Human-robot Interaction Studies Research Articles

Overtrust in AI Recommendations About Whether or Not to Kill: Evidence from Two Human-Robot Interaction Studies

This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants’ subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agent’s intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

LabLinking: theory, framework, and solutions of connecting laboratories for distributed human experiments

We introduce the concept of LabLinking: a technology-based interconnection of experimental laboratories across institutions, disciplines, cultures, languages, and time zones - in other words human studies and experiments without borders. In particular, we introduce a theoretical framework of LabLinking, describing multiple dimensions of conceptual, organizational, and technical aspects. The framework defines LabLinking levels (LLL), which describe the degree of tightness of empirical interconnection between labs. In several examples, we describe the technological infrastructure in terms of hard- and software required for the respective LLLs and share insights about the challenges and benefits. This comprises the connection of multiple labs in a collection of multiple synchronized biosignals (including an MRI scanner) for a decision making study, a human-robot interaction study to investigate attention-adaptive communication behavior, as well as an experiment for LabLinking through Virtual Reality in a virtual commerce setting, for an increased feeling of immersion. In sum, we argue that LabLinking provides a unique platform for a continuous exchange between scientists and experimenters, thereby enabling a time synchronous execution of experiments performed with and by decentralized users and researchers, allowing to establish novel experimental designs that would not be feasible without LabLinking, such as the investigation of high-resolution neural signals in everyday activity situations, which was realized by interconnecting a control participant in an fMRI with an execution participant in a kitchen environment.

A Meta-Analysis of Vulnerability and Trust in Human–Robot Interaction

In human–robot interaction studies, trust is often defined as a process whereby a trustor makes themselves vulnerable to a trustee. The role of vulnerability however is often overlooked in this process but could play an important role in the gaining and maintenance of trust between users and robots. To better understand how vulnerability affects human–robot trust, we first reviewed the literature to create a conceptual model of vulnerability with four vulnerability categories. We then performed a meta-analysis, first to check the overall contribution of the variables included on trust. The results showed that overall, the variables investigated in our sample of studies have a positive impact on trust. We then conducted two multilevel moderator analysis to assess the effect of vulnerability on trust, including: (1) an intercept model that considers the relationship between our vulnerability categories and (2) a non-intercept model that treats each vulnerability category as an independent predictor. Only model 2 was significant, suggesting that to build trust effectively, research should focus on improving robot performance in situations where the users are unsure how reliable the robot will be. As our vulnerability variable is derived from studies of human–robot interaction and researcher reflections about the different risks involved, we relate our findings to these domains and make suggestions for future research avenues.

Let’s Get Physical: The Influence of Embodiment on Industrial Human-Robot Interaction

In recent years, an increasing number of studies in human-robot interaction (HRI) have used images or videos of robots as a simple, inexpensive, and customizable substitute for physically embodied robots. Thus, the question arises whether results from studies using depictions can be validly applied to interactions with embodied robots. This study investigated the effect of embodiment in HRI within an industrial interaction scenario, focusing on perception, trust, and task performance. Eighty-five participants interacted either with an embodied robot or its depiction via a computer screen. Results showed that the embodied robot was perceived as more likable, intelligent, and safer, eliciting higher initial trust. Primary task performance was observed to be higher in interaction with the embodied robot, while secondary task performance was higher in interaction with the robot’s depiction. These findings challenge the generalizability of depiction-based HRI research, highlighting the importance of considering embodiment in future research designs.

Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set

In human-human and human-robot interaction, the counterpart influences the human’s affective state. Contrary to humans, robots inherently cannot respond empathically, meaning non-beneficial affective reactions cannot be mitigated. Thus, to create a responsible and empathetic human-robot interaction (HRI), involving anthropomorphic service robots, the effect of robot behavior on human affect in HRI must be understood. To contribute to this understanding, we provide the new comprehensive data set AFFECT-HRI, including, for the first time, physiological data labeled with human affect (i.e., emotions and mood) gathered from a conducted HRI study. Within the study, 146 participants interacted with an anthropomorphic service robot in a realistic and complex retail scenario. The participants’ questionnaire ratings regarding affect, demographics, and socio-technical ratings are provided in the data set. Five different conditions (i.e., neutral, transparency, liability, moral, and immoral) were considered during the study, eliciting different affective reactions and allowing interdisciplinary investigations (e.g., computer science, law, and psychology). Each condition includes three scenes: a consultation regarding products, a request for sensitive personal information, and a handover.

Previous Experience Matters: An in-Person Investigation of Expectations in Human–Robot Interaction

The human–robot interaction (HRI) field goes beyond the mere technical aspects of developing robots, often investigating how humans perceive robots. Human perceptions and behavior are determined, in part, by expectations. Given the impact of expectations on behavior, it is important to understand what expectations individuals bring into HRI settings and how those expectations may affect their interactions with the robot over time. For many people, social robots are not a common part of their experiences, thus any expectations they have of social robots are likely shaped by other sources. As a result, individual expectations coming into HRI settings may be highly variable. Although there has been some recent interest in expectations within the field, there is an overall lack of empirical investigation into its impacts on HRI, especially in-person robot interactions. To this end, a within-subject in-person study (N=31\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=31$$\\end{document}) was performed where participants were instructed to engage in open conversation with the social robot Pepper during two 2.5 min sessions. The robot was equipped with a custom dialogue system based on the GPT-3 large language model, allowing autonomous responses to verbal input. Participants’ affective changes towards the robot were assessed using three questionnaires, NARS, RAS, commonly used in HRI studies, and Closeness, based on the IOS scale. In addition to the three standard questionnaires, a custom question was administered to capture participants’ views on robot capabilities. All measures were collected three times, before the interaction with the robot, after the first interaction with the robot, and after the second interaction with the robot. Results revealed that participants to large degrees stayed with the expectations they had coming into the study, and in contrast to our hypothesis, none of the measured scales moved towards a common mean. Moreover, previous experience with robots was revealed to be a major factor of how participants experienced the robot in the study. These results could be interpreted as implying that expectations of robots are to large degrees decided before interactions with the robot, and that these expectations do not necessarily change as a result of the interaction. Results reveal a strong connection to how expectations are studied in social psychology and human-human interaction, underpinning its relevance for HRI research.

RoSI: A Model for Predicting Robot Social Influence

A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.

Privacy and utility perceptions of social robots in healthcare

Many Human-Robot Interaction (HRI) researchers are exploring the use of healthcare robots. Due to the sensitive nature of care, privacy concerns play a significant role in determining robot utility and adoption. While HRI research has explored some dimensions of privacy for robots in general, to our knowledge, no prior work has empirically studied how human-like robot design affects people's privacy and utility perceptions of robots across different healthcare contexts and tasks. We conducted a 3 × 3 × 3 study (n = 239) to understand these relationships, varying robot Human Likeness (HL) (low, medium, and high) and scenario/task type (hospital waiting room/robot check-in support, hospital patient room/robot mobility support, home care/robot neurorehabilitation support) via a mixed between-within subjects design. To our knowledge, this is one of the first studies that operationalizes complex constructs of privacy, healthcare, and HL across multiple realistic healthcare contexts, with a high degree of cognitive fidelity. Our results suggest the tasks and contexts in which privacy is considered in healthcare contexts with robots is more impactful than other factors like robot HL appearance. In particular, some settings include more complex tradeoffs between privacy and utility for robots than others. For example, HRI researchers and practitioners who want to build healthcare robots intended for the home may encounter the greatest challenges for balancing privacy risks. Finally, for the community, we demonstrate that design fiction animations can be a useful way to facilitate cognitive fidelity for supporting studies in HRI and serving as a bridge between narrative methods and the use of real-world robots.

Human-robot interactions with an autonomous health screening robot in long-term care settings

Socially assistive robots are increasingly being considered to help address the shortage of care workers in long-term care, which has been further exacerbated by the COVID-19 pandemic. In this paper, we present the first human-robot interaction study with care staff and an autonomous screening socially assistive robot in a long-term care facility. We assessed: (1) overall perceptions, experiences and attitudes of care staff prior to and after interacting with the robot, and (2) perceived workload and usability of the robot by administrators and management staff. Results show staff had overall high ratings of the robot, with a statistically significant increase identified for cognitive attitude towards the robot after interaction. Furthermore, we found that overall, perceived workload was moderately low as defined by the NASA Task Load Index while using the robot screener, and the usability rating of the robot was rated between OK and Good by the System Usability Scale. Personalization of the robot was found to be an important factor for usability. Staff enjoyed using the robot and had high willingness to frequently use it. In general, our robot study motivates the application of autonomous socially assistive robots from the staff perspective for repetitive tasks in long-term care homes.

Towards a conceptualisation and critique of everyday life in HRI.

This paper focuses on the topic of "everyday life" as it is addressed in Human-Robot Interaction (HRI) research. It starts from the argument that while human daily life with social robots has been increasingly discussed and studied in HRI, the concept of everyday life lacks clarity or systematic analysis, and it plays only a secondary role in supporting the study of the key HRI topics. In order to help conceptualise everyday life as a research theme in HRI in its own right, we provide an overview of the Social Science and Humanities (SSH) perspectives on everyday life and lived experiences, particularly in sociology, and identify the key elements that may serve to further develop and empirically study such a concept in HRI. We propose new angles of analysis that may help better explore unique aspects of human engagement with social robots. We look at the everyday not just as a reality as we know it (i.e., the realm of the "ordinary") but also as the future that we need to envision and strive to materialise (i.e., the transformation that will take place through the "extraordinary" that comes with social robots). Finally, we argue that HRI research would benefit not only from engaging with a systematic conceptualisation but also critique of the contemporary everyday life with social robots. This is how HRI studies could play an important role in challenging the current ways of understanding of what makes different aspects of the human world "natural" and ultimately help bringing a social change towards what we consider a "good life."

How Should a Social Mediator Robot Convey Messages About the Self-Disclosures of Elderly People to Recipients?

Encouraging the self-disclosure of the elderly is important for preventing their social isolation. In this article, we discuss a use case in which social robots are employed to mediate remote communication between elderly individuals and their family members or friends. This research aims to elaborate design guidelines for social mediator robots concerning how robots should convey messages from elderly individuals to their recipients. We particularly considered human–robot interactions in which elderly individuals can choose the robot’s behavior (i.e., messaging options) based on their preference. If the robot is implemented with effective messaging options, the elderly’s anxiety about self-disclosing information they usually feel reluctant to share with others (e.g., loss experiences) may be mitigated. An online survey of 589 elderly participants showed that the messaging options for the mediator robot should be designed in three types: requesting-support, concealing, and recording. The study results also suggest that each of the messaging options should be chosen according to the relationships between the factors of recipients, disclosers’ personal characteristics, and dialog topics. Furthermore, an empirical human–robot interaction study conducted with 36 elderly participants suggested that the anxiety of elderly disclosers was significantly lower when they could apply their preferable messaging options to self-disclosure than the case when the robot did not provide any messaging options to them. Thus, the effectiveness of the messaging options designed through this study was demonstrated.

We cannot escape gendering robots

The novel Autonomous embodies two decades of human-robot interaction studies on the effects of robot gender.

From Inanimate Object to Agent: Impact of Pre-beginnings on the Emergence of Greetings with a Robot

The very first moments of co-presence, during which a robot appears to a participant for the first time, are often “off-the-record” in the data collected from human-robot experiments (video recordings, motion tracking, methodology sections, etc.). Yet, this “pre-beginning” phase, well documented in the case of human-human interactions, is not an interactional vacuum: It is where interactional work from participants can take place so the production of a first speaking turn (like greeting the robot) becomes relevant and expected. We base our analysis on an experiment that replicated the interaction opening delays sometimes observed in laboratory or “in-the-wild” human-robot interaction studies—where robots can require time before springing to life after they are in co-presence with a human. Using an ethnomethodological and multimodal conversation analytic methodology (EMCA), we identify which properties of the robot's behavior were oriented to by participants as creating the adequate conditions to produce a first greeting. Our findings highlight the importance of the state in which the robot originally appears to participants: as an immobile object or, instead, as an entity already involved in preexisting activity. Participants’ orientations to the very first behaviors manifested by the robot during this “pre-beginning” phase produced a priori unpredictable sequential trajectories, which configured the timing and the manner in which the robot emerged as a social agent. We suggest that these first instants of co-presence are not peripheral issues with respect to human-robot experiments but should be thought about and designed as an integral part of those.

EEG Correlates of Distractions and Hesitations in Human–Robot Interaction: A LabLinking Pilot Study

In this paper, we investigate the effect of distractions and hesitations as a scaffolding strategy. Recent research points to the potential beneficial effects of a speaker’s hesitations on the listeners’ comprehension of utterances, although results from studies on this issue indicate that humans do not make strategic use of them. The role of hesitations and their communicative function in human-human interaction is a much-discussed topic in current research. To better understand the underlying cognitive processes, we developed a human–robot interaction (HRI) setup that allows the measurement of the electroencephalogram (EEG) signals of a human participant while interacting with a robot. We thereby address the research question of whether we find effects on single-trial EEG based on the distraction and the corresponding robot’s hesitation scaffolding strategy. To carry out the experiments, we leverage our LabLinking method, which enables interdisciplinary joint research between remote labs. This study could not have been conducted without LabLinking, as the two involved labs needed to combine their individual expertise and equipment to achieve the goal together. The results of our study indicate that the EEG correlates in the distracted condition are different from the baseline condition without distractions. Furthermore, we could differentiate the EEG correlates of distraction with and without a hesitation scaffolding strategy. This proof-of-concept study shows that LabLinking makes it possible to conduct collaborative HRI studies in remote laboratories and lays the first foundation for more in-depth research into robotic scaffolding strategies.

A multidimensional taxonomy for human-robot interaction in construction

Despite the increased interest in construction robotics both in academia and the industry, insufficient attention has been given to aspects related to Human-Robot Interaction (HRI). Characterizing HRI for construction tasks can help researchers organize knowledge in a structured manner that allows for classifying construction robotics applications and comparing and benchmarking different studies. This paper builds upon existing taxonomies and empirical studies in HRI in various industries (e.g., construction, manufacturing, and military, among others) to propose a multidimensional taxonomy to characterize HRI applications in the construction industry. The taxonomy design followed a systematic literature review in which common themes were identified and grouped into 16 categories. The proposed taxonomy can be used as a foundation for systematic reviews and meta-analyses of HRI applications in construction and can benefit the construction industry by informing the design of collaborative tasks performed by human-robot teams.

What’s Best for Customers: Empathetic Versus Solution-Oriented Service Robots

A promising application of social robots highlighted by the ongoing labor shortage is to deploy them as service robots at organizational frontlines. As the face of the firms, service robots are expected to provide cognitive and affective supports in response to customer inquiries. However, one question remains unanswered: Would having a robot with a high level of affective support be helpful when such a robot cannot provide a satisfactory level of cognitive support to users? In this study, we aim to address this question by showing that empathetic service robots can be beneficial, although the extent of such benefits depends on the quality of services they provide. Our in-person human–robot interaction study (n = 55) shows that when a service robot can only provide a partial solution, it is preferable for it to express more empathetic behaviors, as users will perceive it to be more useful and will have a better customer experience. However, when a service robot is able to provide a full solution, the level of empathy displayed by it does not result in significant differences on perceived usefulness and customer experience. These findings are further validated in an online experimental study performed in another country (n = 395).

Robots, Rebukes, and Relationships: Confucian Ethics and the Study of Human-Robot Interactions

The status and functioning of shame is contested in moral psychology. In much of anglophone philosophy and psychology, it is presumed to be largely destructive, while in Confucian philosophy and many East Asian communities, it is positively associated with moral development. Recent work in human-robot interaction offers a unique opportunity to investigate how shame functions while controlling for confounding variables of interpersonal interaction. One research program suggests a Confucian strategy for using robots to rebuke participants, but results from experiments with educational technologies imply a different and potentially opposing account of shame’s role in personal development. By digging deeper into the details of Confucian theorizing about shame, I identify a unifying explanation for these apparently conflicting results. I conclude by offering suggestions for future empirical research in human-robot interactions to further investigate shame’s role in moral development.

RISE: an open-source architecture for interdisciplinary and reproducible human-robot interaction research.

In this article, we present RISE-a Robotics Integration and Scenario-Management Extensible-Architecture-for designing human-robot dialogs and conducting Human-Robot Interaction (HRI) studies. In current HRI research, interdisciplinarity in the creation and implementation of interaction studies is becoming increasingly important. In addition, there is a lack of reproducibility of the research results. With the presented open-source architecture, we aim to address these two topics. Therefore, we discuss the advantages and disadvantages of various existing tools from different sub-fields within robotics. Requirements for an architecture can be derived from this overview of the literature, which 1) supports interdisciplinary research, 2) allows reproducibility of the research, and 3) is accessible to other researchers in the field of HRI. With our architecture, we tackle these requirements by providing a Graphical User Interface which explains the robot behavior and allows introspection into the current state of the dialog. Additionally, it offers controlling possibilities to easily conduct Wizard of Oz studies. To achieve transparency, the dialog is modeled explicitly, and the robot behavior can be configured. Furthermore, the modular architecture offers an interface for external features and sensors and is expandable to new robots and modalities.

Invoking and identifying task-oriented interlocutor confusion in human-robot interaction.

Successful conversational interaction with a social robot requires not only an assessment of a user's contribution to an interaction, but also awareness of their emotional and attitudinal states as the interaction unfolds. To this end, our research aims to systematically trigger, but then interpret human behaviors to track different states of potential user confusion in interaction so that systems can be primed to adjust their policies in light of users entering confusion states. In this paper, we present a detailed human-robot interaction study to prompt, investigate, and eventually detect confusion states in users. The study itself employs a Wizard-of-Oz (WoZ) style design with a Pepper robot to prompt confusion states for task-oriented dialogues in a well-defined manner. The data collected from 81 participants includes audio and visual data, from both the robot's perspective and the environment, as well as participant survey data. From these data, we evaluated the correlations of induced confusion conditions with multimodal data, including eye gaze estimation, head pose estimation, facial emotion detection, silence duration time, and user speech analysis-including emotion and pitch analysis. Analysis shows significant differences of participants' behaviors in states of confusion based on these signals, as well as a strong correlation between confusion conditions and participants own self-reported confusion scores. The paper establishes strong correlations between confusion levels and these observable features, and lays the ground or a more complete social and affect oriented strategy for task-oriented human-robot interaction. The contributions of this paper include the methodology applied, dataset, and our systematic analysis.

Effects of service robots' anthropomorphism on consumers' attribution toward and forgiveness of service failure

AbstractWe investigated the effects of service robots' anthropomorphism on consumers' attribution and forgiveness of service failure. By manipulating the levels of anthropomorphism in service robots in three experiments, we evaluated the relationship norms and involvement levels of consumers. Three contributions of our study are as follows. First, most human–robot interaction studies have focused on consumers' dissatisfaction with service robots' service failures. Unfortunately, few studies have investigated the influence of service robots' anthropomorphism levels on consumers' internal attribution and forgiveness of service failure. In the present study, we found a positive correlation between the former and the latter. The findings of this study indicate marketing managers should design their service robots to be less anthropomorphic to reduce the likelihood of consumers making an internal attribution when a service failure occurs and increase their forgiveness of service failure. In addition, to reduce the likelihood of consumers' internal attribution upon service failure, managers may ensure that their service robots explain to the consumers the cause of service failure (e.g., inability to understand consumers' requirements and robots' design‐related limitations). Second, consumers' relationship norms moderate the effect of anthropomorphism level on internal attribution. Third, robot anthropomorphism is reportedly effective only when anthropomorphization occurs subconsciously.