As robots evolve into collaborators in human-robot teams, appropriately calibrated trust becomes crucial. This study investigates trust dynamics in an Augmented Reality-based human-robot teaming system, focusing on the interplay between robot performance and robot-to-human feedback. In an experiment with 32 participants, we examined how robot feedback influences user trust, particularly when it is mismatched with robot performance. The results show that while robot-to-human feedback does not significantly affect trust on its own, it positively affects user responses when matched to performance. Robot performance had a stronger influence on trust than feedback, and error severity significantly impacted trust levels. These findings contribute to understanding trust calibration in human-robot interactions and provide insight for designing effective trust-aware robotic systems, addressing critical gaps in existing research, and offering implications for improving human-robot collaboration across various domains.

This paper explores the elements that are needed to facilitate the seamless transfer of a robotic agent’s "persona" between various embodiments. It addresses the challenges in maintaining the robot identity, user trust, and engagement during transitions into other robot embodiments. Through a literature review, we propose a framework for decomposing and reconstructing a robot’s persona across embodiments, integrating visual, audial, and behavioral identity signals. By leveraging insights from robot interaction studies, this research contributes to the design of transferable and adaptable robot companions, with applications in eldercare and assistive technologies. The findings have broader implications for advancing human-robot interaction and fostering sustainable, user-centered robotic systems.

The interaction between humans and robots transcends mere utility, venturing into the realm of emotional bonds and perceived lifespans. Through a review of the literature and poignant case studies, this article reveals the intricate language and sentiments expressed by individuals as they navigate the loss of their mechanical companions. This exploration is relevant to understanding the complex social and emotional relationships that can form between humans and robots, emphasizing the need for thoughtful consideration of the life-cycle management of social robots. This research also underscores a significant gap in existing literature and industrial practices: the end-of-life phase of robots and the consequential emotional toll on humans.

The creation of information transparency solutions to enable humans to understand robot perception is a challenging requirement for autonomous and artificially intelligent robots to impact a multitude of domains. By taking advantage of comprehensive and high-volume data from robot teammates’ advanced perception and reasoning capabilities, humans will be able to make better decisions, with significant impacts from safety to functionality. We present a solution to this challenge by coupling augmented reality (AR) with an intelligent mobile robot that is autonomously detecting novel changes in an environment. We show that the human teammate can understand and make decisions based on information shared via AR by the robot. Sharing of robot-perceived information is enabled by the robot’s online calculation of the human’s relative position, making the system robust to environments without external instrumentation such as global positioning system. Our robotic system performs change detection by comparing current metric sensor readings against a previous reading to identify differences. We experimentally explore the design of change detection visualizations and the aggregation of information, the impact of instruction on communication understanding, the effects of visualization and alignment error, and the relationship between situated 3D visualization in AR and human movement in the operational environment on shared situational awareness in human-robot teams. We demonstrate this novel capability and assess the effectiveness of human-robot teaming in crowdsourced data-driven studies, as well as an in-person study where participants are equipped with a commercial off-the-shelf AR headset and teamed with a small ground robot that maneuvers through the environment. The mobile robot scans for changes, which are visualized via AR to the participant. The effectiveness of this communication is evaluated through accuracy and subjective assessment metrics to provide insight into interpretation and experience.

In this paper, we examine the risks posed by roboticists’ collaboration with law enforcement agencies in the U.S. Using Trust frameworks from AI Ethics, we argue that collaborations with law enforcement present not only risks of technology misuse, but also risks of legitimizing bad actors, and of exacerbating our field’s challenges of representation. We discuss evidence of bad dispositions justifying these risks, grounded in the behavior, origins, and incentivization of American policing, and suggest courses of action for American roboticists seeking to pursue research projects that currently require collaboration with law enforcement agencies, closing with a call for abolitionist robotics.

Robot designers and Human-Robot Interaction (HRI) practitioners can face challenges when people form a mental model of a robot that is not appropriate. Although the field of robotics would benefit significantly from a broad representation of designers, there is currently no comprehensive method of including many people in the design process and no theory of what expectations a robot design feature might elicit. We seek to address these challenges through the creation of a robot design platform, an online tool similar to a character creation interface in a video game, where users create a robot design. By collecting a large number of robot designs from users, we seek to be able to identify aspects of a robot’s design that influence the mental models humans ascribe to the robot. To maximize the universal usability of the platform, we conducted a three-part survey to assess which icons should be used to visually represent the mental states ascribed to the robots created by users on the platform. In our assessment, we found nine icons that met our criteria for use in the platform and others that should be further evaluated.

Tactical empathy uses neuroscience concepts to navigate difficult situations. The long-term human-robot interaction and the commercially successful long-term human-robot interaction require navigation of all, including difficult situations. It seems that employing tactical empathy in robots is a path to build trust, rapport, and teamwork whenever humans interact with robots. This paper takes a high-level look at the results from academic research and how those can inform tactical empathy in commercial in-home robots, and discusses how academic research paired with the applied concept of tactical empathy facilitate long-term interactions of humans and commercially available robots that are expected to maintain a lasting interaction with their users.

This work presents our strategy for investigating the fundamental guidelines and theories related to robot mind perception, and for establishing a metric for mental models, using our web-based tool, Build-A-Bot. We also discuss the effectiveness and efficiency of our platform by virtue of its inclusive design and its ability to visualize the user’s intended representation of a mental model for a robot through a 3D game-like interface. We conducted an observational user test study to assess if the website and the embedded robot building tool are effective and efficient to use for users. We found that the design of the robot creation platform and its associated website are considered intuitive and effective by a majority of our survey population. The Build-A-Bot platform successfully provides the ability for users to visualize their ideal representation of their mental model through an interactive game. Based on the obtained data, we propose further steps to optimize the Build-A-Bot platform for universal usability

A common issue in Human-Robot Interaction is a gap in understanding how robot designs are perceived by the user. A common issue encountered by practitioners of Machine Learning (ML) is a lack of salient data to use in training. The “Build-A-Bot” project is developing a novel research platform implemented as a web-accessible 3D game that affords data collection of many user-provided robot designs. The designs are used to train ML models to better evaluate robot designs, predict how a design will be perceived using Convolutional Neural Networks (CNNs), and create new robot designs using Generative Adversarial Networks (GANs). This paper outlines the current and future work accomplished by an interdisciplinary undergraduate student team at the University of Denver across Computer Science, Music, Psychology, and other related STEM fields that have created Build-A-Bot.

The COVID-19 pandemic will have a profound and long-lasting impact on the entire scientific endeavor. Scientists already are adapting research programs to adapt to changes in what is prioritized—and what is possible; educators are changing the way that the next generation of researchers are trained, and flagship conferences in many fields are being cancelled, postponed, and fundamentally transformed. These broad-reaching changes are particularly impactful to human-oriented domains such as human-robot interaction (HRI). Because in-person human-subject experiments can take a year or more to conduct, the research we will see published in the field in the immediate future may appear to be “business as usual,” with accounts of laboratory studies with large numbers of in-person participants. The research currently being performed, however, is of course a different story entirely. Studies that were under way when the current crisis began will be truncated, resulting either in work that cannot be published or in work whose true impact is difficult to accurately assess. Yet HRI research performed in the coming years will be changed in fundamentally different ways; the inability to perform—or expect future performance of—in-person human subjects research, especially research involving tactile or multiparty interaction, will change both the dominant methodological techniques employed by HRI researchers and the very research questions that the field chooses to—and is able to—address. These challenges demand that HRI researchers identify precisely how the field can maintain research quality and impact while the ability to conduct human-subject studies is severely impaired for an undetermined amount of time. A natural inclination may be simply to wait the crisis out in the hope of a speedy return to normalcy; however, in this article, we argue that the community can also take this opportunity to reevaluate and refocus how research in this field is conducted and how students are mentored in ways that will yield benefits for years to come after the current crisis has ended.

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.

Socially intelligent artificial agents and robots are anticipated to become ubiquitous in home, work, and military environments. With the addition of such agents to human teams it is crucial to evaluate their role in the planning, decision making, and conflict mediation processes. We conducted a study to evaluate the utility of a virtual agent that provided mission planning support in a three-person human team during a military strategic mission planning scenario. The team consisted of a human team lead who made the final decisions and three supporting roles, two humans and the artificial agent. The mission outcome was experimentally designed to fail and introduced a conflict between the human team members and the leader. This conflict was mediated by the artificial agent during the debriefing process through discuss or debate and open communication strategies of conflict resolution [1]. Our results showed that our teams experienced conflict. The teams also responded socially to the virtual agent, although they did not find the agent beneficial to the mediation process. Finally, teams collaborated well together and perceived task proficiency increased for team leaders. Socially intelligent agents show potential for conflict mediation, but need careful design and implementation to improve team processes and collaboration.

Latest research in robotics is driven by a desire to develop systems that would allow humans to interact with robots in natural and intuitive ways. In this paper we investigate peoples' initial believes, expectations and ascribed mental capabilities of a robot when they encounter it for the first time and how these change after interacting with the robot. The differences between two robots, which differ in morphology and behavior, are compared.

This study focuses on differences and similarities of perception of a small humanoid robot between Japanese and Australian participants. Two conditions were investigated: participants actively interacting with the robot and bystanders observing the interaction. Experimental results suggested that, while the robot was perceived as highly likeable, Japanese participants rated the robot higher for animacy, intelligence and safety. Furthermore, passive observations of the interaction (rather than active interaction) resulted in higher ratings by Japanese participants for anthropomorphism, animacy, intelligence and safety. The findings are discussed in terms of cultural background and robot perception.