Trust In Human-robot Interaction Research Articles

Do Humans Trust Robots that Violate moral trust?

The increasing use of robots in social applications requires further research on human-robot trust. The research on human-robot trust needs to go beyond the conventional definition that mainly focuses on how human-robot relations are influenced by robot performance. The emerging field of social robotics considers optimizing a robot’s personality a critical factor in user perceptions of experienced human-robot interaction (HRI). Researchers have developed trust scales that account for different dimensions of trust in HRI. These trust scales consider one performance aspect (i.e., the trust in an agent’s competence to perform a given task and their proficiency in executing the task accurately) and one moral aspect (i.e., trust in an agent’s honesty in fulfilling their stated commitments or promises) for human-robot trust. The question that arises here is to what extent do these trust aspects affect human trust in a robot? The main goal of this study is to investigate whether a robot’s undesirable behavior due to the performance trust violation would affect human trust differently than another similar undesirable behavior due to a moral trust violation. We designed and implemented an online human-robot collaborative search task that allows distinguishing between performance and moral trust violations by a robot. We ran these experiments on Prolific and recruited 100 participants for this study. Our results showed that a moral trust violation by a robot affects human trust more severely than a performance trust violation with the same magnitude and consequences.

Trust dynamics in human interaction with an industrial robot

ABSTRACT Trust is important for collaboration. In hybrid teams of humans and robots, trust enables smooth collaboration and reduces risks. Just as collaboration between humans and robots differs from interpersonal collaboration, so does the nature of trust in human-robot interaction (HRI). Therefore, further investigations on trust formation and dissolution in HRI, factors affecting it, and means for keeping trust on an appropriate level are needed. However, our knowledge of interpersonal trust and trust in autonomous agents cannot be transferred directly to HRI. In this paper, we present a study with 32 participants on trust formation and dissolution as well as forecasting to influence trust in an industry robot. Results show differences in dynamics and factors of trust formation and dissolution. Additionally, we find that the effect of forecasting on trust depends on task success. These findings support the design of trustful human-robot interaction and corresponding robotic team members.

A matter of consequences

Abstract On reviewing the literature regarding acceptance and trust in human-robot interaction (HRI), there are a number of open questions that needed to be addressed in order to establish effective collaborations between humans and robots in real-world applications. In particular, we identified four principal open areas that should be investigated to create guidelines for the successful deployment of robots in the wild. These areas are focused on: (1) the robot’s abilities and limitations; in particular when it makes errors with different severity of consequences, (2) individual differences, (3) the dynamics of human-robot trust, and (4) the interaction between humans and robots over time. In this paper, we present two very similar studies, one with a virtual robot with human-like abilities, and one with a Care-O-bot 4 robot. In the first study, we create an immersive narrative using an interactive storyboard to collect responses of 154 participants. In the second study, 6 participants had repeated interactions over three weeks with a physical robot. We summarise and discuss the findings of our investigations of the effects of robots’ errors on people’s trust in robots for designing mechanisms that allow robots to recover from a breach of trust. In particular, we observed that robots’ errors had greater impact on people’s trust in the robot when the errors were made at the beginning of the interaction and had severe consequences. Our results also provided insights on how these errors vary according to the individuals’ personalities, expectations and previous experiences.

Influence of Social Robots’ Benevolence and Competence on Perceived Trust in Human-Robot Interactions

Influence of Social Robots’ Benevolence and Competence on Perceived Trust in Human-Robot Interactions

Complexity-Driven Trust Dynamics in Human–Robot Interactions: Insights from AI-Enhanced Collaborative Engagements

This study explores the intricate dynamics of trust in human–robot interaction (HRI), particularly in the context of modern robotic systems enhanced by artificial intelligence (AI). By grounding our investigation in the principles of interpersonal trust, we identify and analyze both similarities and differences between trust in human–human interactions and human–robot scenarios. A key aspect of our research is the clear definition and characterization of trust in HRI, including the identification of factors influencing its development. Our empirical findings reveal that trust in HRI is not static but varies dynamically with the complexity of the tasks involved. Notably, we observe a stronger tendency to trust robots in tasks that are either very straightforward or highly complex. In contrast, for tasks of intermediate complexity, there is a noticeable decline in trust. This pattern of trust challenges conventional perceptions and emphasizes the need for nuanced understanding and design in HRI. Our study provides new insights into the nature of trust in HRI, highlighting its dynamic nature and the influence of task complexity, thereby offering a valuable reference for future research in the field.

The Social Triad Model: Considering the Deployer in a Novel Approach to Trust in Human–Robot Interaction

There is an increasing interest in considering, measuring, and implementing trust in human-robot interaction (HRI). New avenues in this field include identifying social means for robots to influence trust, and identifying social aspects of trust such as a perceptions of robots’ integrity, sincerity or even benevolence. However, questions remain regarding robots’ authenticity in obtaining trust through social means and their capacity to increase such experiences through social interaction with users. We propose that the dyadic model of HRI misses a key complexity: a robot’s trustworthiness may be contingent on the user’s relationship with, and opinion of, the individual or organisation deploying the robot (termed here, Deployer). We present a case study in three parts on researching HRI and a LEGO® Serious® Play focus group on care robotics to indicate how Users’ trust towards the Deployer can affect trust towards robots and robotic research. Our Social Triad model (User, Robot, Deployer) offers novel avenues for exploring trust in a social context.

Neurorehabilitation robotics: how much control should therapists have?

Robotic technologies for rehabilitating motor impairments from neurological injuries have been the focus of intensive research and capital investment for more than 30 years. However, these devices have failed to convincingly demonstrate greater restoration of patient function compared to conventional therapy. Nevertheless, robots have value in reducing the manual effort required for physical therapists to provide high-intensity, high-dose interventions. In most robotic systems, therapists remain outside the control loop to act as high-level supervisors, selecting and initiating robot control algorithms to achieve a therapeutic goal. The low-level physical interactions between the robot and the patient are handled by adaptive algorithms that can provide progressive therapy. In this perspective, we examine the physical therapist's role in the control of rehabilitation robotics and whether embedding therapists in lower-level robot control loops could enhance rehabilitation outcomes. We discuss how the features of many automated robotic systems, which can provide repeatable patterns of physical interaction, may work against the goal of driving neuroplastic changes that promote retention and generalization of sensorimotor learning in patients. We highlight the benefits and limitations of letting therapists physically interact with patients through online control of robotic rehabilitation systems, and explore the concept of trust in human-robot interaction as it applies to patient-robot-therapist relationships. We conclude by highlighting several open questions to guide the future of therapist-in-the-loop rehabilitation robotics, including how much control to give therapists and possible approaches for having the robotic system learn from therapist-patient interactions.

Effect of Different Listening Behaviors of Social Robots on Perceived Trust in Human-robot Interactions

With the increased use of social robots in prominence and beyond functional performance, they are expected to foster trust and confidence in people. Various factors involve providing social robots with more trustworthy behavior. This study investigated whether the listening behavior of a social robot can affect the perception of being trustworthy in human–robot interaction. Therefore, we designed four different listening behaviors, including nonactive listening, active listening, active empathic listening, and verbal-only empathic listening, for a social robot and evaluated the impact of each behavior on the participants’ likelihood of trusting the robot, using a between-subject design. Participants in the four conditions conversed with a robot that simulated one of the listening behaviors, and their general, cognitive and affective trust toward the robot was measured. The results indicated that active empathic listening behavior provided the participants with the highest impression of trustworthiness, specifically in affective trust. Both active listening and active empathic listening were evaluated higher than nonactive listening in general, affective, and cognitive trust. However, active empathic listening behavior was differentiated from active listening behavior only in terms of affective trust. For verbal and nonverbal dimensions of listening behaviors, it was confirmed that nonverbal behaviors such as nodding, body movement, and eye gaze along with verbal behaviors, had a significant effect in eliciting higher affective trust in human-robot interaction. Consequently, we concluded that designing social robots with active (empathic) listening behavior can enhance trust perception in human-robot interaction in different fields such as education, healthcare, and business.

“Sorry, it was my fault”: Repairing trust in human-robot interactions

The current study develops a three-fold categorization (i.e., logic, semantic, and syntax failures) for technical failures that are commonly observed in human-robot interactions (HRI), and investigates it along with four trust repair strategies: internal-attribution apology, external-attribution apology, denial, and no repair. The 743 observations conducted through an online experiment reveal some nuances in participants’ perceprions of competence- and integrity-based trust violations, which may reflect ontological differences between humans and machines. The analysis also shows significant main effects of failure types and repair methods on HRI-based trust.

A Trust Scale for Human-Robot Interaction: Translation, Adaptation, and Validation of a Human Computer Trust Scale

Recently there has been an increasing demand for technologies (automated and intelligent machines) that brings benefits to organizations and society. Similar to the widespread use of personal computers in the past, today’s needs are towards facilitating human-machine technology appropriation, especially in highly risky and regulated industries like robotics, manufacturing, automation, military, finance, or healthcare. In this context, trust can be used as a critical element to instruct how human-machine interaction should occur. Considering the context-dependency and multidimensional trust, this study seeks to find a way to measure the effects of perceived trust in a collaborative robot (cobot), regardless of its literal credibility as a real person. This article aims at translating, adapting, and validating a Human-Computer Trust Scale (HCTM) in human-robot interaction (HRI) context and its application to cobots. The Human-Robot Interaction Trust Scale (HRITS) involved 239 participants and included eleven items. The 2nd order CFA with a general factor called “trust” have proven to be empirically robust ( CFI = .94 ; TLI = .93 ; SRMR = .04 ; and RMSEA = .05 ) [ CR = .84 ; AVE = .58 , and MaxR H = .92 ]; results indicated a good measurement of the general factor trust, and the model satisfied the criteria for measure trust. An analysis of the differences in perceptions of trust by gender was conducted using a t -test. This analysis showed that statistical differences by gender exist ( p = .04 ). This study’s results allowed for a better understanding of trust in HRI, specifically regarding cobots. The validation of a Portuguese scale for trust assessment in HRI can give a valuable contribution to designing collaborative environments between humans and robots.

Human-Likeness of Feedback Gestures Affects Decision Processes and Subjective Trust

Trust is fundamental in building meaningful social interactions. With the advance of social robotics in collaborative settings, trust in Human–Robot Interaction (HRI) is gaining more and more scientific attention. Indeed, understanding how different factors may affect users’ trust toward robots is of utmost importance. In this study, we focused on two factors related to the robot’s behavior that could modulate trust. In a two-forced choice task where a virtual robot reacted to participants’ performance, we manipulated the human-likeness of the robot’s motion and the valence of the feedback it provided. To measure participant’s subjective level of trust, we used subjective ratings throughout the task as well as a post-task questionnaire, which distinguishes capacity and moral dimensions of trust. We expected the presence of feedback to improve trust toward the robot and human-likeness to strengthen this effect. Interestingly, we observed that humans equally trust the robot in most conditions but distrust it when it shows no social feedback nor human-like behavior. In addition, we only observed a positive correlation between subjective trust ratings and the moral and capacity dimensions of trust when robot was providing feedback during the task. These findings suggest that the presence and human-likeness of feedback behaviors positively modulate trust in HRI and thereby provide important insights for the development of non-verbal communicative behaviors in social robots.

The Influence of Robots' Fairness on Humans' Reward-Punishment Behaviors and Trust in Human-Robot Cooperative Teams.

Based on social exchange theory, this study investigates the effects of robots' fairness and social status on humans' reward-punishment behaviors and trust in human-robot interactions. In human-robot teamwork, robots show fair behaviors, dedication (altruistic unfair behaviors), and selfishness (self-interested unfair behaviors), but few studies have discussed the effects of these robots' behaviors on teamwork. This study adopts a 3 (the independent variable is the robot's fairness: self-interested unfair behaviors, fair behaviors, and altruistic unfair behaviors) × 3 (the moderator variable is the robot's social status: superior, peer, and subordinate) experimental design. Each participant and a robot completed the experimental task together through a computer. When robots have different social statuses, the more altruistic the fairness of the robot, the more reward behaviors, the fewer punishment behaviors, and the higher human-robot trust of humans. Robots' higher social status weakens the influence of their fairness on humans' punishment behaviors. Human-robot trust will increase humans' reward behaviors and decrease humans' punishment behaviors. Humans' reward-punishment behaviors will increase repaired human-robot trust. Robots' fairness has a significant impact on humans' reward-punishment behaviors and trust. Robots' social status moderates the effect of their fair behavior on humans' punishment behavior. There is an interaction between humans' reward-punishment behaviors and trust. The study can help to better understand the interaction mechanism of the human-robot team and can better serve the management and cooperation of the human-robot team by appropriately adjusting the robots' fairness and social status.

Perception of Robot Power: Scale Development

Robots are increasingly utilized to work with humans in collaborative tasks. While there is a growing body of research investigating individual measures that impact human-robot interaction (HRI), to our knowledge, no measure exists to quantify an individual's perception of robot power. How powerful one perceives robots could be a driving factor in an individual's attitudes toward robots and their trust in HRI. This study aims to develop and validate a scale to quantify peoples' general perception of the power of robots. Preliminary results from exploratory factor analyses with nearly 60% of planned participants revealed three potential factors: companion/task robots, social coworking robots, and domineering robots. Future work will examine whether and how the scale predicts behavior to continue to refine the scale and isolate its measurement.

An Immersive Investment Game to Study Human-Robot Trust.

As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilize two Neuro-Inspired COmpanion (NICO) - robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of non-verbal communication. We observe a correlation of 0.43 () between self-assessed trust and trust measured from the game, and a positive impact of non-verbal communication on trust () and robot perception for anthropomorphism () and animacy (). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human’s trust in robots.

The collaborative mind: intention reading and trust in human-robot interaction

SummaryRobots are likely to become important social actors in our future and so require more human-like ways of assisting us. We state that collaboration between humans and robots is fostered by two cognitive skills: intention reading and trust. An agent possessing these abilities would be able to infer the non-verbal intentions of others and to evaluate how likely they are to achieve their goals, jointly understanding what kind and which degree of collaboration they require. For this reason, we propose a developmental artificial cognitive architecture that integrates unsupervised machine learning and probabilistic models to imbue a humanoid robot with intention reading and trusting capabilities. Our experimental results show that the synergistic implementation of these cognitive skills enable the robot to cooperate in a meaningful way, with the intention reading model allowing a correct goal prediction and with the trust component enhancing the likelihood of a positive outcome for the task.

Introduction: The evolution of trust in human-robot interaction

Introduction: The evolution of trust in human-robot interaction

Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach

The current research proposed and tested a structural equation model (SEM) that describes hypothesized relationships among factors affecting trust in human-robot interaction (HRI) such as trustworthiness, human-likeness, intelligence, perfect automation schema (PAS), and affect. A video stimulus depicting an autonomous guard robot interacting with humans was employed as a stimulus via Amazon's Mechanical Turk to recruit 233 participants. Human-related and robot-related metrics were found to affect trustworthiness that subsequently affected trust. In particular, ability (as a trustworthiness facet) was a dominant factor affecting trust in HRI. Integrity was found to mediate the relationships between robot- and human-related metrics and trustworthiness. This study also showed a correlation between intelligence and trustworthiness, as well as between PAS and trustworthiness. The findings of the present study have significant implications for both theory and practice on factors and levels that affect trust in HRI.

On the impact of different types of errors on trust in human-robot interaction

Abstract Trust is a key dimension of human-robot interaction (HRI), and has often been studied in the HRI community. A common challenge arises from the difficulty of assessing trust levels in ecologically invalid environments: we present in this paper two independent laboratory studies, totalling 160 participants, where we investigate the impact of different types of errors on resulting trust, using both behavioural and subjective measures of trust. While we found a (weak) general effect of errors on reported and observed level of trust, no significant differences between the type of errors were found in either of our studies. We discuss this negative result in light of our experimental protocols, and argue for the community to move towards alternative methodologies to assess trust.

Social Conformity Effects on Trust in Simulation-Based Human-Robot Interaction.

We investigated the co-acting influences of communication and social conformity on trust in human-robot interaction. Previous work has investigated aspects of the robot, the human, and the environment as influential factors in the human-robot relationship. Little work has examined the conjoint effects of social conformity and communication on this relationship. As social conformity and communication have been shown to affect human-human trust, there are a priori reasons to believe that they will play an influential role in human-robot trust also. The experiment examined the influences of social conformity and robot communication on trust. A 2 × 2 (communication × social group) design was implemented with each variable having two levels (communication, no communication; positive social group, negative social group). We created a communication manipulation which we then demonstrated to mediate the trust level between human and robot. However, this influence on trust was overcome by social information in which the subsequent trust level, attributed to the robot, was dominated by expressed social group attitudes to that robot. The results confirm the importance of human social assessments over direct robot communication in setting human-robot trust levels. When social opinions are expressed, observers appear to conform to the trust displayed by the group than relying on their own judgment. In human-robot teams, the perceptions of the group may exert a greater impact than even robot communication. This may be especially important when new human members are introduced into such teams.

Social robots in rehabilitation: A question of trust.

Social robots can help meet the growing need for rehabilitation assistance; measures for creating and maintaining trust in human-robot interactions should be priorities when designing social robots for rehabilitation.