This was the first of a series of experiments to investigate whether adults respond to a robot’s feedback as effectively as to a human’s during a virtual-reality game. Twenty adults volunteered to pop bubbles in 7 virtual-reality games, including 2 in baseline, 3 in acquisition, and 2 in extinction. Participants were randomized to receive feedback from a robot or a human during acquisition. The 80% of the average movement time at baseline was used as the target time to determine the feedback content. Feedback was the same except for the agent (robot or human). Results indicated adults responding to a robot’s feedback to shape their reaching during acquisition and extinction had faster reaching than baseline; however, their patterns were jerkier, less straight, and slower than participants’ receiving a human’s feedback. This study shows the potential to use robots as the feedback provider, though participants performed slightly better with human feedback.

Previous research has shown that robot mistakes or malfunctions have a significant negative impact on people’s trust. One way to mitigate the negative impact of trust violation is through trust repair. Although trust repair has been studied extensively, it is still not known which strategy is effective in repairing trust in a time-sensitive driving scenario. Additionally, prior research on trust repair has not dealt with the effects of expressing emotion in attempting trust repair. In this paper, we presented the development of a variety of trust repair methods for a time-sensitive scenario using a simulated driving environment as a testbed for validation. These trust repair methods included baseline apology, emotional apology, and explanation. We conducted an experiment to compare the impact of these trust repair methods on human-robot trust. Experimental results indicated that the emotional apology positively affected more participants than the no-repair, baseline apology, and explanation. Furthermore, this study identified emotional apology as the most effective method for the time-sensitive driving scenario.

Trust is a key element for successful human-robot interaction. One challenging problem in this domain is the issue of how to construct a formulation that optimally models this trust phenomenon. This paper presents a framework for modeling human-robot trust based on representing the human decision-making process as a formulation based on trust states. Using this formulation, we then discuss a generalized model of human-robot trust based on Hidden Markov Models and Logistic Regression. The proposed approach is validated on datasets collected from two different human subject studies in which the human is provided the ability to take advice from a robot. Both experimental scenarios were time-sensitive, in that a decision had to be made by the human in a limited time period, but each scenario featured different levels of cognitive load. The experimental results demonstrate that the proposed formulation can be utilized to model trust, in which the system can predict whether the human will decide to take advice (or not) from the robot. It was found that our prediction performance degrades after the robot made a mistake. The validation of this approach on two scenarios implies that this model can be applied to other interactive scenarios as long as the interaction dynamics fits into the proposed formulation. Directions for future improvements are discussed.

Trust is an important characteristic of successful interactions between humans and agents in many scenarios. Self-driving scenarios are of particular relevance when discussing the issue of trust due to the high-risk nature of erroneous decisions being made. The present study aims to investigate decision-making and aspects of trust in a realistic driving scenario in which an autonomous agent provides guidance to humans. To this end, a simulated driving environment based on a college campus was developed and presented. An online and an in-person experiment were conducted to examine the impacts of mistakes made by the self-driving AI agent on participants' decisions and trust. During the experiments, participants were asked to complete a series of driving tasks and make a sequence of decisions in a time-limited situation. Behavior analysis indicated a similar relative trend in the decisions across these two experiments. Survey results revealed that a mistake made by the self-driving AI agent at the beginning had a significant impact on participants' trust. In addition, similar overall experience and feelings across the two experimental conditions were reported. The findings in this study add to our understanding of trust in human-robot interaction scenarios and provide valuable insights for future research work in the field of human-robot trust.

Several HRI studies have investigated the use of interactive social robots to enhance a variety of activities designed for children in recent years. Notably, these robots have shown the ability to facilitate greater engagement with rehabilitative therapy exercises. Yet, very few studies have directly compared the performance of a robotic therapist with that of a human therapist during therapy sessions. This type of analysis is even less common for studies involving children. This work presents the experimental results of a between subjects study conducted with 10 children who interacted with either a robot or human therapist while playing a virtual reality rehabilitation game. The results are analyzed in terms of user intrinsic motivation and compared to previous work with adult participants. Preliminary results show a trend that children enjoyed working with the robot therapist more than the human therapist while playing the game. This finding is consistent with related work within the domain of child-robot interaction and supports the continued investigation of the potential uses of social robots in therapeutic rehabilitation protocols for children.

From personal robot assistant to self-driving vehicles, artificial intelligence (AI) is the backbone underlying millions of future advanced applications. As robots become increasingly pervasive in daily life, it is expected that robots will augment human laborers in many domains in the near future. When robots are deployed in the real world, the underlying assumption is that they are capable of accomplishing their given tasks. However, researchers have shown that robots made mistakes, and in several cases, humans tend to overtrust robotic systems (Abney 2017; Borenstein et al. 2017; Robinette, Howard, and Wagner 2017). Overtrust of a robot happens in scenarios where (1) a person accepts risk because that person believes the robot can perform a function that it cannot or (2) the person accepts too much risk because the expectation is that the system will mitigate the risk." (Abney 2017). In particular, we are interested in two emerging domains where an appropriate amount of trust is a minimal requirement and overtrust could cause harm: 1) healthcare scenarios and 2) self-driving car (i.e. autonomous driving) scenarios. Both healthcare and autonomous driving scenarios often involve high risks, and the negative outcomes could be detrimental to the user. The objective of our research focuses on 1) investigating the causes that contribute to human overtrust of these robots systems 2) developing a behavior-based computational model to predict overtrust, and 3) developing techniques to mitigate outcomes caused by the overtrust.

With the recent advances in computing, artificial intelligence (AI) is quickly becoming a key component in the future of advanced applications. In one application in particular, AI has played a major role - that of revolutionizing traditional healthcare assistance. Using embodied interactive agents, or interactive robots, in healthcare scenarios has emerged as an innovative way to interact with patients. As an essential factor for interpersonal interaction, trust plays a crucial role in establishing and maintaining a patient-agent relationship. In this paper, we discuss a study related to healthcare in which we examine aspects of trust between humans and interactive robots during a therapy intervention in which the agent provides corrective feedback. A total of twenty participants were randomly assigned to receive corrective feedback from either a robotic agent or a human agent. Survey results indicate trust in a therapy intervention coupled with a robotic agent is comparable to that of trust in an intervention coupled with a human agent. Results also show a trend that the agent condition has a medium-sized effect on trust. In addition, we found that participants in the robot therapist condition are 3.5 times likely to have trust involved in their decision than the participants in the human therapist condition. These results indicate that the deployment of interactive robot agents in healthcare scenarios has the potential to maintain quality of health for future generations.

The use of socially interactive robots in people's homes and workplaces will continue to grow. To test such systems before deployment, most human-robot interaction experiments are usually conducted in a research laboratory or require the physical presence of the robot. These experiments are usually time-consuming, costly, and, in most cases, require an experimenter to be present. To address this problem, we have developed a web-based research platform in which participants can remotely interact with a socially interactive robot shown via video based on a behavior profile programmed via a Wizard-of-Oz framework. To help us validate the believability when using this platform, we compared participant's responses to a physical presence robot with that to a remote presence robot during a problem-solving scenario. Participants were asked to complete a series of cognitive tasks with the help of the robot. We analyzed participant's tendency to take the robot's advice, their feelings before and after the experiment, and a quantitative assessment about believability. The experimental results indicate that there was a high degree of agreement between participants' behaviors in the two conditions and only minor changes in feelings before and after the experiment. The survey responses found no significant difference in their overall rating for believability, suggesting the equivalency of believability between the two conditions.

With recent advances in robotics, it is expected that robots will become increasingly common in human environments, such as in the home and workplaces. Robots will assist and collaborate with humans on a variety of tasks. During these collaborations, it is inevitable that disagreements in decisions would occur between humans and robots. Among factors that lead to which decision a human should ultimately follow, theirs or the robot, trust is a critical factor to consider. This study aims to investigate individuals' behaviors and aspects of trust in a problem-solving situation in which a decision must be made in a bounded amount of time. A between-subject experiment was conducted with 100 participants. With the assistance of a humanoid robot, participants were requested to tackle a cognitive-based task within a given time frame. Each participant was randomly assigned to one of the following initial conditions: 1) a working robot in which the robot provided a correct answer or 2) a faulty robot in which the robot provided an incorrect answer. Impacts of the faulty robot behavior on participant's decision to follow the robot's suggested answer were analyzed. Survey responses about trust were collected after interacting with the robot. Results indicated that the first impression has a significant impact on participant's behavior of trusting a robot's advice during a disagreement. In addition, this study discovered evidence supporting that individuals still have trust in a malfunctioning robot even after they have observed a robot's faulty behavior.