Showing papers in "International Journal of Social Robotics in 2020"

Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams

Abstract: The introduction of artificial teammates in the form of autonomous social robots, with fewer social abilities compared to humans, presents new challenges for human–robot team dynamics. A key characteristic of high performing human-only teams is their ability to establish, develop, and calibrate trust over long periods of time, making the establishment of longitudinal human–robot team trust calibration a crucial part of these challenges. This paper presents a novel integrative model that takes a longitudinal perspective on trust development and calibration in human–robot teams. A key new proposed factor in this model is the introduction of the concept relationship equity. Relationship equity is an emotional resource that predicts the degree of goodwill between two actors. Relationship equity can help predict the future health of a long-term relationship. Our model is descriptive of current trust dynamics, predictive of the impact on trust of interactions within a human–robot team, and prescriptive with respect to the types of interventions and transparency methods promoting trust calibration. We describe the interplay between team trust dynamics and the establishment of work agreements that guide and improve human–robot collaboration. Furthermore, we introduce methods for dampening (reducing overtrust) and repairing (reducing undertrust) mis-calibrated trust between team members as well as methods for transparency and explanation. We conclude with a description of the implications of our model and a research agenda to jump-start a new comprehensive research program in this area.

A Systematic Review of Attitudes, Anxiety, Acceptance, and Trust Towards Social Robots

Abstract: As social robots become more common, there is a need to understand how people perceive and interact with such technology. This systematic review seeks to estimate people’s attitudes toward, trust in, anxiety associated with, and acceptance of social robots; as well as factors that are associated with these beliefs. Ninety-seven studies were identified with a combined sample of over 13,000 participants and a standardized score was computed for each in order to represent the valence (positive, negative, or neutral) and magnitude (on a scale from 1 to − 1) of people’s beliefs about robots. Potential moderating factors such as the robots’ domain of application and design, the type of exposure to the robot, and the characteristics of potential users were also investigated. The findings suggest that people generally have positive attitudes towards social robots and are willing to interact with them. This finding may challenge some of the existing doubt surrounding the adoption of robotics in social domains of application but more research is needed to fully understand the factors that influence attitudes.

Child–robot relationship formation: A narrative review of empirical research

Abstract: This narrative review aimed to elucidate which robot-related characteristics predict relationship formation between typically-developing children and social robots in terms of closeness and trust. Moreover, we wanted to know to what extent relationship formation can be explained by children’s experiential and cognitive states during interaction with a robot. We reviewed 86 journal articles and conference proceedings published between 2000 and 2017. In terms of predictors, robots’ responsiveness and role, as well as strategic and emotional interaction between robot and child, increased closeness between the child and the robot. Findings about whether robot features predict children’s trust in robots were inconsistent. In terms of children’s experiential and cognitive states during interaction with a robot, robot characteristics and interaction styles were associated with two experiential states: engagement and enjoyment/liking. The literature hardly addressed the impact of experiential and cognitive states on closeness and trust. Comparisons of children’s interactions with robots, adults, and objects showed that robots are perceived as neither animate nor inanimate, and that they are entities with whom children will likely form social relationships. Younger children experienced more enjoyment, were less sensitive to a robot’s interaction style, and were more prone to anthropomorphic tendencies and effects than older children. Tailoring a robot’s sex to that of a child mainly appealed to boys.

ENRICHME: Perception and Interaction of an Assistive Robot for the Elderly at Home

Abstract: Recent technological advances enabled modern robots to become part of our daily life. In particular, assistive robotics emerged as an exciting research topic that can provide solutions to improve the quality of life of elderly and vulnerable people. This paper introduces the robotic platform developed in the ENRICHME project, with particular focus on its innovative perception and interaction capabilities. The project’s main goal is to enrich the day-to-day experience of elderly people at home with technologies that enable health monitoring, complementary care, and social support. The paper presents several modules created to provide cognitive stimulation services for elderly users with mild cognitive impairments. The ENRICHME robot was tested in three pilot sites around Europe (Poland, Greece, and UK) and proven to be an effective assistant for the elderly at home.

The Humanoid Robot NAO as Trainer in a Memory Program for Elderly People with Mild Cognitive Impairment

Abstract: Many studies on social interaction have used the humanoid robot NAO. In the present paper, we described our project designed to address the growing unmet need for alternative approaches to slowing the progression of cognitive decline in Mild Cognitive Impairment patients. NAO is the experimental platform used in an ecological setting: a center for the treatment of cognitive disorders and dementia of the Italian health service. This paper describes the study addressed to evaluate the effectiveness of human–robot interaction to reinforce therapeutic behavior and treatments adherence and presents the latest findings of functional tests and users investigation recently conducted. The robot was programmed to implement some tasks from the usual memory-training program protocol. In different training conditions, subjects participated in sessions with the support of NAO or only from the psychologist while the interaction was recorded for subsequent exploration. Data indicated that memory training with NAO resulted in an increase of visual gaze from patients and reinforce of therapeutic behavior reducing, in some cases, depressive symptoms. Unexpectedly, significant changes in prose memory and verbal fluency measures were detected. These findings suggest that further research on robotics in ecological settings is necessary to determine the extent to which they can effectively support clinical practice.

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

Abstract: In the last years, there have been rapid developments in social robotics, which bring about the prospect of their application as persuasive robots to support behavior change. In order to guide related developments and pave the way for their adoption, it is important to understand the factors that influence the acceptance of social robots as persuasive agents. This study extends the technology acceptance model by including measures of social responses. The social responses include trusting belief, compliance, liking, and psychological reactance. Using the Wizard of Oz method, a laboratory experiment was conducted to evaluate user acceptance and social responses towards a social robot called SociBot. This robot was used as a persuasive agent in making decisions in donating to charities. Using partial least squares method, results showed that trusting beliefs and liking towards the robot significantly add the predictive power of the acceptance model of persuasive robots. However, due to the limitations of the study design, psychological reactance and compliance were not found to contribute to the prediction of persuasive robots’ acceptance. Implications for the development of persuasive robots are discussed.

Mutual shaping in the design of socially assistive robots: A case study on social robots for therapy

Abstract: This paper offers a case study in undertaking a mutual shaping approach to the design of socially assistive robots. We consider the use of social robots in therapy, and we present our results regarding this application, but the approach is generalisable. Our methodology combines elements of user-centered and participatory design with a focus on mutual learning. We present it in full alongside a more general guide for application to other areas. This approach led to valuable results concerning mutual shaping effects and societal factors regarding the use of such robots early in the design process. We also measured a significant shift in participant robot acceptance pre-/post-study, demonstrating that our approach led to the two-way sharing and shaping of knowledge, ideas and acceptance.

A Holistic Approach to Behavior Adaptation for Socially Assistive Robots

Abstract: Socially assistive robotics aims at providing users with continuous support and personalized assistance, through appropriate social interactions. The design of robots capable of supporting people in heterogeneous tasks, raises several challenges among which the most relevant are the need to realise intelligent and continuous behaviours, robustness and flexibility of services and, furthermore, the ability to adapt to different contexts and needs. Artificial intelligence plays a key role in realizing cognitive capabilities like e.g., learning, context reasoning or planning that are highly needed in socially assistive robots. The integration of several of such capabilities is an open problem. This paper proposes a novel “cognitive approach” integrating ontology-based knowledge reasoning, automated planning and execution technologies. The core idea is to endow assistive robots with intelligent features in order to reason at different levels of abstraction, understand specific health-related needs and decide how to act in order to perform personalized assistive tasks. The paper presents such a cognitive approach pointing out the contribution of different knowledge contexts and perspectives, presents detailed functioning traces to show adaptation and personalization features, and finally discusses an experimental assessment proving the feasibility of the approach.

Theory of Mind in Social Robots: Replication of Five Established Human Tests

Abstract: Theory of Mind is an inferential system central to human–human communication by which people ascribe mental states to self and other, and then use those deductions to make predictions about others’ behaviors. Despite the likelihood that ToM may also be central to interactions with other types of agents exhibiting similar cues, it is not yet fully known whether humans develop ToM for mechanical agents exhibiting properties of intelligence and sociality. A suite of five tests for implicit ToM were performed (white lie test, behavioral intention task, facial affect inference, vocal affect inference, and false-belief test) for three different robots and a human control. Findings suggest that implicit ToM signals are consistent across variably human-like robots and humans, so long as the social cues are similar and interpretable, but there is no association between implicit ToM signals and explicit mind ascription; findings suggest that heuristics and deliberation of mental status of robots may compete with implicit social-cognitive reactions.

Mini: A New Social Robot for the Elderly

Abstract: The unceasing aging of the population is leading to new problems in developed countries. Robots represent an opportunity to extend the period of independent living of the elderly as well as to ameliorate their economic burden and social problems. We present a new social robot, Mini, specifically designed to assist and accompany the elderly in their daily life either at home or in a nursing facility. Based on the results of several meetings with experts in this field, we have built a robot able to provide services in the areas of safety, entertainment, personal assistance and stimulation. Mini supports elders and caregivers in cognitive and mental tasks. We present the robot platform and describe the software architecture, particularly focussing on the human–robot interaction. We give in detail how the robot operates and the interrelation of the different modules of the robot in a real use case. In the last part of the paper, we evaluated how users perceive the robot. Participants reported interesting results in terms of usability, appearance, and satisfaction. This paper describes all aspects of the design and development of a new social robot that can be used by other researchers who face the multiple challenges of creating a new robotic platform for older people.

Human-Like Guide Robot that Proactively Explains Exhibits

Abstract: We developed an autonomous human-like guide robot for a science museum. Its identifies individuals, estimates the exhibits at which visitors are looking, and proactively approaches them to provide explanations with gaze autonomously, using our new approach called speak-and-retreat interaction. The robot also performs such relation-building behaviors as greeting visitors by their names and expressing a friendlier attitude to repeat visitors. We conducted a field study in a science museum at which our system basically operated autonomously and the visitors responded quite positively. First-time visitors on average interacted with the robot for about 9 min, and 94.74% expressed a desire to interact with it again in the future. Repeat visitors noticed its relation-building capability and perceived a closer relationship with it.

Simplified Stable Admittance Control Using End-Effector Orientations

Abstract: Admittance control is used mainly for human–robot interaction. It transforms forces and torques to the desired position and orientation of the end effector. When the admittance control is in the task space, it needs the Jacobian matrix, while in the joint space, it requires the inverse kinematics. This paper modifies the admittance control using only the orientation components of the end-effector to avoid the calculation of the inverse kinematics and the Jacobian matrix. We use geometric properties, adaptive control and sliding mode control to approximate them. The stability of those controllers is proven. Experiments are presented in real time with a 2-DOF pan and tilt robot and a 4-DOF exoskeleton. The results of the experiment show the effectiveness of the proposed controllers.

Robot–City Interaction: Mapping the Research Landscape—A Survey of the Interactions Between Robots and Modern Cities

Abstract: The goal of this work is to describe how robots interact with complex city environments, and to identify the main characteristics of an emerging field that we call Robot–City Interaction (RCI). Given the central role recently gained by modern cities as use cases for the deployment of advanced technologies, and the advancements achieved in the robotics field in recent years, we assume that there is an increasing interest both in integrating robots in urban ecosystems, and in studying how they can interact and benefit from each others. Therefore, our challenge becomes to verify the emergence of such area, to assess its current state and to identify the main characteristics, core themes and research challenges associated with it. This is achieved by reviewing a preliminary body of work contributing to this area, which we classify and analyze according to an analytical framework including a set of key dimensions for the area of RCI. Such review not only serves as a preliminary state-of-the-art in the area, but also allows us to identify the main characteristics of RCI and its research landscape.

Can We Keep Him Forever? Teens’ Engagement and Desire for Emotional Connection with a Social Robot

Abstract: Today’s teens will most likely be the first generation to spend a lifetime living and interacting with both mechanical and social robots. Although human–robot interaction has been explored in children, adults, and seniors, examination of teen–robot interaction has been limited. In this paper, we provide evidence that teen–robot interaction is a unique area of inquiry and designing for teens is categorically different from other types of human–robot interaction. Using human-centered design, our team is developing a social robot to gather stress and mood data from teens in a public high school. To better understand teen–robot interaction, we conducted an interaction study in the wild to explore and capture teens’ interactions with a low-fidelity social robot prototype. Then, through group interviews we gathered data regarding their perceptions about social robots. Although we anticipated minimal engagement due to the low fidelity of our prototype, teens showed strong engagement and lengthy interactions. Additionally, teens expressed thoughtful articulations of how a social robot could be emotionally supportive. We conclude the paper by discussing future areas for consideration when designing for teen–robot interaction.

Real-Time Estimation of Drivers’ Trust in Automated Driving Systems

Abstract: Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of techniques for measuring drivers’ trust in the automated driving system during real-time applications execution. One possible approach for measuring trust is through modeling its dynamics and subsequently applying classical state estimation methods. This paper proposes a framework for modeling the dynamics of drivers’ trust in automated driving systems and also for estimating these varying trust levels. The estimation method integrates sensed behaviors (from the driver) through a Kalman filter-based approach. The sensed behaviors include eye-tracking signals, the usage time of the system, and drivers’ performance on a non-driving-related task. We conducted a study ( $$n=80$$ ) with a simulated SAE level 3 automated driving system, and analyzed the factors that impacted drivers’ trust in the system. Data from the user study were also used for the identification of the trust model parameters. Results show that the proposed approach was successful in computing trust estimates over successive interactions between the driver and the automated driving system. These results encourage the use of strategies for modeling and estimating trust in automated driving systems. Such trust measurement technique paves a path for the design of trust-aware automated driving systems capable of changing their behaviors to control drivers’ trust levels to mitigate both undertrust and overtrust.

Robotic Versus Human Coaches for Active Aging: An Automated Social Presence Perspective

Abstract: This empirical study compares elderly people’s social perception of human versus robotic coaches in the context of an active and healthy aging program. In evaluating hedonic and utilitarian value perceptions of exergames (i.e., video games integrating physical activity), we consider elderly people’s judgments of the warmth and competence (i.e., social cognition) of their assigned coach (human vs. robot). The field experiments involve 58 elderly participants in the real-life context. Leveraging a mixed-method approach that combines quantitative and qualitative data, we show that (1) socially assistive robots activate feelings of (automated) social presence (2) human coaches score higher on perceived warmth and competence relative to robotic coaches, and (3) social cognition affects elderly people’s experience (i.e., emotional and cognitive reactions and behavioral intentions) with respect to exergames. These findings can inform future developments and design of social robots and systems for their smoother inclusion into elderly people’s social networks. In particular, we recommend that socially assistive robots take complementary roles (e.g., motivational coach) and assist human caregivers in improving elderly people’s physical and psychosocial well-being.

A Social Robot Learning to Facilitate an Assistive Group-Based Activity from Non-expert Caregivers

Abstract: Socially assistive robots are a promising technology for supporting residential care facilities to provide stimulating recreational activities to residents in group settings. In order for caregivers to teach robots customized recreational activities for residents in their facilities, these robots need to be able to learn such activities from non-experts. In this work, we present a novel learning from demonstration system that allows socially assistive robots to learn customized group recreational activities from caregivers and facilitate these activities with users. We validate the usability and effectiveness of the proposed system by conducting a robot teaching study with caregivers and the Tangy robot at a local residential care facility. The caregivers found the learning system easy to use, experienced moderately low perceived workload, and were able to successfully teach Tangy the game of Bingo. Once Tangy learned the game, it autonomously facilitated Bingo games with elderly residents. The residents found the robot behaviors, personalized by the caregivers, both helpful and entertaining. Furthermore, they enjoyed playing Bingo with Tangy and would participate in future games.

Adaptive Side-by-Side Social Robot Navigation to Approach and Interact with People

Abstract: This paper presents a new framework for how autonomous social robots approach and accompany people in urban environments. The method discussed allows the robot to accompany a person and approach to other one, by adapting its own navigation in anticipation of future interactions with other people or contact with static obstacles. The contributions of the paper are manifold: firstly, we extended the Social Force model and the Anticipative Kinodynamic Planner (Ferrer and Sanfeliu, in: IEEE/RSJ international conference on intelligent robots and systems. IEEE, 2014) to the case of an adaptive side-by-side navigation; secondly, we enhance side-by-side navigation with an approaching task and a final positioning that allows the robot to interact with both people; and finally, we use findings from experiments of real-life observations of people walking in pairs to define the parameters of the human–robot interaction in our case of adaptive side-by-side. The method was validated by a large set of simulations; we also conducted real-life experiments with our robot, Tibi, to validate the framework described for the interaction process. In addition, we carried out various surveys and user studies to indicate the social acceptability of the robots performance of the accompanying, approaching and positioning tasks.

Learning Proxemics for Personalized Human–Robot Social Interaction

Abstract: Each person has their personal area which they do not want to share with others during social interactions. The size of this area usually depends on various factors such as their culture, personal traits, and acquaintanceship. The same applies to the case of human–robot interaction, especially when the robot is required to exhibit a certain level of social competence. Here, we propose a new robot navigation strategy to socially interact with people reflecting upon the social relationship between the robot and each person. To this end, we need a clear definition of interaction areas: (1) quality interaction area where people can be engaged in high-quality interactions with robots, and (2) private area not to be interfered with by the robot speech or action. A technical challenge in enhancing social human–robot interactions is how to enable robots to delineate the boundary of the two areas of each person. Specifically, the social force model (SFM) is designed by a fuzzy inference system, where the membership functions are optimized to give the robot the ability to navigate autonomously in the quality interaction area using a reinforcement learning algorithm. Finally, the proposed model was verified through simulations and experiments with a real robot that can generate a suitable SFM of each person, allowing the robot to maintain the quality of interaction with each person while keeping their private personal distance.

Human–Robot Interaction Analysis for a Smart Walker for Elderly: The ACANTO Interactive Guidance System

Abstract: The present study aims to investigate the interaction between older adults and a robotic walker named FriWalk, which has the capability to act as a navigation support and to guide the user through indoor environments along a planned path. To this purpose, we developed a guidance system named Simulated Passivity, which leaves the responsibility of the locomotion to the user, both to increase the mobility of elder users and to enhance their perception of control over the robot. Moreover, the robotic walker can be integrated with a tablet and graphical user interface (GUI) which provides visual indications to the user on the path to follow. Since the FriWalk and Simulated Passivity were developed to suit the needs of users with different deficits, we conducted a human–robot interaction experiment, complemented with direct interviews of the participants. The goals of the present work were to observe the relation between elders (with and without visual impairments) and the robot in completing a path (with and without the support of the GUI), and to collect the impressions about of the older adult participants about the interaction. Our results show an overall positive impression of the FriWalk and an evident flexibility and adaptability of its guidance system across different categories of users (e.g., with or without visual impairments). In the paper, we discuss the implications of these findings on service social robotics.

Why Do Robots Need a Head? The Role of Social Interfaces on Service Robots

Abstract: Developing effective ways for robots to communicate with humans presents many significant design challenges and requires detailed consideration of a wide range of factors. To facilitate communication between people and machines, it is common for robots to possess head-like features capable of providing social feedback through facial expressions, attention, gaze, etc. This paper explores the multifaceted roles that robotic head-like interfaces play in human–robot interaction. The research makes two main contributions. First, the paper outlines the motivations for using social interfaces on service robots and reviews key design insights from past studies in the field. Second, a taxonomy for classifying robot heads is proposed. This taxonomy has broad appeal for designers, as it gives structure to a large, disorganised design space.

Emotional and Behavioural Distraction by a Social Robot for Children Anxiety Reduction During Vaccination

Abstract: Social Assistive Robots are starting to be widely used for paediatric health-care. In this setting, the development of effective strategies to engage and remain compelling during the interaction is still an open research area since, in the case of an incoming medical procedure, children could be in an anxiety state. In this work, the proposed strategy relies on the use of a social robot interacting with the children and applying distraction strategies that are used in human–human interaction. Additionally, the robot displays emotional behaviours to attract the children attention. We present the results of a 2 months study (N = 139) conducted in a Health-Vaccines Centre, where the effects of the distraction provided by a social robot, showing such interactive behaviours interleaved with emotional ones, are compared with the same distracting strategies without any emotional social cues and with the case without the robot. Such emotional behaviours are selected with a positive or negative valence according to the initial anxiety state of the children (e.g., low or high). Outcome criteria for the evaluation of the intervention included the parents reported fear, anxiety and happiness at different stages of the interaction, self-report of the perceived pain, and an external behavioural evaluation of the pain. Results showed that the robot distraction strategies were able to reduce fear and anxiety, and increase happiness in every condition. Moreover, children perceived less pain with respect to the case of no robot. Finally, results showed that the initial children anxiety has an impact on the ability of the robot to be engaging.

Care Robot Orientation: What, Who and How? Potential Users` Perceptions

Abstract: Exploring the specific field of care robot orientation generates many questions regarding the meaning, content and how it should be conducted. The issue is important due to the general digitalisation and implementation of welfare technology and care robots. The aim of the study was to explore perceptions of care robot orientation from the potential users’ perspective. Data were collected by focus group interviews in Finland, Germany and Sweden. In all three countries, potential user groups were represented: older adults, relatives, professional caregivers and care service managers. A qualitative descriptive method was used for analysing data. The data revealed three aspects of care robot orientation: (1) What care robot orientation is, (2) Who needs it and by Whom it should be given and (3) How it should be performed. The need for care robot orientation is general in society. In the absence of knowledge about care robots, it is nearly impossible to know what to ask for or actually seek information about. Therefore, care robot orientation must be founded on agile implementation planning for care robots, with a firm basis in trustworthy knowledge and information and respecting individuals’ wishes. This also gives rise to an ethical challenge when care robots are offered to people having reduced decision-making ability (dementia, cognitive impairment), along with the issue of who then should make the decision. The mapping of the What, Who/Whom and How aspects of care robot orientation offers a foundation for the creation of orientation models, which might facilitate structured and goal-oriented care robot orientation strategies.

Gathering expert opinions for social robots' ethical, legal, and societal concerns. Findings from Four International Workshops

Abstract: Social robots, those that exhibit personality and communicate with us using high-level dialogue and natural cues, will soon be part of our daily lives. In this paper, we gather expert opinions from different international workshops exploring ethical, legal, and social (ELS) concerns associated with social robots. In contrast to literature that looks at specific challenges, often from a certain disciplinary angle, our contribution to the literature provides an overview of the ELS discussions in a holistic fashion, shaped by active deliberation with a multitude of experts across four workshops held between 2015 and 2017 held in major international workshops (ERF, NewFriends, JSAI-isAI). It also explores pathways to address the identified challenges. Our contribution is in line with the latest European robot regulatory initiatives but covers an area of research that the latest AI and robot governance strategies have scarcely covered. Specifically, we highlight challenges to the use of social robots from a user perspective, including issues such as privacy, autonomy, and the dehumanization of interactions; or from a worker perspective, including issues such as the possible replacement of jobs through robots. The paper also compiles the recommendations to these ELS issues the experts deem appropriate to mitigate compounding risks. By then contrasting these challenges and solutions with recent AI and robot regulatory strategies, we hope to inform the policy debate and set the scene for further research.

The Effect of Design Features on Relationship Quality with Embodied Conversational Agents: A Systematic Review

Abstract: Embodied conversational agents (ECAs) are increasingly used in healthcare and other settings to improve self-management and provide companionship. Their ability to form close relationships with people is important for enhancing effectiveness and engagement. Several studies have looked at enhancing relationships with ECAs through design features focused on behaviours, appearance, or language. However, this evidence is yet to be systematically synthesized. This systematic review evaluates the effect of different design features on relationship quality with ECAs. A systematic search was conducted on electronic databases EMBASE, PsychInfo, PubMed, MEDLINE, Cochrane Library, SCOPUS, and Web of Science in January–February 2019. 43 studies were included for review that evaluated the effect of a design feature on relationship quality and social perceptions or behaviours towards an ECA. Results synthesize effective design features and lay a scientific framework for improving relationships with ECAs in healthcare and other applications. Risk of bias for included studies was generally low, however there were some limitations in the research quality pertaining to outcome measurement and the reporting of statistics. Further research is needed to understand how to make ECAs effective and engaging for all consumers.

Modeling and Predicting Trust Dynamics in Human–Robot Teaming: A Bayesian Inference Approach

Abstract: Trust in automation, or more recently trust in autonomy, has received extensive research attention in the past three decades. The majority of prior literature adopted a “snapshot” view of trust and typically evaluated trust through questionnaires administered at the end of an experiment. This “snapshot” view, however, does not acknowledge that trust is a dynamic variable that can strengthen or decay over time. To fill the research gap, the present study aims to model trust dynamics when a human interacts with a robotic agent over time. The underlying premise of the study is that by interacting with a robotic agent and observing its performance over time, a rational human agent will update his/her trust in the robotic agent accordingly. Based on this premise, we develop a personalized trust prediction model and learn its parameters using Bayesian inference. Our proposed model adheres to three properties of trust dynamics characterizing human agents’ trust development process de facto and thus guarantees high model explicability and generalizability. We tested the proposed method using an existing dataset involving 39 human participants interacting with four drones in a simulated surveillance mission. The proposed method obtained a root mean square error of 0.072, significantly outperforming existing prediction methods. Moreover, we identified three distinct types of trust dynamics, the Bayesian decision maker, the oscillator, and the disbeliever, respectively. This prediction model can be used for the design of individualized and adaptive technologies.

Children Teach Handwriting to a Social Robot with Different Learning Competencies

Abstract: As robots are entering into educational fields to enhance children’s learning, it becomes relevant to explore different methods of learning in the area of child–robot interaction. In this article, we present an autonomous educational system incorporating a social robot to enhance children’s handwriting skills. The system provides a one-to-one learning scenario based on the learning-by-teaching approach where a tutor-child assess the handwriting skills of a learner-robot. The robot’s writing was generated by an algorithm incorporating human-inspired movements and could reproduce a set of writing errors. We tested the system by conducting two multi-session studies. In the first study, we assigned the robot two contrasting competencies: ‘learning’ and ‘non-learning’. We measured the differences in children’s learning gains and changes in their perceptions of the learner-robot. The second study followed a similar interaction scenario and research questions, but this time the robot performed three learning competencies: ‘continuous-learning’; ‘non-learning’ and ‘personalised-learning’. The findings of these studies show that the children learnt with the robot that exhibits learning competency and children’s learning and perceptions of the robot changed as interactions unfold, confirming the need for longitudinal studies. This research supports that the contrasting learning competencies of social robots can impact children’s learning differently in peer-learning scenarios.

Task and Design Requirements for an Affordable Mobile Service Robot for Elder Care in an All-Inclusive Care for Elders Assisted-Living Setting

Abstract: The high cost of elder care combined with the shortage of caregivers lead us to consider how service robots can be affordably leveraged to support the independence of elders and the work of their caregivers and clinicians. Our objective is to gain design insight into tasks older adults desire to accomplish daily in a low-resource, assisted living setting and how an affordable service robot could suit. A need-finding design approach consisting of focus groups and surveys was completed with three stakeholders groups: Elders, Clinicians, and Caregivers. Stakeholders were asked to identify and then prioritize service tasks by importance and frustration. Thirty-six unique high priority tasks were identified. Instrumental activities of daily living, a desire to have their preferences known, leisure activities, and increased opportunities for socialization were the most important tasks that the elders wanted a low-cost mobile service robot to address. Clinicians and caregivers prioritized highly safety-related reminders and assistance in complying with care plans in assessment of elder task needs. Service robots exist that do some, but not all of these desired tasks. An effective and affordable service robot requires design trade-offs in terms of cost, preference and complexity. A low-cost robot targeting reminders, companion walking, hydration and fetching assistance was suggested as an initial prototype. Prototypes may address high priority desires of all stakeholders, but robots that can intervene and affect long-lasting changes in elder care are still needed.

I–C–E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction

Abstract: The research community of human-robot interaction relies on theories and phenomena from the social sciences in order to study and validate robotic developments in interaction. These studies mainly concerned one (human) on one (robot) interactions in the past. The present paper shifts the attention to groups and group dynamics and reviews relevant concepts from the social sciences: ingroup identification (I), cohesion (C) and entitativity (E). Ubiquitous robots will be part of larger social settings in the near future. A conceptual framework, the I–C–E framework, is proposed as a theoretical foundation for group (dynamics) research in HRI. Additionally, we present methods and possible measures for these relevant concepts and outline topics for future research.

It Does Not Matter Who You Are: Fairness in Pre-schoolers Interacting with Human and Robotic Partners

Abstract: The relationship between humans and robots is increasingly becoming focus of interest for many fields of research. The studies investigating the dynamics underpinning the human–robot interaction have, up to date, mainly analysed adults’ behaviour when interacting with artificial agents. In this study, we present results associated with the human–robot interaction involving children aged 5 to 6 years playing the Ultimatum Game (UG) with either another child or a humanoid robot. Assessment of children’s attribution of mental and physical properties to the interactive agents showed that children recognized the robot as a distinct entity compared to the human. Nevertheless, consistently with previous studies on adults, the results on the UG revealed very similar behavioural responses and reasoning when the children played with the other child and with the robot. Finally, by analysing children’s justifications for their behaviour at the UG, we found that children tended to consider “fair” only the divisions that were exactly equal (5–5 divisions), and to justify them either in quantitative terms (outcome) or in terms of equity. These results are discussed in terms of theory of mind, as well as in light of developmental theories underpinning children’s behaviour at the Ultimatum Game.