Americans will choose to ride them. Trust and attitudes, often influenced by individual differences, are vital predictors of technology acceptance and AVs are no exception. To deepen our understanding of individual differences as they pertain to AV buses, this paper presents the results of a national survey of 401 participants located in the United States of America. Findings from this survey indicate that individual differences influenced trust, attitude, and intention to ride AV buses. Specifically, trust in AV buses differed by individual's age and bus riding frequency while attitudes toward AV buses differed by individual's age, ethnicity, and bus riding frequency. Finally, intention to ride an AV bus differed by age, gender, ethnicity, and bus riding frequency. Based on these results, we propose a research agenda that seeks to inform future research on acceptance of AV buses.

Robots are becoming an important way to deliver health care, and personality is vital to understanding their effectiveness. Despite this, there is a lack of a systematic overarching understanding of personality in health care human robot interaction (H-HRI). To address this, the authors conducted a review that identified 18 studies on personality in H-HRI. This paper presents the results of that systematic literature review. Insights are derived from this review regarding the methodologies, outcomes, and samples utilized. The authors of this review discuss findings across this literature while identifying several gaps worthy of attention. Overall, this paper is an important starting point in understanding personality in H-HRI.

Human-robot teams offer both benefits and new challenges. Human robot teams combine the advantages of automation such as high accuracy, speed, and repeat-ability with the flexibility, adaptability, and creative problem-solving commonly associated with humans. Several challenges, however, must first be addressed to effectively leverage such teams. One challenge is understanding effective human-robot team design (HRTD). HRTD is vital as the wrong team can lead to potentially negative outcomes. The theoretical model and methodology presented are the planned first steps towards the establishment of guidelines based on statistical models that can recommend an optimal human-robot team design based on a given set of criteria.

Computer cursor control using electroencephalogram (EEG) signals is a common and well-studied brain-computer interface (BCI). The emphasis of the literature has been primarily on evaluation of the objective measures of assistive BCIs such as accuracy of the neural decoder whereas the subjective measures such as user's satisfaction play an essential role for the overall success of a BCI. As far as we know, the BCI literature lacks a comprehensive evaluation of the usability of the mind-controlled computer cursor in terms of decoder efficiency (accuracy), user experience, and relevant confounding variables concerning the platform for the public use. To fill this gap, we conducted a two-dimensional EEG-based cursor control experiment among 28 healthy participants. The computer cursor velocity was controlled by the imagery of hand movement using a paradigm presented in the literature named imagined body kinematics (IBK) with a low-cost wireless EEG headset. We evaluated the usability of the platform for different objective and subjective measures while we investigated the extent to which the training phase may influence the ultimate BCI outcome. We conducted pre- and post- BCI experiment interview questionnaires to evaluate the usability. Analyzing the questionnaires and the testing phase outcome shows a positive correlation between the individuals' ability of visualization and their level of mental controllability of the cursor. Despite individual differences, analyzing training data shows the significance of electrooculogram (EOG) on the predictability of the linear model. The results of this work may provide useful insights towards designing a personalized user-centered assistive BCI.