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.

Trust calibration is a challenge in human-robot interaction (HRI). Miscalibrated trust can result in overreliance or distrust of robotic systems, ultimately reducing the effectiveness of collaboration. This paper presents a novel AI-driven approach to trust calibration that integrates adaptive verbal apologies by incorporating user feedback. A QT Robot dynamically adjusts its responses based on an identification of the specific error by a user. The robot then generates an apology that incorporates this feedback using ChatGPT. We conducted a CAPTCHA-based assisted decision-making experiment with 40 participants to determine whether adaptive apology improves trust more than static apology. The levels of trust before and after the interaction were measured using the Multidimensional Measure of Trust (MDMT) survey. The results indicate that adaptive trust repair generated by LLM significantly improved user perceptions in the dimensions of reliability, transparency and dependability. These findings demonstrate the effectiveness of personalized, real-time trust interventions and contribute to the growing body of research on trust calibration by introducing a dynamic, adaptive system that enhances collaboration and trust adaptation.

The growing implementation of robots in societal contexts necessitates a deeper exploration of the dynamics of trust between humans and robots. This exploration should expand beyond traditional viewpoints that primarily emphasize the influence of robot performance. In the burgeoning area of social robotics, fine-tuning a robot’s personality traits is increasingly recognized as a crucial element in shaping users’ experiences during human-robot interaction (HRI). Research in this field has led to the creation of trust scales that encompass various trust dimensions in HRI. These scales include aspects related to performance as well as moral dimensions. Our previous study revealed that breaches of moral trust by robots impact human trust more negatively than performance trust breaches, and humans take retaliatory approaches in response to morality breaches by robots. In the present study, our main aim was to explore if trust loss and retaliation tendencies differ based on the identity of the teammates following the violations of these different trust aspects. Through multiple versions of an online search task, we examined our research questions and found that breaches of morality by robotic teammates cause a significantly higher trust loss in humans compared to human teammates. These findings highlight the importance of a robot’s morality in determining how humans view a robot’s trustworthiness. For effective robot design, robots must meet ethical and moral standards, which are higher than the ethical and moral standards expected from humans.

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.

This paper describes a set of software modules and algorithms for maritime object detection and tracking. The approach described here is designed to work in conjunction with various sensors from a maritime surface vessel (e.g. marine RADAR, LIDAR, camera). The described system identifies obstacles from the input sensors, estimates their state, and fuses the obstacle data into a consolidated report. The system is verified using experiments conducted on a live system and successfully demonstrates the ability to detect and track obstacles up to 450m away while operating at 7 fps. The software is open source and available at https://github.com/uml-marine-robotics/asv_perception.

The HRI community is working to develop interactive robots for a wide variety of pro-social tasks and ideals. As such we naturally focus on the positive side of HRI including how robots and humans may collaborate and the benefits of doing so. This workshop, in contrast, will focus on the dark side of HRI with the goal of identifying, understanding and guarding against the potential negative consequences of interactive robots. The primary objective of the workshop is to articulate and discuss the most pertinent ethical issues facing the HRI community and to develop a set of common community guidelines.

In this paper, we introduce publicly available human-robot teaming datasets captured during the summer 2018 season using our Aquaticus testbed. Our Aquaticus testbed is designed to examine the interactions between human-human and human-robot teammates while situated in the marine environment in their own vehicles. In particular, we assess these interactions while humans and fully autonomous robots play a competitive game of capture the flag on the water. Our testbed is unique in that the humans are situated in the field with their fully autonomous robot teammates in vehicles that have similar dynamics. Having a competition on the water reduces the safety concerns and cost of performing similar experiments in the air or on the ground. By having the competitions on the water, we create a complex, dynamic, and partially observable view of the world for participants while in their motorized kayak. The main modality for teammate interaction is audio to better simulate the experience of real-world tactical situations - ie fighter pilots talking to each other over radios. We have released our complete datasets publicly so that we can enable researchers throughout the HRI community that do not have access to such a testbed and may have expertise other than our own to leverage our datasets to perform their own analysis and contribute to the HRI community.

Robotic rescuers digging through rubble, fire-fighting drones flying over populated areas, robotic servers pouring hot coffee for you, and a nursing robot checking your vitals are all examples of current or near-future situations where humans and robots are expected to interact in a dangerous situation. Dangerous HRI is an as-yet understudied area of the field. We define dangerous HRI as situations where humans experience some amount of risk of bodily harm while interacting with robots. This interaction could take many forms, such as a bystander (e.g. when an autonomous car waits at a crossing for a pedestrian), as a recipient of robotic assistance (rescue robots), or as a teammate (like an autonomous robot working with a SWAT team). To facilitate better study of this area, the Dangerous HRI workshop brings together researchers who perform experiments with some risk of bodily harm to participants and discuss strategies for mitigating this risk while still maintaining validity of the experiment. This workshop does not aim to tackle the general problem of human safety around robots, but instead focused on guidelines for and experience from experimenters.

Project Aquaticus is a human-robot teaming competition on the water involving autonomous surface vehicles and human operated motorized kayaks. Teams composed of both humans and robots share the same physical environment to play capture the flag. In this paper, we present results from seven competitions of our half-court (one participant versus one robot) game. We found that participants indicated more trust in more aggressive behaviors from robots.

This article presents a conceptual framework for human-robot trust which uses computational representations inspired by game theory to represent a definition of trust, derived from social psychology. This conceptual framework generates several testable hypotheses related to human-robot trust. This article examines these hypotheses and a series of experiments we have conducted which both provide support for and also conflict with our framework for trust. We also discuss the methodological challenges associated with investigating trust. The article concludes with a description of the important areas for future research on the topic of human-robot trust.

Robots have the potential to save lives in high-risk situations, such as emergency evacuations. To realize this potential, we must understand how factors such as the robot's performance, the riskiness of the situation, and the evacuee's motivation influence his or her decision to follow a robot. In this paper, we developed a set of experiments that tasked individuals with navigating a virtual maze using different methods to simulate an evacuation. Participants chose whether or not to use the robot for guidance in each of two separate navigation rounds. The robot performed poorly in two of the three conditions. The participant's decision to use the robot and self-reported trust in the robot served as dependent measures. A 53% drop in self-reported trust was found when the robot performs poorly. Self-reports of trust were strongly correlated with the decision to use the robot for guidance (φ(90) = +0.745). We conclude that a mistake made by a robot will cause a person to have a significantly lower level of trust in it in later interactions.

Robots have the potential to save lives in emergency scenarios, but could have an equally disastrous effect if participants overtrust them. To explore this concept, we performed an experiment where a participant interacts with a robot in a non-emergency task to experience its behavior and then chooses whether to follow the robot's instructions in an emergency or not. Artificial smoke and fire alarms were used to add a sense of urgency. To our surprise, all 26 participants followed the robot in the emergency, despite half observing the same robot perform poorly in a navigation guidance task just minutes before. We performed additional exploratory studies investigating different failure modes. Even when the robot pointed to a dark room with no discernible exit the majority of people did not choose to safely exit the way they entered.

Most Human-Robot Interaction (HRI) experiments are costly and time consuming because they involve deploying a physical robot in a physical space. Experiments using virtual environments can be easier and less expensive, but it is difficult to ensure that the results will be valid in the physical domain. To begin to answer this concern, we have performed an evaluation comparing participants' understanding of robotic guidance instructions using robots that were virtually, remotely, or physically present for the experiment. All but one set of experimental conditions gave similar results across the three presence levels. Further, we find that qualitative responses about the robots were largely the same regardless of presence level.

Motivated by the desire to mitigate human casualties in emergency situations, this paper explores various guidance modalities provided by a robotic platform for instructing humans to safely evacuate during an emergency. We focus on physical modifications of the robot, which enables visual guidance instructions, since auditory guidance instructions pose potential problems in a noisy emergency environment. Robotic platforms can convey visual guidance instructions through motion, static signs, dynamic signs, and gestures using single or multiple arms. In this paper, we discuss the different guidance modalities instantiated by different physical platform constructs and assess the abilities of the platforms to convey information related to evacuation. Human-robot interaction studies with 192 participants show that participants were able to understand the information conveyed by the various robotic constructs in 75.8% of cases when using dynamic signs with multi-arm gestures, as opposed to 18.0% when using static signs for visual guidance. Of interest to note is that dynamic signs had equivalent performance to single-arm gestures overall but drastically different performances at the two distance levels tested. Based on these studies, we conclude that dynamic signs are important for information conveyance when the robot is in close proximity to the human but multi-arm gestures are necessary when information must be conveyed across a greater distance.

Inspired by large fatality rates due to fires in crowded areas and the increasing presence of robots in dangerous emergency situations, we have implemented a model of information propagation among evacuees. Information about the locations of exits and the relative confidence of the individual in the location of the exit disseminated through a simulated crowd of people during an evacuation modeled after The Station Nightclub fire of 2003. True believers were added to this system as individuals who refused to accept exit information from others, instead preferring to head to their own exit. This system was then tested to find what percentage of true believers most likely existed in the actual fire. Using this true believer percentage, robots were added to the environment to guide evacuees to the nearest exit. The number of people who believed a robot's instructions was varied to find what percentage of people need to trust these robots in order to exploit information propagation and thus increase survivability. As a lower bound, we have found that 30% of the evacuees should believe a robot's instructions to significantly increase survival rates.

Evacuating a building in an emergency situation can be very confusing and dangerous. Exit signs are static and thus have no ability to convey information about congestion or danger between the sign and the actual exit door. Emergency personnel may arrive too late to assist in an evacuation. Robots, however, can be stored inside of buildings and can be used to guide evacuees to the best available exit. To enable this process, evacuation robots must have an understanding of how people react in emergency situations. By incorporating a model of human panic behavior, these robots can effectively guide crowds of people to zones of safety. In this paper, we discuss an initial design of these robots and their behaviors. Preliminary simulation results show that a significantly larger proportion of people are evacuated with robot assistance than without.

We propose a new research organization management paradigm to increase throughput of projects by allowing researchers to choose their own projects through self-organization. Our methods draw upon the field of Agent-Based computational social science where Artificial Life and simulated societies have been used to study complex systems including economies and financial markets. Modeling the researchers as individual agents, we simulate our new management structure against a more traditional organization where the researchers are broken into departments based on their skills and assigned projects by management. Our results, measuring the amount of time it takes a research organization to serve a given number of contracts, show promise in the less hierarchical approach.

LabRat™is an autonomous, self-contained mobile robot kit with batteries, motors, two bumper whisker sensors, and three infrared proximity sensors that double as channels for “Rat-to-Rat” communication. The vehicle determines its position with an optical sensor that detects movement in both lateral directions. The LabRat™design is completely open source, including software examples and libraries. LabRat™is designed to fit inside the body of a computer mouse and has applications in the classroom, the lab and the home. The device has been successfully used in an undergraduate robotics class.

The NetFlix Prize is a research contest that will award $1 Million to the first group to improve NetFlix’s movie recommendation system by 10%. Contestants are given a dataset containing the movie rating histories of customers for movies. From this data, a processing scheme must be developed that can predict how a customer will rate a given movie on a scale of 1 to 5. An architecture is presented that utilizes the Fuzzy-Adaptive Resonance Theory clustering method to create an interesting set of data attributes that are input to a neural network for mapping to a classification.