Volume 12, Issue 1

In increasingly digitized working and living environments, human-robot collaboration is growing fast with human trust toward robotic collaboration as a key factor for the innovative teamwork to succeed. This article explores the impact of design factors of the robotic interface (anthropomorphic vs functional) and usage context (production vs care) on human–robot trust and attributions. The results of a scenario-based survey with N = 228 N=228 participants showed a higher willingness to collaborate with production robots compared to care. Context and design influenced the trust attributed to the robots: robots with a technical appearance in production were trusted more than anthropomorphic robots or robots in the care context. The evaluation of attributions by means of a semantic differential showed that differences in robot design were less pronounced for the production context in comparison to the care context. In the latter, anthropomorphic robots were associated with positive attributes. The results contribute to a better understanding of the complex nature of trust in automation and can be used to identify and shape use case-specific risk perceptions as well as perceived opportunities to interacting with collaborative robots. Findings of this study are pertinent to research (e.g., experts in human–robot interaction) and industry, with special regard given to the technical development and design.

We investigated the influence of visual sensitivity on the performance of an imitation task with the robot R1 in its virtual and physical forms. Virtual and physical embodiments offer different sensory experience to the users. As all individuals respond differently to their sensory environment, their sensory sensitivity may play a role in the interaction with a robot. Investigating how sensory sensitivity can influence the interactions appears to be a helpful tool to evaluate and design such interactions. Here we asked 16 participants to perform an imitation task, with a virtual and a physical robot under conditions of full and occluded visibility, and to report the strategy they used to perform this task. We asked them to complete the Sensory Perception Quotient questionnaire. Sensory sensitivity in vision predicted the participants’ performance in imitating the robot’s upper limb movements. From the self-report questionnaire, we observed that the participants relied more on visual sensory cues to perform the task with the physical robot than on the virtual robot. From these results, we propose that a physical embodiment enables the user to invest a lower cognitive effort when performing an imitation task over a virtual embodiment. The results presented here are encouraging that following this line of research is suitable to improve and evaluate the effects of the physical and virtual embodiment of robots for applications in healthy and clinical settings.

In health care, a telepresence robot could be used to have a clinician or a caregiver assist seniors in their homes, without having to travel to these locations. However, the usability of these platforms for such applications requires that they can navigate and interact with a certain level of autonomy. For instance, robots should be able to go to their charging station in case of low energy level or telecommunication failure. The remote operator could be assisted by the robot’s capabilities to navigate safely at home and to follow and track people with whom to interact. This requires the integration of autonomous decision-making capabilities on a platform equipped with appropriate sensing and action modalities, which are validated out in the laboratory and in real homes. To document and study these translational issues, this article presents such integration on a Beam telepresence platform using three open-source libraries for integrated robot control architecture, autonomous navigation and sound processing, developed with real-time, limited processing and robustness requirements, so that they can work in real-life settings. Validation of the resulting platform, named SAM, is presented based on the trials carried out in 10 homes. Observations made provide guidance on what to improve and will help identify interaction scenarios for the upcoming usability studies with seniors, clinicians and caregivers.

In this article, we investigate the role of causal reasoning in robotics research. Inspired by a categorization of human causal cognition, we propose a categorization of robot causal cognition. For each category, we identify related earlier work in robotics and also connect to research in other sciences. While the proposed categories mainly cover the sense–plan–act level of robotics, we also identify a number of higher-level aspects and areas of robotics research where causation plays an important role, for example, understandability, machine ethics, and robotics research methodology. Overall, we conclude that causation underlies several problem formulations in robotics, but it is still surprisingly absent in published research, in particular when it comes to explicit mentioning and using of causal concepts and terms. We discuss the reasons for, and consequences of, this and hope that this article clarifies the broad and deep connections between causal reasoning and robotics and also by pointing at the close connections to other research areas. At best, this will also contribute to a “causal revolution” in robotics.

Human beings are deeply social, and both evolutionary traits and cultural constructs encourage cooperation based on trust. Social robots interject themselves in human social settings, and they can be used for deceptive purposes. Robot deception is best understood by examining the effects of deception on the recipient of deceptive actions, and I argue that the long-term consequences of robot deception should receive more attention, as it has the potential to challenge human cultures of trust and degrade the foundations of human cooperation. In conclusion: regulation, ethical conduct by producers, and raised general awareness of the issues described in this article are all required to avoid the unfavourable consequences of a general degradation of trust.

The design of user interfaces (UIs) for assistive robot systems can be improved through the use of a set of design guidelines presented in this article. As an example, the article presents two different UI designs for an assistive manipulation robot system. We explore the design considerations from these two contrasting UIs. The first is referred to as the graphical user interface (GUI), which the user operates entirely through a touchscreen as a representation of the state of the art. The second is a type of novel UI referred to as the tangible user interface (TUI). The TUI makes use of devices in the real world, such as laser pointers and a projector–camera system that enables augmented reality. Each of these interfaces is designed to allow the system to be operated by an untrained user in an open environment such as a grocery store. Our goal is for these guidelines to aid researchers in the design of human–robot interaction for assistive robot systems, particularly when designing multiple interaction methods for direct comparison.

When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. We focus in particular on how such situations can be detected and presented to the user. We describe the human–robot behaviour teaching system that we developed as well as the formal behaviour checking methods used. The online use of behaviour checking is demonstrated, based on static analysis of behaviours during the operation of the robot, and evaluated in a user study. We conducted a proof-of-concept human–robot interaction study with an autonomous, multi-purpose robot operating within a smart home environment. Twenty participants individually taught the robot behaviours according to instructions they were given, some of which caused interference with other behaviours. A mechanism for detecting behaviour interference provided feedback to participants and suggestions on how to resolve those conflicts. We assessed the participants’ views on detected interference as reported by the behaviour teaching system. Results indicate that interference warnings given to participants during teaching provoked an understanding of the issue. We did not find a significant influence of participants’ technical background. These results highlight a promising path towards verification and validation of assistive home companion robots that allow end-user personalisation.

Love dolls, the successors of blow-up dolls, are widespread. They can be ordered online or bought in sex shops and can be found in brothels and households. Sex robots are also on the rise. Research, however, has been slow to address this topic thoroughly. Often, it does not differentiate between users and areas of application, remaining vague, especially in the humanities and social sciences. The present contribution deals with the idea and history of love dolls and sex robots. Against this background, it identifies areas of application that have not been investigated or have hardly been investigated at all. These include prisons, the military, monasteries and seminaries, science, art and design as well as the gamer scene. There is, at least, some relevant research about the application of these artefacts in nursing and retirement homes and as such, these will be given priority. The use of love dolls and sex robots in all these fields is outlined, special features are discussed and initial ethical, legal and pragmatic considerations are made. It becomes clear that artificial love servants can create added value, but that their use must be carefully considered and prepared. In some cases, their use may even be counterproductive.

The increasing presence of computers in society calls for the need to better understand how differently the sociocognitive mechanisms involved in natural human relationships operate in human–robot interactions. In the present study, we investigated one fundamental aspect often neglected in the literatures on psychology and educational sciences: how the source of information, either human or computer, influences its perceived reliability and modulates cognitive and motivational processes. In Experiment 1, participants performed a reasoning task that presented cues following participants’ errors, helping them to succeed in the task. Using two levels of task difficulty, we manipulated the source of the cues as either a human or a computer. In addition to task accuracy, Experiment 2 assessed the impact of the information source on socially and nonsocially related dimensions of achievement goals. In Experiment 1, participants who believed that they received cues from a human teacher performed better on difficult trials compared to those who believed that they received cues from a computer. In Experiment 2, we replicated these findings by additionally showing that the nature of the source only had an impact on the socially related dimension of achievement goals, which in turn mediated the source’s effect on reasoning performance. For the first time, the present study showed modulations of cognitive and motivational processes resulting from the manipulation of the type of information source aimed at providing assistance with a reasoning task. The findings highlight the importance of considering the social and motivational aspects involved in human–computer interactions.

This article is a retrospective overview of work performed in the domain of Active Assisted Living over a span of almost 18 years. The authors have been creating and refining artificial intelligence (AI) and robotics solutions to support older adults in maintaining their independence and improving their quality of life. The goal of this article is to identify strong features and general lessons learned from those experiences and conceive guidelines and new research directions for future deployment, also relying on an analysis of similar research efforts. The work considers key points that have contributed to increase the success of the innovative solutions grounding them on known technology acceptance models. The analysis is presented with a threefold perspective: A Technological vision illustrates the characteristics of the support systems to operate in a real environment with continuity , robustness , and safety ; a Socio-Health perspective highlights the role of experts in the socio-assistance domain to provide contextualized and personalized help based on actual people’s needs; finally, a Human dimension takes into account the personal aspects that influence the interaction with technology in the long term experience. The article promotes the crucial role of AI and robotics in ensuring intelligent and situated assistive behaviours. Finally, considering that the produced solutions are socio-technical systems , the article suggests a transdisciplinary approach in which different relevant disciplines merge together to have a complete, coordinated, and more informed vision of the problem.

This article explores the rapidly advancing innovation to endow robots with social intelligence capabilities in the form of multilingual and multimodal emotion recognition, and emotion-aware decision-making capabilities, for contextually appropriate robot behaviours and cooperative social human–robot interaction for the healthcare domain. The objective is to enable robots to become trustworthy and versatile social robots capable of having human-friendly and human assistive interactions, utilised to better assist human users’ needs by enabling the robot to sense, adapt, and respond appropriately to their requirements while taking into consideration their wider affective, motivational states, and behaviour. We propose an innovative approach to the difficult research challenge of endowing robots with social intelligence capabilities for human assistive interactions, going beyond the conventional robotic sense-think-act loop. We propose an architecture that addresses a wide range of social cooperation skills and features required for real human–robot social interaction, which includes language and vision analysis, dynamic emotional analysis (long-term affect and mood), semantic mapping to improve the robot’s knowledge of the local context, situational knowledge representation, and emotion-aware decision-making. Fundamental to this architecture is a normative ethical and social framework adapted to the specific challenges of robots engaging with caregivers and care-receivers.

This article discusses the fundamental requirements for making explainable robots trustworthy and comprehensible for non-expert users. To this extent, we identify three main issues to solve: the approximate nature of explanations, their dependence on the interaction context and the intrinsic limitations of human understanding. The article proposes an organic solution for the design of explainable robots rooted in a sensemaking perspective. The establishment of contextual interaction boundaries, combined with the adoption of plausibility as the main criterion for the evaluation of explanations and of interactive and multi-modal explanations, forms the core of this proposal.

Interaction between humans and robots will benefit if people have at least a rough mental model of what a robot knows about the world and what it plans to do. But how do we design human-robot interactions to facilitate this? Previous research has shown that one can change people’s mental models of robots by manipulating the robots’ physical appearance. However, this has mostly not been done in a user-centred way, i.e. without a focus on what users need and want. Starting from theories of how humans form and adapt mental models of others, we investigated how the participatory design method, PICTIVE, can be used to generate design ideas about how a humanoid robot could communicate. Five participants went through three phases based on eight scenarios from the state-of-the-art tasks in the RoboCup@Home social robotics competition. The results indicate that participatory design can be a suitable method to generate design concepts for robots’ communication in human-robot interaction.

In this article, we present work on collaborating robot teams that use verbal explanations of their actions and intentions in order to be more understandable to the human. For this, we introduce a mechanism that determines what information the robots should verbalize in accordance with Grice’s maxim of quantity, i.e., convey as much information as is required and no more or less. Our setup is a robot team collaborating to achieve a common goal while explaining in natural language what they are currently doing and what they intend to do. The proposed approach is implemented on three Pepper robots moving objects on a table. It is evaluated by human subjects answering a range of questions about the robots’ explanations, which are generated using either our proposed approach or two further approaches implemented for evaluation purposes. Overall, we find that our proposed approach leads to the most understanding of what the robots are doing. In addition, we further propose a method for incorporating policies driving the distribution of tasks among the robots, which may further support understandability.

As social robots are increasingly introduced into health interventions, one potential area where they might prove valuable is in supporting people’s psychological health through conversation. Given the importance of self-disclosure for psychological health, this study assessed the viability of using social robots for eliciting rich disclosures that identify needs and emotional states in human interaction partners. Three within-subject experiments were conducted with participants interacting with another person, a humanoid social robot, and a disembodied conversational agent (voice assistant). We performed a number of objective evaluations of disclosures to these three agents via speech content and voice analyses and also probed participants’ subjective evaluations of their disclosures to three agents. Our findings suggest that participants overall disclose more to humans than artificial agents, that agents’ embodiment influences disclosure quantity and quality, and that people are generally aware of differences in their personal disclosures to three agents studied here. Together, the findings set the stage for further investigation into the psychological underpinnings of self-disclosures to artificial agents and their potential role in eliciting disclosures as part of mental and physical health interventions.

Autism spectrum disorder (ASD) is a lifelong developmental condition that affects an individual’s ability to communicate and relate to others. Despite such challenges, early intervention during childhood development has shown to have positive long-term benefits for individuals with ASD. Namely, early childhood development of communicative speech skills has shown to improve future literacy and academic achievement. However, the delivery of such interventions is often time-consuming. Socially assistive robots (SARs) are a potential strategic technology that could help support intervention delivery for children with ASD and increase the number of individuals that healthcare professionals can positively affect. For SARs to be effectively integrated in real-world treatment for individuals with ASD, they should follow current evidence-based practices used by therapists such as Applied Behavior Analysis (ABA). In this work, we present a study that investigates the efficacy of applying well-known ABA techniques to a robot-mediated listening comprehension intervention delivered to children with ASD at a university-based ABA clinic. The interventions were delivered in place of human therapists to teach study participants a new skill as a part of their overall treatment plan. All the children participating in the intervention improved in the skill being taught by the robot and enjoyed interacting with the robot, as evident by high occurrences of positive affect as well as engagement during the sessions. One of the three participants has also reached mastery of the skill via the robot-mediated interventions.

In this work, we tested a recently developed novel methodology to assist children with Autism Spectrum Disorder (ASD) improve their Visual Perspective Taking (VPT) and Theory of Mind (ToM) skills using the humanoid robot Kaspar. VPT is the ability to see the world from another person’s perspective, drawing upon both social and spatial information. Children with ASD often find it difficult to understand that others might have perspectives, viewpoints and beliefs that are different from their own, which is a fundamental aspect of both VPT and ToM. The games we designed were implemented as the first attempt to study if these skills can be improved in children with ASD through interacting with a humanoid robot in a series of trials. The games involved a number of different actions with the common goal of helping the children to see the world from the robot’s perspective. Children with ASD were recruited to the study according to specific inclusion criteria that were determined in a previous pilot study. In order to measure the potential impact of the games on the children, three pre- and post-tests (Smarties, Sally–Anne and Charlie tests) were conducted with the children. Our findings suggest that children with ASD can indeed benefit from this approach of robot-assisted therapy.

Children with autism need innovative solutions that help them learn to master everyday experiences and cope with stressful situations. We propose that socially assistive robot companions could better understand and react to a child’s needs if they utilized tactile sensing. We examined the existing relevant literature to create an initial set of six tactile-perception requirements, and we then evaluated these requirements through interviews with 11 experienced autism specialists from a variety of backgrounds. Thematic analysis of the comments shared by the specialists revealed three overarching themes: the touch-seeking and touch-avoiding behavior of autistic children, their individual differences and customization needs, and the roles that a touch-perceiving robot could play in such interactions. Using the interview study feedback, we refined our initial list into seven qualitative requirements that describe robustness and maintainability , sensing range , feel , gesture identification , spatial , temporal , and adaptation attributes for the touch-perception system of a robot companion for children with autism. Finally, by utilizing the literature and current best practices in tactile sensor development and signal processing, we transformed these qualitative requirements into quantitative specifications. We discuss the implications of these requirements for future human–robot interaction research in the sensing, computing, and user research communities.

Robots have been used for many years in therapeutic activities with people with Autism Spectrum Disorder. However, most robots presented in the literature have limited or no mobility, are made of rigid materials, or are too expensive for many care centers. We share the choices and the design rationale of the latest version of a soft, mobile, low-cost, autonomous robot that has successfully been used for 3 years in a care center for activities that include both free play and structured games. Moreover, the kind of activities that can be performed with this robot, and the feedback obtained from therapists about its application are reported.

This article endeavors to present the impact of conducting robot-assisted music-based intervention sessions for children with low-functioning (LF) autism. To this end, a drum/xylophone playing robot is used to teach basic concepts of how to play the instruments to four participants with LF autism during nine educational sessions. The main findings of this study are compared to similar studies conducted with children with high-functioning autism. Our main findings indicated that the stereotyped behaviors of all the subjects decreased during the course of the program with an approximate large Cohen’s d effect size. Moreover, the children showed some improvement in imitation, joint attention, and social skills from the Pre-Test to Post-Test. In addition, regarding music education, we indicated that while the children could not pass a test on the music notes or reading music phrases items because of their cognitive deficits, they showed acceptable improvements (with a large Cohen’s d effect size) in the Stambak Rhythm Reproduction Test, which means that some rhythm learning occurred for the LF participants. In addition, we indicated that parenting stress levels decreased during the program. This study presents some potential possibilities of performing robot-assisted interventions for children with LF autism.

Intelligent robot companions contribute significantly to improve the living standards of people in the modern society. Therefore, humanlike decision-making skills are sought after during the design of such robots. On the one hand, such features enable the robot to be easily handled by its human user. On the other hand, the robot will have the capability of dealing with humans without disturbing them by its behavior. Perception of Behavioral Ontology prior to an interaction is an important aspect in this regard. Furthermore, humans make an instant evaluation of task-related movements of others before approaching them. In this article, we present a mechanism to monitor how the activity space is utilized by a particular user on a temporal basis as an ontological assessment of the situation and then determine an appropriate approach behavior for a proactive robot to initiate an interaction with its user. This evaluation was then used to determine appropriate proxemic behavior to approach that person. The usage of activity space varies depending on the task of an individual. We used a probabilistic approach to find the areas that are the most and least likely to be occupied within the activity space of a particular individual during various tasks. As the robot approaches its subject after analyzing the spatial behavior of the subject within his/her activity space, spatial constraints occurred as a result of which robot’s movement could be demolished. Hence, a more socially acceptable spatial behavior could be observed from the robot. In other words, an etiquette based on approach behavior is derived considering the user’s activity space. Experiment results used to validate the system are presented, and critical observations during the study and implications are discussed.

Can we have personal robots without giving away personal data? Besides, what is the role of a robots Privacy Policy in that question? This work explores for the first time privacy in the context of consumer robotics through the lens of information communicated to users through Privacy Policies and Terms and Conditions. Privacy, personal and non-personal data are discussed under the light of the human–robot relationship, while we attempt to draw connections to dimensions related to personalization, trust, and transparency. We introduce a novel methodology to assess how the “Organization for Economic Cooperation and Development Guidelines Governing the Protection of Privacy and Trans-Border Flows of Personal Data” are reflected upon the publicly available Privacy Policies and Terms and Conditions in the consumer robotics field. We draw comparisons between the ways eight consumer robotic companies approach privacy principles. Current findings demonstrate significant deviations in the structure and context of privacy terms. Some practical dimensions in terms of improving the context and the format of privacy terms are discussed. The ultimate goal of this work is to raise awareness regarding the various privacy strategies used by robot companies while ultimately creating a usable way to make this information more relevant and accessible to users.

Following ethnographic studies of Danish companies, this article examines how small- and medium-sized companies are implementing cobots into their manufacturing systems and considers how this is changing the practices of technicians and operators alike. It considers how this changes human values and has ethical consequences for the companies involved. By presenting a range of dilemmas arising during emergent processes, it raises questions about the extent to which ethics can be regulated and predetermined in processes of robot implementation and the resulting reconfiguration of work.

Suppose an artificial agent a adj {a}_{\text{adj}} , as time unfolds, (i) receives from multiple artificial agents (which may, in turn, themselves have received from yet other such agents…) propositional content, and (ii) must solve an ethical problem on the basis of what it has received. How should a adj {a}_{\text{adj}} adjudicate what it has received in order to produce such a solution? We consider an environment infused with logicist artificial agents a 1 , a 2 , … , a n {a}_{1},{a}_{2},\ldots ,{a}_{n} that sense and report their findings to “adjudicator” agents who must solve ethical problems. (Many if not most of these agents may be robots.) In such an environment, inconsistency is a virtual guarantee: a adj {a}_{\text{adj}} may, for instance, receive a report from a 1 {a}_{1} that proposition ϕ \phi holds, then from a 2 {a}_{2} that ¬ ϕ \neg \phi holds, and then from a 3 {a}_{3} that neither ϕ \phi nor ¬ ϕ \neg \phi should be believed, but rather ψ \psi instead, at some level of likelihood. We further assume that agents receiving such incompatible reports will nonetheless sometimes simply need, before long, to make decisions on the basis of these reports, in order to try to solve ethical problems. We provide a solution to such a quandary: AI capable of adjudicating competing reports from subsidiary agents through time, and delivering to humans a rational, ethically correct (relative to underlying ethical principles) recommendation based upon such adjudication. To illuminate our solution, we anchor it to a particular scenario.

Although the principles followed by modern standards for interaction between humans and robots follow the First Law of Robotics popularized in science fiction in the 1960s, the current standards regulating the interaction between humans and robots emphasize the importance of physical safety. However, they are less developed in another key dimension: psychological safety. As sales of industrial robots have been increasing over recent years, so has the frequency of human–robot interaction (HRI). The present article looks at the current safety guidelines for HRI in an industrial setting and assesses their suitability. This article then presents a means to improve current standards utilizing lessons learned from studies into human aware navigation (HAN), which has seen increasing use in mobile robotics. This article highlights limitations in current research, where the relationships established in mobile robotics have not been carried over to industrial robot arms. To understand this, it is necessary to focus less on how a robot arm avoids humans and more on how humans react when a robot is within the same space. Currently, the safety guidelines are behind the technological advance, however, with further studies aimed at understanding HRI and applying it to newly developed path finding and obstacle avoidance methods, science fiction can become science fact.

Increasingly, robots are decision makers in manufacturing, finance, medicine, and other areas, but the technology may not be trusted enough for reasons such as gaps between expectation and competency, challenges in explainable AI, users’ exposure level to the technology, etc. To investigate the trust issues between users and robots, the authors employed in this study, the case of robots making decisions in football (or “soccer” as it is known in the US) games as referees. More specifically, we presented a study on how the appearance of a human and three robotic linesmen (as presented in a study by Malle et al.) impacts fans’ trust and preference for them. Our online study with 104 participants finds a positive correlation between “Trust” and “Preference” for humanoid and human linesmen, but not for “AI” and “mechanical” linesmen. Although no significant trust differences were observed for different types of linesmen, participants do prefer human linesman to mechanical and humanoid linesmen. Our qualitative study further validated these quantitative findings by probing possible reasons for people’s preference: when the appearance of a linesman is not humanlike, people focus less on the trust issues but more on other reasons for their linesman preference such as efficiency, stability, and minimal robot design. These findings provide important insights for the design of trustworthy decision-making robots which are increasingly integrated to more and more aspects of our everyday lives.

Recovery procedures are targeted at correcting issues encountered by robots. What are people’s opinions of a robot during these recovery procedures? During an experiment that examined how a mobile robot moved, the robot would unexpectedly pause or rotate itself to recover from a navigation problem. The serendipity of the recovery procedure and people’s understanding of it became a case study to examine how future study designs could consider breakdowns better and look at suggestions for better robot behaviors in such situations. We present the original experiment with the recovery procedure. We then examine the responses from the participants in this experiment qualitatively to see how they interpreted the breakdown situation when it occurred. Responses could be grouped into themes of sentience, competence, and the robot’s forms. The themes indicate that the robot’s movement communicated different information to different participants. This leads us to introduce the concept of movement acts to help examine the explicit and implicit parts of communication in movement. Given that we developed the concept looking at an unexpected breakdown, we suggest that researchers should plan for the possibility of breakdowns in experiments and examine and report people’s experience around a robot breakdown to further explore unintended robot communication.

There is increasing attention given to the concept of trustworthiness for artificial intelligence and robotics. However, trust is highly context-dependent, varies among cultures, and requires reflection on others’ trustworthiness, appraising whether there is enough evidence to conclude that these agents deserve to be trusted. Moreover, little research exists on what happens when too much trust is placed in robots and autonomous systems. Conceptual clarity and a shared framework for approaching overtrust are missing. In this contribution, we offer an overview of pressing topics in the context of overtrust and robots and autonomous systems. Our review mobilizes insights solicited from in-depth conversations from a multidisciplinary workshop on the subject of trust in human–robot interaction (HRI), held at a leading robotics conference in 2020. A broad range of participants brought in their expertise, allowing the formulation of a forward-looking research agenda on overtrust and automation biases in robotics and autonomous systems. Key points include the need for multidisciplinary understandings that are situated in an eco-system perspective, the consideration of adjacent concepts such as deception and anthropomorphization, a connection to ongoing legal discussions through the topic of liability, and a socially embedded understanding of overtrust in education and literacy matters. The article integrates diverse literature and provides a ground for common understanding for overtrust in the context of HRI.

As robotic arms become prevalent in industry, it is crucial to improve levels of trust from human collaborators. Low levels of trust in human–robot interaction can reduce overall performance and prevent full robot utilization. We investigated the potential benefits of using emotional musical prosody (EMP) to allow the robot to respond emotionally to the user’s actions. We define EMP as musical phrases inspired by speech-based prosody used to display emotion. We tested participants’ responses to interacting with a virtual robot arm and a virtual humanoid that acted as a decision agent, helping participants select the next number in a sequence. We compared results from three versions of the application in a between-group experiment, where the robot presented different emotional reactions to the user’s input depending on whether the user agreed with the robot and whether the user’s choice was correct. One version used EMP audio phrases selected from our dataset of singer improvisations, the second version used audio consisting of a single pitch randomly assigned to each emotion, and the final version used no audio, only gestures. In each version, the robot reacted with emotional gestures. Participants completed a trust survey following the interaction, and we found that the reported trust ratings of the EMP group were significantly higher than both the single-pitch and no audio groups for the robotic arm. We found that our audio system made no significant difference in any metric when used on a humanoid robot implying audio needs to be separately designed for each platform.

Privacy is an essential topic in (social) robotics and becomes even more important when considering interactive and autonomous robots within the domestic environment. Robots will collect a lot of personal and sensitive information about the users and their environment. Thereby, privacy does consider the topic of (cyber-)security and the protection of information against misuse by involved service providers. So far, the main focus relies on theoretical concepts to propose privacy principles for robots. This article provides a privacy framework as a feasible approach to consider security and privacy issues as a basis. Thereby, the proposed privacy framework is put in the context of a user-centered design approach to highlight the correlation between the design process steps and the steps of the privacy framework. Furthermore, this article introduces feasible privacy methodologies for privacy-enhancing development to simplify the risk assessment and meet the privacy principles. Even though user participation plays an essential role in robot development, this is not the focus of this article. Even though user participation plays an essential role in robot development, this is not the focus of this article. The employed privacy methodologies are showcased in a use case of a robot as an interaction partner contrasting two different use case scenarios to encourage the importance of context awareness.

Human–robot interaction (HRI) and game theory have developed distinct theories of trust for over three decades in relative isolation from one another. HRI has focused on the underlying dimensions, layers, correlates, and antecedents of trust models, while game theory has concentrated on the psychology and strategies behind singular trust decisions. Both fields have grappled to understand over-trust and trust calibration, as well as how to measure trust expectations, risk, and vulnerability. This article presents initial steps in closing the gap between these fields. By using insights and experimental findings from interdependence theory and social psychology, this work starts by analyzing a large game theory competition data set to demonstrate that the strongest predictors for a wide variety of human–human trust interactions are the interdependence-derived variables for commitment and trust that we have developed. It then presents a second study with human subject results for more realistic trust scenarios, involving both human–human and human–machine trust. In both the competition data and our experimental data, we demonstrate that the interdependence metrics better capture social “overtrust” than either rational or normative psychological reasoning, as proposed by game theory. This work further explores how interdependence theory – with its focus on commitment, coercion, and cooperation – addresses many of the proposed underlying constructs and antecedents within human–robot trust, shedding new light on key similarities and differences that arise when robots replace humans in trust interactions.

Artificial agents can uplift the living standards of domestic population considerably. One hindrance for this is that the robot is less competent to perceive complex human behaviors. With such perceptive skills in the robot, nonexpert users will find it easier to cope with their robot companion with less and less instructions to follow. Perception of the internal state of a user or “user situation” before interaction is crucial in this regard. There are a variety of factors that affect this user situation. Out of these, posture becomes prominent in displaying the emotional state of a person. This article presents a novel approach to identify diverse human postures often encountered in domestic environments and how a robot could assess its user’s emotional state of mind before an interaction based on postures. Therefore, the robot evaluates posture and the overall postural behavior of its user throughout the period of observation before initiating an interaction with its user. Aforementioned user evaluation is nonverbal and decisions are made through observation as well. We introduced a variable called “valence” to measure how “relaxed” or “stressed” a user is, in a certain encounter. The robot decides upon an appropriate approach behavior accordingly. Furthermore, the proposed concept was capable of recognizing both arm and body postures and both postural behaviors over time. This leads to an interaction initiated by robot itself in a favorable situation so that the scenario looks more intelligent. Hence more humanlike. The system has been implemented, and experiments have been conducted on an assistive robot placed in an artificially created domestic environment. Results of the experiments have been used to validate the proposed concept and critical observations are discussed.