Social robots offer a promising avenue for boosting engagement and education in pediatric clinical settings. This paper evaluates the suitability of Mirrly, a novel custom-built tabletop social robot, by evaluating children’s opinions of it through the application area of amblyopia ("lazy eye") treatment. We conducted focus group sessions with 24 children (ages 7–10 years) to gauge initial impressions of Mirrly as a health literacy tool and interactive game facilitator. Preliminary results indicate that children perceive the robot as highly knowledgeable (M = 4.696/5) and likeable (M = 4.160/5). Immediately following the interaction, 100% of participants retained key information about vision-saving treatment (eye patching) presented during the study. We discuss these findings and children’s suggestions for improving the robot’s design in the context of child-robot healthcare interactions. We provide insights for future in-clinic deployments aimed at supporting patient well-being and health outcomes for children diagnosed with amblyopia.

Most mental health conditions emerge during adolescence, making university years pivotal for intervention. Nearly 30% of students worldwide experience mental health difficulties, yet support remains constrained by stigma and limited resources. This work investigates how interactive technologies, integrated with a wearable heart-rate sensor, can enhance psychological and emotional well-being through the ancient yogic breathing practice, Nadi Shuddhi. We developed two autonomous, adaptive systems - one combining a social robot with a tablet, and one tablet-only, both delivering real-time guidance based on heart-rate variability and breath rate. A study involving 42 university students across 200 sessions revealed both systems significantly increased parasympathetic activation, mindfulness, and calmness while reducing short-term stress and depression symptoms. Compared to a tablet-only condition, the robot’s physical presence led to a significant decrease in breath rate, improved mood, higher competence, and more positive user perceptions, while usability remained comparable, highlighting its potential for supporting youth mental health through social robots with biofeedback.

A better understanding of how humans perceive robot personality variables could enable the design of more socially acceptable robots. In this exploratory study, we examined whether manipulations of an iCub robot’s voice and movements affected human participants’ perceptions of the robot’s personality. We programmed the robot to behave in different ways during a teaching scenario in which it played either a teaching, learning, or collaborative role, shown in recorded videos of human–robot interactions. A total of 240 participants in an Amazon Mechanical Turk study watched these videos and completed a series of questionnaires assessing their perceptions of the robot. Participants perceived the iCub as more extroverted when it spoke faster, with a higher pitch, and performed larger-amplitude movements. It was determined that participants’ personality dimensions were more influential in their perceptions of the robot’s TIPI and RoSAS personality dimensions than the robot’s social role and personality manipulations. Participants’ self-rated extroversion, emotional stability, and conscientiousness repeatedly appeared as significant factors affecting their perceptions of the robot’s personality. Interestingly, we observed strong perceiver effects, whereby participants’ perceptions of the robot’s personality traits were correlated with their own self-rated personality traits.

To be able to learn effectively, robots sometimes will need to select more suitable human teachers. We propose an attribute in human teachers for robots that learn through visual observations, namely human teachers’ awareness of and attention to the robot’s visual capabilities and constraints, and explore how it affects robot learning outcomes. In an in-person experiment involving 72 participants who taught three physical tasks to an iCub humanoid robot, we manipulated teachers’ awareness of the robot’s visual constraints by offering the visual perspective of the robot in one of the experimental conditions. Participants who were able to see the robot’s vision output paid increased attention to ensuring task objects were visible to the robot when providing demonstrations of physical tasks. This emphasis on attention to the robot’s view resulted in better learning outcomes for the robot, as indicated by lower perception error rates and higher learning scores. This study contributes to understanding factors in human teachers that lead to better learning outcomes for robots.

Social robots are increasingly employed as personalized coaches in educational settings, offering new opportunities for applications such as public speaking training. In this domain, emotional self-regulation plays a crucial role, especially for students presenting in a non-native language. This study proposes a novel pipeline for detecting public speaking anxiety (PSA) using multimodal emotion recognition. Unlike traditional datasets that typically rely on acted emotions, we consider spontaneous data from students interacting naturally with a social robot coach. Emotional labels are generated through knowledge distillation, enabling the creation of soft labels that reflect the emotional valence of each presentation. We introduce a lightweight multimodal model that integrates speech prosody and body posture to classify speakers by anxiety level, without relying on linguistic content. Evaluated on a collected dataset of student presentations, the system achieves 74.67% accuracy and an F1-score of 0.64. The model can operate completely disconnected from the transmission network on an NVIDIA Jetson board, safeguarding data privacy and demonstrating its feasibility for real-world deployment.

This paper presents a co-designed robot system developed through an 22-month collaboration with Speech Language Pathologists (SLPs) for the use in real-world therapeutic setting. We created persona profiles of SLPs and children with speech and language challenges to inform the development of five game types for two age groups (0-4 and 5-9 years). The system integrates a robot platform with a web-based application that facilitates real-time interaction during therapy sessions. Each game addresses specific therapeutic needs, using the developed child personas as a reference point. Prototype testing with SLPs through role-playing sessions revealed usability insights that led to system refinements, including enhanced robot dialogue, age-appropriate content adjustments, and additional interactive features. The resulting system demonstrates how human-centered design can create robotic system that addresses the practical challenges faced by SLPs and children in therapeutic settings.

Social robots have great potential in supporting individuals’ physical and mental health/wellbeing. While they have been increasingly evaluated in some domains, such as with children with autism, their evaluation has not been as extensive in other areas. We present a systematic review of domains in which social robots have been evaluated specifically in health/wellbeing contexts. We ask which robots have been evaluated, who the participants were, and how participants interacted with the robots. PRISMA guidelines for systematic reviews were followed. Articles with children as participants, using a purely robotic device, and in languages other than English were excluded. A total of 9,362 peer-reviewed articles (up to February 2021) from ACM DL, IEEE Xplore, Scopus, PubMed, and PsychInfo were identified. After applying the inclusion/exclusion criteria 443 articles were included in the review. The majority of studies were conducted at care centers while studies in hospitals/clinics have seen relatively limited attention. In many cases, the social robots were not programmed for specific health-related tasks, limiting their application. We also discuss robots used in real-world settings and propose a “Personal healthcare journey,” which includes different stages of one’s life which could benefit from a social robot, with the goal of increasing long-term adoption of social robots for supporting health/wellbeing.

Public attitudes toward stuttering are rooted in stereotypes and misconceptions, leading to negative reactions and discrimination against individuals who stutter. Previous research highlights the positive impact of educational interventions on people’s attitudes toward stuttering. The potential of social robots as an educational tool in the context of stuttering awareness remains unexplored. In the present study, we investigate whether a social robot can improve public attitudes when giving an interactive presentation on the topic. We compare its impact with a tablet-only condition. Additionally, we differentiate between two robot conditions—one in which the robot imitates stuttering and another where the robot has fluent speech. In the robot conditions, visuals are shown on a tablet. We used a co-design approach and incorporated the perspectives and experiences of two individuals with lived experiences of stuttering into our study design. A user study with 69 participants reveals significant improvements in attitudes across all three conditions, with no significant difference between conditions. However, participants perceived the robot as significantly “warmer,” more “attractive,” and “novel” when compared to the tablet. These findings provide valuable insights into the potential of social robots as intervention techniques for improving attitudes in the field of stuttering.

As robots increasingly permeate diverse domains like healthcare, education, service industries and homes, accurately understanding humans’ responses to and behaviour towards robots is crucial. While many human-robot interaction (HRI) studies focus on either quantitative or qualitative approaches, we advocate a mixed-method approach. This study investigated robot norm violations by implementing a scenario where a mobile manipulator robot and a human, in-person, carry out a physical, competitive task. Sixty-two participants were recruited and randomly assigned to either an experimental or a control condition (balanced for age/gender). The scenario was a competitive scavenger hunt game where participants took turns with a robot. We investigated the robot behaviours’ effects on trust, discomfort, competence, enjoyment, participant behaviour and physiological changes. The mixed-method approach integrated physiological measurements, behavioural observations and qualitative responses, thus offering a comprehensive account of HRI dynamics in the context of norm violations. Questionnaire results reveal significant shifts in human perceptions and attitudes when social norms are violated by robots, compared to a norm-compliant control condition. Specifically, trust and enjoyment decrease, discomfort increases and the robot’s perceived competence is compromised. These findings are extended through additional analyses of participants’ physiological changes, behaviours and responses to open-ended questions. Behavioural observations indicated increased verbal engagement and emotional responses, while physiological data showed elevated stress levels in the experimental group. Our study highlights the advantage of a mixed-methods approach combining different qualitative and quantitative data, providing a more comprehensive picture of participants’ perceptions of a robot, and how they react and respond to robot norm violations.

As robots integrate more into daily life, socially aware robots with specific social attributes and behaviors are necessary. This review aims to explore how social norms in Human–Robot Interaction (HRI) impact robot design and human perception. We searched for relevant articles in the following databases: ACM Digital Library, IEEE Digital Library, Scopus, Springer Link, and PsycINFO. After applying inclusion and exclusion criteria, a final set of 69 articles were included in the review. These articles were categorized based on whether they examined norm conformity or norm violations, and were further sorted into 12 categorical norm labels to assist in analysis and comparison. By examining the existing literature, this review uncovers how social norms impact aspects of HRIs like trust, acceptance, and comfort while highlighting the importance of aligning robot design with user expectations. It reveals design challenges such as accounting for cultural variations, context-specific norms, and evolving norms over time. Addressing these challenges has the potential to improve user experiences, promote broader acceptance of robots, and foster successful integration of robots into various domains. The findings contribute to the ongoing discussion on the role of social norms in HRI, offering valuable insights and a foundation for future research.

Public speaking anxiety is one of the most common subtypes of social anxiety and is a prevalent concern among university students. Many students experience excessive anxiety when giving presentations in front of other people, which can negatively impact their academic performance and overall mental well-being. With limited access to human coaches and interventions, there is a need for innovative technological solutions, including social robots, to extend and enhance mental health support and accessibility. In this article, we first outline a co-design study with five mental health professionals and a participatory design study with six university students, aiming to design a robotic mental well-being coach to help university students manage public speaking anxiety. Afterwards, we detail a user study with 50 university students to evaluate the usability and acceptability of the developed robotic mental well-being coach system. The findings showed that the robotic coach system, which includes the robot and a tablet, received a usability score of 84.05 and had high acceptability among participants who perceived the robot as knowledgeable and competent. Moreover, participants’ self-reported moods significantly improved following the study. Overall, the qualitative and quantitative analyses in this study yield promising results regarding the potential use of robotic coaches to help university students manage their public speaking anxiety.

Adaptive task planning is fundamental to ensuring effective and seamless human-robot collaboration. This paper introduces a robot task planning framework that takes into account both human leading/following preferences and performance, specifically focusing on task allocation and scheduling in collaborative settings. We present a proactive task allocation approach with three primary objectives: enhancing team performance, incorporating human preferences, and upholding a positive human perception of the robot and the collaborative experience. Through a user study, involving an autonomous mobile manipulator robot working alongside participants in a collaborative scenario, we confirm that the task planning framework successfully attains all three intended goals, thereby contributing to the advancement of adaptive task planning in human-robot collaboration. This paper mainly focuses on the first two objectives, and we discuss the third objective, participants' perception of the robot, tasks, and collaboration in a companion paper.

Mirrly is a new humanoid robot designed to facilitate human-robot social interactions, focusing on applications in therapy and education for children. Inspired by the need for engaging and effective interactions, Mirrly’s design incorporates a friendly appearance, articulated expressive face, and multimodal interaction capabilities. A key goal of designing Mirrly was keeping costs low while still retaining affective and expressive qualities of the robot. This paper presents the process of development and design of Mirrly. We also delve into the decisions we made to choose and design hardware, software, and interactions. By comparing existing robots and exploring implications for future research, Mirrly demonstrates the potential for advancing Child-Robot Interaction in many application areas. The open-source nature of Mirrly’s platform further encourages collaboration and innovation in the research community, offering a versatile and potentially impactful platform for a diverse range of applications in human-robot social interactions.

For robots to perform assistive tasks in unstructured home environments, they must learn and reason on the semantic knowledge of the environments. Despite a resurgence in the development of semantic reasoning architectures, these methods assume that all the training data is available a priori. However, each user’s environment is unique and can continue to change over time, which makes these methods unsuitable for personalized home service robots. Although research in continual learning develops methods that can learn and adapt over time, most of these methods are tested in the narrow context of object classification on static image datasets. In this paper, we combine ideas from continual learning, semantic reasoning, and interactive machine learning literature and develop a novel interactive continual learning architecture for continual learning of semantic knowledge in a home environment through human-robot interaction. The architecture builds on core cognitive principles of learning and memory for efficient and real-time learning of new knowledge from humans. We integrate our architecture with a physical mobile manipulator robot and perform extensive system evaluations in a laboratory environment over two months. Our results demonstrate the effectiveness of our architecture to allow a physical robot to continually adapt to the changes in the environment from limited data provided by the users (experimenters), and use the learned knowledge to perform object fetching tasks.

For social robots to succeed in places such as homes, they must learn new skills from various people and act in a manner desirable to different users. We introduce a novel biologically inspired approach for robot learning through program-level imitation, inspired by the way primates, including humans, understand and perform complex actions. Our approach enables robots to discover the hierarchical structure of tasks by identifying sequential regularities and sub-goals from diverse human demonstrations. To do so, human-provided demonstrations, which can be obtained by a robot through different modalities (such as kinesthetic teaching, behavioural observation, and verbal instruction), are processed by an algorithm that discovers multiple possibilities for arranging observed sub-goals to achieve a final goal. Prior to acting, the available sequences are evaluated based on user-defined criteria, through mental simulation of the task by the robot, to find the optimal sequence of actions. As a proof-of-concept, we implemented our system on an iCub humanoid robot and present here how our method allowed the robot to adapt its action sequences for task execution when starting the task from different states, incorporating user preference for finishing the task as fast as possible. Our envisaged system is meant to accommodate variations in human teaching styles and is expected to help a robot perform tasks with greater flexibility and efficiency. This work contributes by proposing a framework for robots to learn from humans at an abstract level, opening the way to more adaptable and intelligent robotic assistants in everyday tasks.

HRI research has evolved to take a broader, more inclusive view of how culture influences our interaction with robots. As we delve deeper into cultural integration in HRI, it has become evident that while integrating cultural aspects offers new opportunities, it requires careful consideration due to the heightened sensitivity to the fluid nature of cultural dynamics. Our study focuses on a particular case and examines the role of context and personal preferences in a restaurant setting. We investigate how preferences for cross-cultural greetings performed by a humanoid robot can change based on the restaurant theme and describe what factors influence these preferences by looking at two different groups who participated based on different ethnic greetings. Our study reveals insight into how ethnicity, percentage of life lived in Western countries, personality variations, and implementation of cultural aspects influence the likability of robotic greeting gestures. Our investigations highlight the complexity of creating culturally adaptive robots that demonstrate the cultural norms and gestures that align with the expectations of the respective cultural groups.

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human–Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in CL, however, has been robot-centered to develop CL algorithms that can quickly learn new information on systematically collected static datasets. In this article, we take a human-centered approach to CL, to understand how humans interact with, teach, and perceive CL robots over the long term, and if there are variations in their teaching styles. We developed a socially guided CL system that integrates CL models for object recognition with a mobile manipulator robot and allows humans to directly teach and test the robot in real time over multiple sessions. We conducted an in-person study with 60 participants who interacted with the CL robot in 300 sessions with 5 sessions per participant. In this between-participant study, we used three different CL models deployed on a mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. Our analysis shows that the constrained experimental setups that have been widely used to test most CL models are not adequate, as real users interact with and teach CL robots in a variety of ways. Finally, our analysis shows that although users have concerns about CL robots being deployed in our daily lives, they mention that with further improvements CL robots could assist older adults and people with disabilities in their homes.

Past studies have emphasized the importance of adjusting agent personalities for improving users’ acceptance and engagement. However, it is not yet clear how agent personalities can be decided on, as preferences have highly varied in different studies and are task/context dependent. In this proof of concept study, we use Affect Control Theory (ACT) to evaluate perceived affective dimensions of personality (called identities thereafter) of 11 different social robots, and study how this perception affects participants’ interests in interacting with the robots. We ask whether ACT can be used as a novel approach to identify participants’ preferred identities for robots in health/therapy contexts. An online study with 95 participants (a total of 1045 robot ratings) was conducted. Our study supports the use of ACT for understanding users’ preferences for social robot identities measured through robot images: the closer the participants rated their own identity to a robot’s, the more interested they reported to be in using the robot in a health/well-being context. We also report on different factors that influenced rating of social robots as described by the participants, such as robot’s size, animal/human-likeness, and perceived friendliness and complexity. We finally discuss advantages of using ACT as an alternative method, compared to Big 5 dimensions, to assess user and agent/robot identities and to guide personalization.

Dangers in modern human society are commonly attributed to the safety of online activities. In the domain of cybersecurity, Social Engineering (SE) relates to how attackers manipulate and coerce their targets into divulging sensitive information. One major problem in designing social engineering defenses is making users aware they are being targeted. In the context of fostering human empowerment and building an inclusive society, we explore the possibility of leveraging social robot companions to provide improved protection for individuals and companies against cybersecurity attacks, specifically focusing on the realm of social engineering (SE) tactics. We asked participants to play an immersive interactive storytelling game, challenging them with risky and social-engineering-related decisions and monitoring their explicit (i.e., decisions) and implicit (i.e., mouse trajectories and facial expressions) behavior. After each decision, the Furhat tabletop robot intervened, always suggesting the not-selected option. We compared two Compliance Gaining Behaviors (CGBs) the robot could use, either leveraging affection with the participants or logical thinking. Overall, Furhat’s interventions increased the acceptance of risky and SE proposals. However, comparing the situations in which the robot tried to convince participants to avoid a social engineering request to those in which it tried to persuade them to accept it, the former was significantly more successful. Also, participants struggled with ignoring Furhat’s advice, as shown by their more uncertain mouse trajectories and negative emotional valence. From the latter results, we trained a Decision Tree model, based on mouse trajectory features only, to predict if participants would change their minds with an accuracy of 64.9%. Such defense mechanisms could help better understand users’ decision-making process in cybersecurity and social engineering, designing more helpful and supportive robot companions.

Public speaking anxiety, one of the most common subtypes of social anxiety, is prevalent among university students and can negatively impact their academic success and mental well-being. Limited access to human coaches and interventions calls for innovative technological solutions, including social robots, to extend and complement mental health support and increase accessibility. This study employs a co-design approach to design a robotic mental well-being coach aimed at assisting university students in managing public speaking anxiety. Collaborative co-design sessions with five mental health professionals were conducted to identify the design-related needs (i.e., robot behaviour and interactions) for developing a robotic coach that can effectively assist students’ public speaking anxiety. In addition, a co-design study involving university students was conducted to gather opinions for further improvements of the robotic coach. Students provided feedback on the developed system and generally found the robot engaging, relaxing, knowledgeable, and beneficial for learning relaxation exercises. The findings provide insights into the development of a robotic coach for supporting university students in managing public speaking anxiety.

The successful integration of domestic service robots into home environments can bring significant services and convenience to the general population and possibly mitigate important societal issues, such as care provision for older adults. However, home environments are complex, dynamic and object-rich. It is, thus, very probable that service robots will encounter ambiguity while interacting with household items. To enable service robots to be more adaptive, we proposed a learning so-cial referencing computational framework and experimentally evaluated the framework on a mobile manipulator robot, Fetch, in object selection scenarios. The framework allows the robot to (1) detect and analyze the ambiguity level based on the robot's view and user's command, (2) assess the human's attention level and attract their attention, (3) disambiguate references to objects using human feedback and (4) learn novel objects after clarification from the user. System evaluation results are presented. The framework is modular and can be applied to different robotic platforms.

Between 2% to 5% of children are affected by Developmental Coordination Disorders in Canada and have been diagnosed with upper limb impairments, which affect their daily lives and reduces their autonomy. Motor impairments can be part of progressive disorders, so despite regular therapy, progress remains fleeting. Affected individuals therefore consistently face many barriers, including entertainment opportunities, as availability of off-the-shelf inclusive technology is very limited. Our long-term goal is to develop a play-mediator robot, which would facilitate play between children with motor impairments and their peers or family members. Here, games that the robot can play are remotely controlled by the participants, using appropriate interfaces (e.g. joysticks). In this paper, we take the first step towards that goal and develop an adaptive joystick controller that can compensate for individual deficits. We monitor movement statistics to determine if re-calibration of the controller is necessary. Moreover, we propose a computational model of data ‘distortion’, as a tool for developers to test their technology in the very early stages of prototype development, without requiring access to participants. This work is validated with data from healthy adults and children with upper limb impairments.

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been robot-centered to develop continual learning algorithms that can quickly learn new information on static datasets. In this paper, we take a human-centered approach to continual learning, to understand how humans teach continual learning robots over the long term and if there are variations in their teaching styles. We conducted an in-person study with 40 participants that interacted with a continual learning robot in 200 sessions. In this between-participant study, we used two different CL models deployed on a Fetch mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. The results also show that although there is a difference in the teaching styles between expert and non-expert users, the style does not have an effect on the performance of the continual learning robot. Finally, our analysis shows that the constrained experimental setups that have been widely used to test most continual learning techniques are not adequate, as real users interact with and teach continual learning robots in a variety of ways. Our code is available at https://github. com/aliayub7/c1-hri.

For robots to assist users with household tasks, they must first learn about the tasks from the users. Further, performing the same task every day, in the same way, can become boring for the robot’s user(s), therefore, assistive robots must find creative ways to perform tasks in the household. In this paper, we present a cognitive architecture for a household assistive robot that can learn personalized breakfast options from its users and then use the learned knowledge to set up a table for breakfast. The architecture can also use the learned knowledge to create new breakfast options over a longer period of time. The proposed cognitive architecture combines state-of-the-art perceptual learning algorithms, computational implementation of cognitive models of memory encoding and learning, a task planner for picking and placing objects in the household, a graphical user interface (GUI) to interact with the user and a novel approach for creating new breakfast options using the learned knowledge. The architecture is integrated with the Fetch mobile manipulator robot and validated, as a proof-of-concept system evaluation in a large indoor environment with multiple kitchen objects. Experimental results demonstrate the effectiveness of our architecture to learn personalized breakfast options from the user and generate new breakfast options never learned by the robot

Play is valuable in making therapy more enjoyable, and has been studied intensively in human-robot interaction. However, the use of robots as play-mediators in multiplayer games, and the study of the dynamics of players have barely been explored. In this work, pairs of participants played with the MyJay robot in a game with two collaborative conditions (Shared and Fusion). In the Shared condition, participants shared the tasks and in the Fusion condition, participants had to synchronize their commands for the robot. In previous work, we analyzed the video recordings and questionnaires and observed that participants perceived the Fusion condition as more challenging, and requiring more coordination, while the Shared condition was perceived as more enjoyable. This paper will report on new analyses based on physiological and joystick data. The results revealed different patterns of heart rate and usage of the joysticks in the two conditions, while no link between physiological data and enjoyment was found.

Maintaining physical, emotional and psychological health is vital for well-being. Social robots have been increasingly used in healthcare to support physical and mental health. Providing appropriate, adaptive and personalised feedback based on the user’s internal states is crucial for effective and engaging human-robot interaction, especially in one-to-one interaction scenarios. In this research, we developed an adaptive and autonomous system, integrating a social robot, a wearable non-intrusive Polar chest sensor and algorithms to guide people in three application scenarios to promote physical, emotional, and psychological well-being. The social robot senses users’ psychophysiological measures such as heart rate and heart-rate variability via the wearable sensor, monitors their stress responses, provides real-time feedback and guides them to perform activities. We detail the system development and a pilot study with fifteen participants to evaluate the system in the three scenarios. The findings suggest that the autonomous system could effectively guide participants through the activities by regulating their stress responses. Participants’ physiological data also support these results. Moreover, the system was well-accepted by its users.

Social robots have the potential to serve as coaches for public speaking training. To design successful social robots, it is important to understand the expectations and perceptions of prospective users of such robots. In this paper, we present thematic analyses of comments made by 168 participants in an online study where participants watched videos of agents in the role of a public speaking coach. The study had a between-participant design with three conditions: two conditions with a humanoid social robot in either (1) active listening mode, i.e., using non-verbal backchanneling, or (2) passive listening mode, and (3) a voice assistant agent. The themes identified and discussed can contribute to the development of social robots and other agents as public speaking coaches.

This study aimed to investigate the effects of norm-violating behaviour on human perception and attitudes towards robots. Specifically, we examined the impact of a robot performing social norm violations in the context of a competitive scavenger hunt game. During the game, the robot was programmed to engage in predefined behaviours considered as social norm violations, including both injunctive and descriptive norm violations (e.g., cheating, and making loud noises). The study used an experimental and control group, with participants either exposed to norm-violating behaviour or not, respectively. The results indicated that participants in the experimental group had a strong awareness of the norm-violating behaviour according to self-reported assessments. Additionally, post-questionnaire results revealed a significant difference in trust, overall enjoyment, and discomfort between the two groups. These findings show that in our study, participants expected robots to abide by both types of social norms (i.e., injunctive and descriptive) and that violations of them negatively impacted participants’ perceptions and attitudes towards robots. This further emphasizes the importance of considering social norms in the design and programming of robots for human-robot interactions.

With the introduction of collaborative robots, humans and robots can now work together in close proximity and share the same workspace. However, this collaboration presents various challenges that need to be addressed to ensure seamless cooperation between the agents. This paper focuses on task planning for human-robot collaboration, taking into account the human’s performance and their preference for following or leading. Unlike conventional task allocation methods, the proposed system allows both the robot and human to select and assign tasks to each other. Our previous studies evaluated the proposed framework in a computer simulation environment. This paper extends the research by implementing the algorithm in a real scenario where a human collaborates with a Fetch mobile manipulator robot. We briefly describe the experimental setup, procedure and implementation of the planned user study. As a first step, in this paper, we report on a system evaluation study where the experimenter enacted different possible behaviours in terms of leader/follower preferences that can occur in a user study. Results show that the robot can adapt and respond appropriately to different human agent behaviours, enacted by the experimenter. A future user study will evaluate the system with human participants.

Emotions can provide a natural communication modality to complement the existing multi-modal capabilities of social robots, such as text and speech, in many domains. We conducted three online studies with 112, 223, and 151 participants, respectively, to investigate the benefits of using emotions as a communication modality for Search And Rescue (SAR) robots. In the first experiment, we investigated the feasibility of conveying information related to SAR situations through robots’ emotions, resulting in mappings from SAR situations to emotions. The second study used Affect Control Theory as an alternative method for deriving such mappings. This method is more flexible, e.g., allows for such mappings to be adjusted for different emotion sets and different robots. In the third experiment, we created affective expressions for an appearance-constrained outdoor field research robot using LEDs as an expressive channel. Using these affective expressions in a variety of simulated SAR situations, we evaluated the effect of these expressions on participants’ (in the role rescue workers) situational awareness. Our results and proposed methodologies (a) provide insights on how emotions could help conveying messages in the context of SAR, and (b) show evidence on the effectiveness of adding emotions as a communication modality in a (simulated) SAR communication context.

To be successful, robots that can learn new tasks from humans should interact effectively with them while being trained, and humans should be able to trust the robots’ abilities after teaching. Typically, when human learners make mistakes, their teachers tolerate those errors, especially when students exhibit acceptable progress overall. But how do errors and appearance of a trainee robot affect human teachers’ trust while the robot is generally improving in performing a task? First, an online survey with 173 participants investigated perceived severity of robot errors in performing a cooking task. These findings were then used in an interactive online experiment with 138 participants, in which the participants were able to remotely teach their food preparation preferences to trainee robots with two different appearances. Compared with an untidy-looking robot, a tidy-looking robot was rated as more professional, without impacting participants’ trust. Furthermore, while larger errors at the end of iterative training had a greater impact, even a small error could significantly reduce trust in a trainee robot performing the domestic physical task of food preparation, regardless of the robot’s appearance. The present study extends human–robot interaction knowledge about teachers’ perception of trainee robots, particularly when teachers observe them accomplishing domestic physical tasks.

Planning an autonomous vehicle’s (AV) path in a space shared with pedestrians requires reasoning about pedestrians’ future trajectories. A practical pedestrian trajectory prediction algorithm for the use of AVs needs to consider the effect of the vehicle’s interactions with the pedestrians on pedestrians’ future motion behaviours. In this regard, this paper systematically reviews different methods proposed in the literature for modelling pedestrian trajectory prediction in presence of vehicles that can be applied for unstructured environments. This paper also investigates specific considerations for pedestrian-vehicle interaction (compared with pedestrian-pedestrian interaction) and reviews how different variables such as prediction uncertainties and behavioural differences are accounted for in the previously proposed prediction models. PRISMA guidelines were followed. Articles that did not consider vehicle and pedestrian interactions or actual trajectories, and articles that only focused on road crossing were excluded. A total of 1260 unique peer-reviewed articles from ACM Digital Library, IEEE Xplore, and Scopus databases were identified in the search. 64 articles were included in the final review as they met the inclusion and exclusion criteria. An overview of datasets containing trajectory data of both pedestrians and vehicles used by the reviewed papers has been provided. Research gaps and directions for future work, such as having more effective definition of interacting agents in deep learning methods and the need for gathering more datasets of mixed traffic in unstructured environments are discussed.

Mental health problems are on the rise among university students. Many students face overwhelming stress and anxiety when participating in different activities and interacting with peers, which can affect their performance and mental well-being. However, many are unlikely to seek or receive help. Intelligent agents can offer the possibility of delivering health and mental well-being interventions with the aim of extending and complementing mental health interventions and increasing accessibility. To provide efficient interventions for students, it is imperative to identify design elements and functionalities that are most effective for engaging students. In this paper, we conducted an online survey with 85 participants (undergraduate and graduate students) to investigate preferences for using intelligent agents (e.g., conversational agents, social robots, etc.) to support their mental well-being, specifically to deal with feelings of stress and anxiety in social situations that are common in academic contexts. We asked students to complete a questionnaire in order to explore students’ experience of anxiety and their perceptions of different aspects of intelligent agents in the context of managing anxiety. The results provide insights on different social and technical capabilities as well as design elements that need to be considered when developing intelligent agents to help address stress and anxiety among university students.

One of the fundamental modes of learning in children is through curiosity. Children (and adults) interact with new people, learn about novel objects, activities and other stimuli through curiosity and other intrinsic motivations. Creating autonomous robots that learn continually through intrinsic curiosity may result in breakthroughs in artificial intelligence. Such robots could continue to learn about themselves and the world around them through curiosity, thus improving their abilities over their ‘lifetime’. Although recent works on curiosity in different fields have produced significant results, most of these works have focused on constrained simulated environments which do not involve human interaction. However, in real-world applications such as healthcare, home-assistance etc., robots generally have to interact with humans on a regular basis. In these scenarios, it is imperative that curiosity is directed towards seeking out and learning important information from the humans when needed rather than simply learning in an unsupervised manner. Further, there is limited work on how humans perceive such curious robots and whether humans prefer curious robots that adapt over time to other robots that simply perform their assigned tasks. In this workshop, our goal is to bring together researchers and practitioners in different multidisciplinary fields to discuss the role of robot curiosity in real-world applications and its implications in human-robot interaction (HRI).

Stuttering detection and classification are important issues in speech therapy as they could help therapists track the progression of patients’ dysfluencies. This is also an important tool for technology-assisted speech therapy. In this paper, we combine MFCC and phoneme probabilities to train a neural network for stuttering detection and classification of four dysfluency types. We evaluate our system on the UCLASS, FluencyBank and SEP-28K datasets and show that our system is effective and suitable for real-time applications.

Students with a learning disability (LD) generally require supplementary one-to-one instruction and support to acquire the foundational academic skills learned at school. Because learning is more difficult for students with LD, students can frequently display off-task behaviours to avoid attempting or completing challenging learning tasks. Re-directing students back to their learning task is a frequent strategy used by educators to support students. However, there have been limited studies investigating the use of assistive technology to support student re-direction, specifically in a "real-world" educational setting. In this in situ study, we investigate the impact of integrating socially assistive robot to provide re-direction strategies to students. A social robot, QT, was employed within the existing learning program during one-to-one remedial instruction sessions. The study comprised two phases, "Instruction as usual" (IAU) and "Robot-mediated instructions" (RMI). Both followed the students' one-to-one instructional program where students get personalised learning support from their instructors, except for the RMI phase which included a social robot as a tool. We investigated the impact of the robot on students' on-task behaviours and progress towards learning goals. The results of our mixed method analysis suggest that the robotic intervention supported students in staying on-task and completing their learning goal.

Recent advances in collaborative robots have provided an opportunity for the close collaboration of humans and robots in a shared workspace. To exploit this collaboration, robots need to plan for optimal team performance while considering human presence and preference. This paper studies the problem of task selection and planning in a collaborative, simulated scenario. In contrast to existing approaches, which mainly involve assigning tasks to agents by a task allocation unit and informing them through a communication interface, we give the human and robot the agency to be the leader or follower. This allows them to select their own tasks or even assign tasks to each other. We propose a task selection and planning algorithm that enables the robot to consider the human’s preference to lead, as well as the team and the human’s performance, and adapts itself accordingly by taking or giving the lead. The effectiveness of this algorithm has been validated through a simulation study with different combinations of human accuracy levels and preferences for leading.

Social robots have been used in mental health care interventions not only to increase access to mental health treatments, but also to complement the support provided by practitioners. We propose incorporating social robots in conventional treatments for children and adolescents with Social Anxiety Disorder (SAD). Although non-robotic, evidence-based interventions for social anxiety are already available, factors such as embarrassment, and anticipatory anxiety have led to treatment delay and avoidance among this clinical population. To encourage treatment and to further improve treatment outcomes, in this work-in-progress article we propose the incorporation of social robots in conventional treatments for SAD. Social robots offer many advantages, such as adaptability, being non-judgmental, and providing interaction capabilities, which could make them a useful tool in the hands of practitioners working with children and adolescents with SAD. We discuss the different roles that social robots could play in helping children with social anxiety make the most of conventional treatments. We also present preliminary results (68 participants) on adolescents’ preferences for using intelligent agents in promoting mental well-being. We conclude by summarizing the potential benefits and limitations of using social robots in conventional treatments for social anxiety.

Playing games is an inherent part of children’s lives as it impacts several aspects of their physical and mental development. Technological advances have been manifesting new and exciting avenues of interaction when children play games, ranging from board and card games, to videogames that are played on mobile devices, virtual and augmented reality (VR/AR) headsets, robotic systems, and social agents. These games encompass a wide range of applications aiming to provide educational benefits, promote development, enhance well-being, or simply enjoy leisure time. Along with the fun and excitement, these advancements also bring unique challenges in the game design process due to the inclusion of complex technology, the maximization of the players’ engagement and expectations and interests of the children. The player-centric approach of co-designing games with the target audience has a unique position as it involves creating the games for and with the children, allowing them to act as an equal stakeholder rather than simple users or informants. This half-day workshop aims to expose the researchers to collaborative techniques used in game design to create interactive and playful activities for children that involve contemporary technologies such as AR/VR and social agents.

Bullying in schools is a widespread problem with serious consequences. We are exploring the use of social robots and role-playing to foster anti-bullying peer-support. In this paper, we present results from a co-design study with 22 children (8-12 years old) to explore how they envision a “student robot”. To understand how they conceptualize bullying in this context (e.g. whether robots can be bullied), we also investigated how they envision this robot’s various social interactions. We prompted children to imagine a fictional robot about their age and follow a stepwise process to design the robot, and make stories about its interactions. Qualitative analysis of this study suggests themes of robots being described as highly-customizable characters with predominantly positive traits, that are imperfect. We also found that children can articulate various scenarios involving robots taking roles of bullies, victims, and bystanders. These findings contribute insights for designing pedagogical robots and anti-bullying interventions for children.

When humans make a mistake, they often try to employ some strategies to manage the situation and possibly mitigate the negative effects of the mistake. Robots that operate in the real world will also make errors and therefore might benefit from such recovery strategies. In this work, we studied how different verbal expression strategies of a trainee humanoid robot when committing an error after learning a task influence participants’ intention to use it. We performed a virtual experiment in which the expression modes of the robot were as follows: (1) being silent; (2) verbal expression but ignoring any errors; or (3) verbal expression while mentioning any error by apologizing, as well as acknowledging and justifying the error. To simulate teaching, participants remotely demonstrated their preferences to the robot in a series of food preparation tasks; however, at the very end of the teaching session, the robot made an error (in two of the three experimental conditions). Based on data collected from 176 participants, we observed that, compared to the mode where the robot remained silent, both modes where the robot utilized verbal expression could significantly enhance participants’ intention to use the robot in the future if it made an error in the last practice round. When no error occurred at the end of the practice rounds, a silent robot was preferred and increased participants’ intention to use.

It has been suggested that autonomous vehicles can improve efficiency and safety of the transportation systems. While research in this area often focuses on autonomous vehicles which operate on roads, the deployment of low-speed, autonomous vehicles in unstructured, crowded environments has been studied less well and requires specific considerations regarding their interaction with pedestrians. For making the operation of these vehicles acceptable, their behaviour needs to be perceived as safe by both pedestrians and the passengers riding the vehicle. In this paper we conducted an online survey with 116 participants, to understand people’s preferences with respect to an autonomous golf cart’s behaviour in different interaction scenarios. We measured people’s self-reported perceived safety towards different behaviour of the cart in a variety of scenarios. Results suggested that despite the unstructured nature of the environment, the cart was expected to follow common traffic rules when interacting with a group of pedestrians.

The use of collaborative robots is becoming more widespread across industries. This makes it essential to study robot planning in order to work effectively and smoothly with human teammates while maintaining a positive human perception of the robots. This paper evaluates the influence of a robot’s strategy and decision making on the participants’ perception of the robot. We designed an online experiment where a robot and participants need to collaborate and organize a set of objects. We studied three different strategies where the robot either prioritizes the human’s objective, its own objective, or uses a balanced strategy. We then analyze and report the results based on participants’ answers to questionnaires before and after the experiment, their comments, and their actions during the experiment. The results show that strategies prioritizing the human’s objective, or balancing between the robot’s and the human’s objectives can effectively improve participants’ perception of the robot and create a collaborative environment.

When human students practise new skills with a teacher, they often display nonverbal behaviours (e.g., head and limb movements, gaze, etc.) to communicate their level of understanding and expressing their interest in the task. Similarly, a student robot's capability to provide human teachers with social signals to express its internal state might improve learning outcomes. This could also lead to a more successful social interactions between intelligent robots and human teachers. However, to design successful nonverbal communication for a robot, we first need to understand how human teachers interpret such nonverbal cues when watching a trainee robot practising a task. Therefore, in this paper, we study the effects of different gaze behaviours as well as manipulating speed and smoothness of arm movement on human teachers' perception of a robot's (a) confidence, (b) eagerness to learn, and (c) attention to the task. In an online experiment, we asked the 167 participants (as teachers) to rate the behaviours of a trainee robot in the context of learning a physical task. The results suggest that splitting the robot's gaze between the teacher and the task not only affects the perceived attention, but can also make the robot appear to be more eager to learn. Furthermore, perceptions of all three attributes tested were systematically affected by varying parameters of the robot's arm movement trajectory while performing task actions.

It is anticipated that intelligent robots will gain the ability to learn from humans how to perform tasks, and will assist them in many contexts such as with household chores in the near future; therefore, people should have the confidence to trust these robots after teaching them how to do a task. Like most machines, robots may sometimes behave in an erroneous manner and such errors can easily undermine trust in the robots, depending on their severity. Nevertheless, when a robot has been taught a task by humans, we hypothesize that the teachers may ignore small mistakes made by the robot, if it shows significant improvements while practising the task. We first conducted a study with 173 participants in which the perceived severity of different robot errors in a household chore (preparing food) was investigated. We then used the results to create scenarios of different levels of severity and conducted a second study with 138 participants to investigate the impact of error severity on trust. Participants remotely taught their preferences in food preparation tasks to robots. Over several practice rounds, robots’ behaviour improved, but the robots made either (a) no errors, (b) a small, or (c) a big error at the end, depending on the experimental condition. Small errors significantly affected trust and big errors had an even more adverse impact. Trust in the robot was found to be correlated with personality traits of the participants as well as with their disposition to trust other people.

Technological advancements in creating and commercializing novel unobtrusive and wearable physiological sensors generate new opportunities to develop adaptive human-robot interaction (HRI) scenarios. Detecting complex human states such as engagement and stress when interacting with social agents could bring numerous advantages to create meaningful interactive experiences. Despite being widely used to explain human behaviors in post-interaction analysis with social agents, using bodily signals to create more adaptive and responsive systems remains an open challenge. This paper presents the development of an open-source, integrative, and modular library created to facilitate the design of physiologically adaptive HRI scenarios. The HRI Physio Lib streamlines the acquisition, analysis, and translation of human body signals to additional dimensions of perception in HRI applications using social robots. The software framework has four main components: signal acquisition, processing and analysis, social robot and communication, and scenario and adaptation. Information gathered from the sensors is synchronized and processed to allow designers to create adaptive systems that can respond to detected human states. This paper describes the library and presents a use case that uses a humanoid robot as a cardio-aware exercise coach that uses heartbeats to adapt the exercise intensity to maximize cardiovascular performance. The main challenges, lessons learned, scalability of the library, and implications of the physio-adaptive coach are discussed.

Robots create a window of opportunity to challenge the barriers that children with special needs face. Robots are physical agents that can be imbued with seemingly "intelligent" behaviours. They can facilitate accessible play by acting as proxies to both children with physical special needs and typically developing children, creating an even playing field. Including target users such as children with special needs in the design process is essential in creating a child-friendly robot that ensures repeated use, engagement and long-term interaction. This paper presents an online approach to involve stakeholders with user-centered design, exemplifying that children can be included in the creation and feedback process even when it is not possible to hold in-person co-design sessions due to COVID-19. We present qualitative findings from a user-centered design study and offer recommendations for designing social robots for accessible play and facilitating child-child interaction.

Bullying in schools is a serious issue with severe and long-term consequences. We explore using social robots in anti-bullying programs to encourage children to intervene in bullying of their peers. To that end, we have conducted a crowdsourced study to explore the feasibility of using robots in the context of bullying (i.e., to investigate whether robots are perceived as entities that can be bullied). We present qualitative and quantitative results from a between-subjects video study, comparing robot bullying (robots being bullied) to human bullying (humans being bullied). Our findings suggest that while the majority of participants describe both instances with connotations of wrongness and immorality, they use different cognitive mechanisms for moral disengagement with robot bullying vs human bullying. We also found significant differences in participants’ perceptions of each scenario, including associating robot mistreatment with bullying less strongly, and being less willing to intervene in it. This work contributes insights for understanding how people perceive bullying of robots, designing intelligent behaviors to discourage bullying of robots, and to our long-term goal of developing anti-bullying pedagogical programs that use social robots.

Intelligent assistive robots can enhance the quality of life of people with dementia and their caregivers. They can increase the independence of older adults, reduce tensions between a person with dementia and their caregiver, and increase social engagement. This article provides a review of assistive robots designed for and evaluated by persons with dementia. Assistive robots that only increased mobility or brain-computer interfaces were excluded. Google Scholar, IEEE Digital Library, PubMed, and ACM Digital Library were searched. A final set of 53 articles covering research in 16 different countries are reviewed. Assistive robots are categorized into five different applications and evaluated for their effectiveness, as well as the robots’ social and emotional capabilities. Our findings show that robots used in the context of therapy or for increasing engagement received the most attention in the literature, whereas the robots that assist by providing health guidance or help with an activity of daily living received relatively limited attention. PARO was the most commonly used robot in dementia care studies. The effectiveness of each assistive robot and the outcome of the studies are discussed, and particularly, the social/emotional capabilities of each assistive robot are summarized. Gaps in the research literature are identified and we provide directions for future work.

The use of games as vehicles to study human-robot interaction (HRI) has been established as a suitable solution to create more realistic and naturalistic opportunities to investigate human behavior. In particular, multiplayer games that involve at least two human players and one or more robots have raised the attention of the research community. This article proposes a scoping review to qualitatively examine the literature on the use of multiplayer games in HRI scenarios employing embodied robots aiming to find experimental patterns and common game design elements. We find that researchers have been using multiplayer games in a wide variety of applications in HRI, including training, entertainment and education, allowing robots to take different roles. Moreover, robots have included different capabilities and sensing technologies, and elements such as external screens or motion controllers were used to foster gameplay. Based on our findings, we propose a design taxonomy called Robo Ludens, which identifies HRI elements and game design fundamentals and classifies important components used in multiplayer HRI scenarios. The Robo Ludens taxonomy covers considerations from a robot-oriented perspective as well as game design aspects to provide a comprehensive list of elements that can foster gameplay and bring enjoyable experiences in HRI scenarios.

An experiment is presented to investigate whether there is consensus in mapping emotions to messages/situations in urban search and rescue scenarios, where efficiency and effectiveness of interactions are key to success. We studied mappings between 10 specific messages, presented in two different communication styles, reflecting common situations that might happen during search and rescue missions, and the emotions exhibited by robots in those situations. The data was obtained through a Mechanical Turk study with 78 participants. Our findings support the feasibility of using emotions as an additional communication channel to improve multi-modal human-robot interaction for urban search and rescue robots, and suggests that these mappings are robust, i.e. are not affected by the robot's communication style.

Autonomous Vehicles (AV) will transform transportation, but also the interaction between vehicles and pedestrians. In the absence of a driver, it is not clear how an AV can communicate its intention to pedestrians. One option is to use visual signals. To advance their design, we conduct four human-participant experiments and evaluate six representative AV visual signals for visibility, intuitiveness, persuasiveness, and usability at pedestrian crossings. Based on the results, we distill twelve practical design recommendations for AV visual signals, with focus on signal pattern design and placement. Moreover, the paper advances the methodology for experimental evaluation of visual signals, including lab, closed-course, and public road tests using an autonomous vehicle. In addition, the paper also reports insights on pedestrian crosswalk behaviours and the impacts of pedestrian trust towards AVs on the behaviors. We hope that this work will constitute valuable input to the ongoing development of international standards for AV lamps, and thus help mature automated driving in general.

As we expect that the presence of autonomous robots in our everyday life will increase, we must consider that people will have not only to accept robots to be a fundamental part of their lives, but they will also have to trust them to reliably and securely engage them in collaborative tasks. Several studies showed that robots are more comfortable interacting with robots that respect social conventions. However, it is still not clear if a robot that expresses social conventions will gain more favourably people's trust. In this study, we aimed to assess whether the use of social behaviours and natural communications can affect humans' sense of trust and companionship towards the robots. We conducted a between-subjects study where participants' trust was tested in three scenarios with increasing trust criticality (low, medium, high) in which they interacted either with a social or a non-social robot. Our findings showed that participants trusted equally a social and non-social robot in the low and medium consequences scenario. On the contrary, we observed that participants' choices of trusting the robot in a higher sensitive task was affected more by a robot that expressed social cues with a consequent decrease of their trust in the robot.

Motor resonance, the activation of an observer’s motor control system by another actor’s movements, has been claimed to be an indicator for quality of interaction. Motor interference as one of the consequences of the presence of resonance can be detected by analyzing an actor’s spatial movements. It has therefore been used as an indicator for the presence of motor resonance. Unfortunately, the experimental paradigm in which motor interference has been shown to be detectable is ecologically implausible both in terms of the types of movements employed and the number of repetitions required. In the presented experiment, we tested whether some of these experimental constraints can be relaxed or modified toward a more naturalistic behavior without losing the ability to detect the interference effect. In the literature, spatial variance has been analytically quantified in many different ways. This study found these analytical variations to be nonequivalent by implementing them. Back-and-forth transitive movements were tested for motor interference; the effect was found to be more robust than with left-right movements, although the direction of interference was opposite to that reported in the literature. We conclude that motor interference, when measured by spatial variation, lacks promise for embedding in naturalistic interaction scenarios because the effect sizes were small.

In this work we investigate how humans' awareness of a social robot's capabilities affect their trust in the robot. We present a user study that relates knowledge on different quality levels to participants' ratings of trust. Primary school pupils were asked to rate their trust in the robot after three types of interactions: a video demonstration, a live interaction, and a programming task. The study revealed that the pupils' trust is not significantly affected across different domains after each session. It did not appear to be significant differences in trust tendencies for the different experiences either; however, our results suggest that human users trust a robot more the more awareness about the robot they have.

This work investigates how human awareness about a social robot's capabilities is related to trusting this robot to handle different tasks. We present a user study that relates knowledge on different quality levels to participant's ratings of trust. Secondary school pupils were asked to rate their trust in the robot after three types of exposures: a video demonstration, a live interaction, and a programming task. The study revealed that the pupils' trust is positively affected across different domains after each session, indicating that human users trust a robot more the more awareness about the robot they have.

My talk will highlight some of the developments over the past 15 years in human-robot interaction and social robotics. I have been fascinated by this area since it requires us to tackle hard problems in many dimensions, including robot design and functionalities. robot cognition and intelligence, and last but not least the human dimension of how people perceive robots and how we relate to robots. I will make a few points on what robots are, and what they are not, and emphasize useful roles they can play in human society. Particularly promising are areas where robots are not meant to replace humans, but carry out tasks that they are specifically good at. I will illustrate those points with examples of research projects I have been involved in, and highlight lessons that we have learned over the years in our quest to develop robot companions that provide useful assistance and are socially acceptable. These examples include work on robotic mediators for children with autism and special needs, including research on robot-assisted therapy for children with autism and robot-mediated interviews, as well as work with different home companion robots which are meant to support older people's independent living at home. It is my firm belief that robots can play a valuable role in human society, e.g. as helpful tools, mediators, assistants and companions, if we exploit the strengths of both robots and humans, considering the specific requirements in real-world application areas, while preserving human dignity, privacy and independence.

As home robot companions become more common, it is important to understand what types of tasks are considered critical to perform correctly. This paper provides working definitions of task criticality, physical and cognitive tasks with respect to robot task performance. Our research also suggests that although people's perceptions of task criticality is independent of robot appearances, their expectation that a robot performs tasks correctly is affected by it's appearance.

Robot-assisted therapy has been successfully used to help children with Autism Spectrum Condition (ASC) develop their social skills, but very often with the robot being fully controlled remotely by an adult operator. Although this method is reliable and allows the operator to conduct a therapy session in a customised child-centred manner, it increases the cognitive workload on the human operator since it requires them to divide their attention between the robot and the child to ensure that the robot is responding appropriately to the child's behaviour. In addition, a remote-controlled robot is not aware of the information regarding the interaction with children (e.g., body gesture and head pose, proximity etc) and consequently it does not have the ability to shape live HRIs. Further to this, a remote-controlled robot typically does not have the capacity to record this information and additional effort is required to analyse the interaction data. For these reasons, using a remote-controlled robot in robot-assisted therapy may be unsustainable for long-term interactions. To lighten the cognitive burden on the human operator and to provide a consistent therapeutic experience, it is essential to create some degrees of autonomy and enable the robot to perform some autonomous behaviours during interactions with children. Our previous research with the Kaspar robot either implemented a fully autonomous scenario involving pairs of children, which then lacked the often important input of the supervising adult, or, in most of our research, has used a remote control in the hand of the adult or the children to operate the robot. Alternatively, this paper provides an overview of the design and implementation of a robotic system called Sense- Think-Act which converts the remote-controlled scenarios of our humanoid robot into a semi-autonomous social agent with the capacity to play games autonomously (under human supervision) with children in the real-world school settings. The developed system has been implemented on the humanoid robot Kaspar and evaluated in a trial with four children with ASC at a local specialist secondary school in the UK where the data of 11 Child-Robot Interactions (CRIs) was collected. The results from this trial demonstrated that the system was successful in providing the robot with appropriate control signals to operate in a semi-autonomous manner without any latency, which supports autonomous CRIs, suggesting that the proposed architecture appears to have promising potential in supporting CRIs for real-world applications.

It is a great honor to write the editorial for the inaugural issue of ACM Transactions of Human-Robot Interaction.This is an exciting time for the HRI community.The journal is a descendant of the Journal of Human-Robot Interaction (JHRI), which has been successful for many years.Under the umbrella of ACM Transactions, I am sure it will go from strength to strength.

Children with autism often find it difficult to understand that other people might have perspectives, viewpoints, beliefs and knowledge that are different from their own. One fundamental aspect of this difficulty is Visual Perspective Taking (VPT). Visual perspective taking is the ability to see the world from another person's perspective, taking into account what they see and how they see it, drawing upon both spatial and social information. In this paper, we outline the child-robot interaction scenarios that we have developed as part of the European BabyRobot project to assist children with autism explore elements that are important in developing VPT skills. Further to this we describe the standard pre and post assessments that we will perform with the children in order to measure their progress. The games were implemented with the Kaspar robot. To our knowledge this is the first attempt to improve the VPT skills of children with autism through playing and interacting with a humanoid robot.

This paper proposes a set of five ethical principles, together with seven high-level messages, as a basis for responsible robotics. The Principles of Robotics were drafted in 2010 and published online in 2011. Since then the principles have influenced, and continue to influence, a number of initiatives in robot ethics but have not, to date, been formally published. This paper remedies that omission.

In the late 1990s, the question of control was raised in the Human Computer Interaction (HCI) community within the process of designing computer interfaces. Following in their footsteps, the question of how much people want to be in control of their robots and how it affects the way we should design robotic interfaces is explored. To investigate the subject, we conducted a study which involved two fully autonomously operating mobile robots, namely a multi-purpose companion robot and a single-purpose domestic robot. The purpose of the study was to evaluate participants' sense of control (perceived control -who they felt was in charge of the robots- and desired control -how they wanted the action to be executed-) for a common domestic task: cleaning. Unexpectedly, the results show the higher the participants' desired control was, the more autonomous they wanted the companion robot to be (meaning the robot executed the needed task without an explicit permission from the participants).

This paper examines participants' experiences of interacting with a robotic companion (agent) that has the ability to move its “mind” between different robotic embodiments to take advantage of the features and functionalities associated with the different embodiments in a process called agent migration. In particular, we focus on identifying factors that can help the companion retain its identity in different embodiments. This includes examining the clarity of the migration behaviour and how this behaviour may contribute to identity retention. Nine participants took part in a long-term study, and interacted with the robotic companion in the smart house twice-weekly over a period of 5 weeks. We used Narrative-based Integrated Episodic Scenario (NIES) framework for designing long-term interaction scenarios that provided habituation and intervention phases while conveying the impression of continuous long-term interaction. The results show that NEIS allows us to explore complex intervention scenarios and obtain a sense of continuity of context across the long-term study. The results also suggest that as participants become habituated with the companion, they found the realisation of migration signaling clearer, and felt more certain of the identity of the companion in later sessions, and that the most important factor for this was the agent's continuation of tasks across embodiments. This paper is both empirical as well as methodological in nature.

Interacting with humans is one of the main challenges for mobile robots in a human inhabited environment. To enable adaptive behavior, a robot needs to recognize touch gestures and/or the proximity to interacting individuals. Moreover, a robot interacting with two or more humans usually needs to distinguish between them. However, this remains both a configuration and cost intensive task. In this paper we utilize inexpensive Bluetooth Low Energy (BLE) devices and propose an easy and configurable technique to enhance the robot's capabilities to interact with surrounding people. In a noisy laboratory setting, a mobile spherical robot is utilized in three proof-of-concept experiments of the proposed system architecture. Firstly, we enhance the robot with proximity information about the individuals in the surrounding environment. Secondly, we exploit BLE to utilize it as a touch sensor. And lastly, we use BLE to distinguish between interacting individuals. Results show that observing the raw received signal strength (RSS) between BLE devices already enhances the robot's interaction capabilities and that the provided infrastructure can be facilitated to enable adaptive behavior in the future. We show one and the same sensor system can be used to detect different types of information relevant in human-robot interaction (HRI) experiments.

Care issues and costs associated with an increasing elderly population are becoming a major concern for many countries. The use of assistive robots in “smart-home” environments has been suggested as a possible partial solution to these concerns. A challenge is the personalization of the robot to meet the changing needs of the elderly person over time. One approach is to allow the elderly person, or their carers or relatives, to make the robot learn activities in the smart home and teach it to carry out behaviors in response to these activities. The overriding premise being that such teaching is both intuitive and “nontechnical.” To evaluate these issues, a commercially available autonomous robot has been deployed in a fully sensorized but otherwise ordinary suburban house. We describe the design approach to the teaching, learning, robot, and smart home systems as an integrated unit and present results from an evaluation of the teaching component with 20 participants and a preliminary evaluation of the learning component with three participants in a human-robot interaction experiment. Participants reported findings using a system usability scale and ad-hoc Likert questionnaires. Results indicated that participants thought that this approach to robot personalization was easy to use, useful, and that they would be capable of using it in real-life situations both for themselves and for others.

It is essential for robots working in close proximity to people to be both safe and trustworthy. We present a case study on formal verification for a high-level planner/scheduler for the Care-O-bot, an autonomous personal robotic assistant. We describe how a model of the Care-O-bot and its environment was developed using Brahms, a multiagent workflow language. Formal verification was then carried out by automatically translating this model to the input language of an existing model checker. Four sample properties based on system requirements were verified. We then refined the environment model three times to increase its accuracy and the persuasiveness of the formal verification results. The first refinement uses a user activity log based on real-life experiments, but is deterministic. The second refinement uses the activities from the user activity log nondeterministically. The third refinement uses “conjoined activities” based on an observation that many user activities can overlap. The four samples properties were verified for each refinement of the environment model. Finally, we discuss the approach of environment model refinement with respect to this case study.

How do mistakes made by a robot affect its trustworthiness and acceptance in human-robot collaboration? We investigate how the perception of erroneous robot behavior may influence human interaction choices and the willingness to cooperate with the robot by following a number of its unusual requests. For this purpose, we conducted an experiment in which participants interacted with a home companion robot in one of two experimental conditions: (1) the correct mode or (2) the faulty mode. Our findings reveal that, while significantly affecting subjective perceptions of the robot and assessments of its reliability and trustworthiness, the robot's performance does not seem to substantially influence participants' decisions to (not) comply with its requests. However, our results further suggest that the nature of the task requested by the robot, e.g. whether its effects are revocable as opposed to irrevocable, has a significant impact on participants' willingness to follow its instructions.

Over the past 10 years we have seen worldwide an immense growth of research and development into companion robots. Those are robots that fulfil particular tasks, but do so in a socially acceptable manner. The companionship aspect reflects the repeated and long-term nature of such interactions, and the potential of people to form relationships with such robots, e.g. as friendly assistants. A number of companion and assistant robots have been entering the market, two of the latest examples are Aldebaran's Pepper robot, or Jibo (Cynthia Breazeal). Companion robots are more and more targeting particular application areas, e.g. as home assistants or therapeutic tools. Research into companion robots needs to address many fundamental research problems concerning perception, cognition, action and learning, but regardless how sophisticated our robotic systems may be, the potential users need to be taken into account from the early stages of development. The talk will emphasize the need for a highly user-centred approach towards design, development and evaluation of companion robots. An important challenge is to evaluate robots in realistic and long-term scenarios, in order to capture as closely as possible those key aspects that will play a role when using such robots in the real world. In order to illustrate these points, my talk will give examples of interaction studies that my research team has been involved in. This includes studies into how people perceive robots' non-verbal cues, creating and evaluating realistic scenarios for home companion robots using narrative framing, and verbal and tactile interaction of children with the therapeutic and social robot Kaspar. The talk will highlight the issues we encountered when we proceeded from laboratory-based experiments and prototypes to real-world applications.

The question of what to imitate is pivotal for imitation learning in robotics. When the robot's tutor is a naive user, it is very difficult for the embodied agent to account for the unpredictability of the tutor's behaviour. Preliminary results from a previous study suggested that the phenomenon of temporal emphasis, i.e., that tutors tend to keep the goal state of the demonstrated task stationary longer than the sub-states, can be used to recognise that task. In the present paper, the previous study is expanded and the existence of the phenomenon is investigated further. An improved experimental setup, using the iCub humanoid robot and naive users, was implemented. Analysis of the data showed that the phenomenon was detected in the majority of the cases, with a strongly significant result. In the few cases that the end state was not the one with the longest time span, it was a borderline second. Then, a very simple algorithm using a single binary criterion was used to show that the phenomenon exists and can be detected easily. That leads to the argument that humans may also be able to detect this phenomenon and use it for recognizing, as learners or emphasizing and teaching as tutors, the end goal, at least for tasks with clear and separate sub-goal sequences. A robot that implements this behavior could be able to perform better both as a tutor and as a learner when interacting with naive users.

With changes in life expectancy across the world, technologies enhancing well-being of individuals, specifically for older people, are subject to a new stream of research and development. In this paper we present the ACCOMPANY project, a pan-European project which focuses on home companion technologies. The projects aims to progress beyond the state of the art in multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, monitoring persons and chores at home, and technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 and in the context of a smart-home environment utilising a multitude of sensor arrays. The resulting prototype from integrating these developments undergoes multiple formative cycles and a summative evaluation cycle towards identifying acceptable behaviours and roles for the robot for example role as a butler or a trainer. Furthermore, the evaluation activities will use an evaluation grid in order to assess achievement of the identified user requirements, formulated in form of distinct scenarios. Finally, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developments.

This paper presents a study of the readability of dog-inspired visual communication signals in a human-robot interaction scenario. This study was motivated by specially trained hearing dogs which provide assistance to their deaf owners by using visual communication signals to lead them to the sound source. For our human-robot interaction scenario, a robot was used in place of a hearing dog to lead participants to two different sound sources. The robot was preprogrammed with dog-inspired behaviors, controlled by a wizard who directly implemented the dog behavioral strategy on the robot during the trial. By using dog-inspired visual communication signals as a means of communication, the robot was able to lead participants to the sound sources (the microwave door, the front door). Findings indicate that untrained participants could correctly interpret the robot's intentions. Head movements and gaze directions were important for communicating the robot's intention using visual communication signals.

We overview how sensorimotor experience can be operationalized for interaction scenarios in which humanoid robots acquire skills and linguistic behaviours via enacting a “form-of-life” in interaction games (following Wittgenstein) with humans. The enactive paradigm is introduced which provides a powerful framework for the construction of complex adaptive systems, based on interaction, habit, and experience. Enactive cognitive architectures (following insights of Varela, Thompson and Rosch) that we have developed support social learning and robot ontogeny by harnessing information-theoretic methods and raw uninterpreted sensorimotor experience to scaffold the acquisition of behaviours. The success criterion here is validation by the robot engaging in ongoing human-robot interaction with naive participants who, over the course of iterated interactions, shape the robot's behavioural and linguistic development. Engagement in such interaction exhibiting aspects of purposeful, habitual recurring structure evidences the developed capability of the humanoid to enact language and interaction games as a successful participant.

Music in general and drumming in specific has been used several times in human-robot interaction studies. We present a drumming robotic system based on the iCub humanoid robotic platform using all four limbs to play a full-sized drum set and the exploratory experimental setups used to assess it. The social aspect of the system uses the concept of music as a communicative gesture to transfer information and facilitate the interaction. The first implementation is focused on assessing the imitative capabilities of a robot on a full drum set at different levels of speed and complexity. By treating the rhythmic content as communication, the second implementation allows for improvisation of both the robot and the human in a kind of musical dialogue. While keeping the structure of turn-taking fixed and predefined, the focus of the setup is on the actual content of each turn and how it affects the other participant's communicative exchange. The resulting platform was able to imitate the drumming of the humans within the timing constraints of the rhythm and tempo. The second setup was used to evaluate the improvisation capabilities of the platform and also led to observations about the potential behaviour of the human participant in such an interaction. The robotic drumming system produced can be used for more complex rhythmical interactions between humans and robots. These will facilitate the study of not only the structure of the interaction but also its content.

The current paper focuses on a novel integration of the Personas technique into HRI studies, and the definition of a Persona-Based Computational Behaviour Model for achieving socially intelligent robot companions in living environments. Our core interest is the creation of companions adapted to users' needs to support their activities of daily living. The aim is to create a mechanism that allows us to develop initial robot behaviour, i.e. behaviour when first encountering the user, which is already adapted to each user without the necessity of collecting in advance a large dataset to train the system. A persona represents the specific needs of many individuals for a particular scenario. This technique helps us develop initial robot behaviour adapted to user needs, and so reduces the amount of trials that participants have to perform during early stages of the system development. The paper describes how this behaviour model has been created and integrated into a functional architecture, and presents the motivation, background and conceptual framework for this new research direction. Future empirical studies will validate this approach and expand the initial definition of our model.

A theatre production of a play illustrating the functionality, social and ethical aspects of robots helping with aspects of elderly care was presented at a residential care home. The audience consisted of mainly elderly residents and carers. The residents suffered from various physical and mental disabilities which impaired their ability to provide responses through standard questionnaires. Therefore, additional structured interviews were used to gain insight into their views on the theatre scenario. Both carers and residents were generally positive towards the idea of using robots to help with care. Residents in particular stressed the desire for care robots to also provide social interaction and entertainment.

Mutual gaze is an important aspect of face-to-face communication that arises from the interaction of the gaze behavior of two individuals. In this dual eye-tracking study, gaze data was collected from human conversational pairs with the goal of gaining insight into what characteristics of the conversation partners influence this behavior. We investigate the link between personality, familiarity and mutual gaze. The results found indicate that mutual gaze behavior depends on the characteristics of both partners rather than on either individual considered in isolation. We discuss the implications of these findings for the design of socially appropriate gaze controllers for robots that interact with people.

In this paper, we present an approach to monitor human activities such as entry, exit and break times of people in a workplace environment. The companion robot then learns the users' presence patterns over a period of time through memory generalisation and plans a suitable time for re charging itself causing less hindrance to human-robot interaction.

On considering symbiosis of humans and robots, its benefits and risks should be taken into account for persons in weaker positions of the society, in particular, children. On the other hand, several robotics applications have been developed, including education and welfare for children. In this stage, it is important that more researchers from interdisciplinary research fields, including robotics, computer science, psychology, sociology, and pedagogy, share an opportunity to discuss about the potential of "robots with children". This half-day workshop aims at providing with the forum where researchers from these interdisciplinary fields discuss about how symbiosis of robots and children should and can be realized, from the perspectives of engineering, psychology, education, and welfare.

The main target of this work is to improve human-robot interaction capabilities, by adding a new modality of sense, touch, to KASPAR, a humanoid robot. Large scale distributed skin-like sensors are designed and integrated on the robot, covering KASPAR at various locations. One of the challenges is to classify different types of touch. Unlike digital images represented by grids of pixels, the geometrical structure of the sensor array limits the capability of straightforward application of well-established approaches for image patterns. This paper introduces a novel histogram-based classification algorithm, transforming tactile data into histograms of local features termed as codebook. Tactile pattern can be invariant at periodical locations, allowing tactile pattern classification using a smaller number of training data, instead of using training data from everywhere on the large scale skin sensors. To generate the codebook, this method uses a two-layer approach, namely local neighbourhood structures and encodings of pressure distribution of the local neighbourhood. Classification is performed based on the constructed features using Support Vector Machine (SVM) with the intersection kernel. Real experimental data are used for experiment to classify different patterns and have shown promising accuracy. To evaluate the performance, it is also compared with the SVM using the Radial Basis Function (RBF) kernel and results are discussed from both aspects of accuracy and the location invariance property.

Summary form only given. Scaled technologies continue to exhibit variability, driven by both random process effects and systematic structural effects. Process and design rule actions can be taken to reduce, or even eliminate, sources of systematic variability. Random variability is more difficult to combat, but architectural decisions can be made to limit the device sensitivity to specific random effects. A review of several current sources of technology variability is presented, and the impacts to the overall technology offering are assessed.

Our research is concerned with developing scenarios for robot home companions as part of the EU project LIREC. In this work, we employed a particular methodology to gain user feedback in early stages of robot prototyping: the Theatre HRI (THRI) methodology which we have recently introduced in a pilot study. Extending this work, this study used a theatre presentation to convey the user experience of domestic service robots to a group of participants and to gain their feedback in order to further refine our scenarios. The play was designed both from the perspective of projected technological development of the LIREC project, as well as for facilitating engagement with an audience of secondary school students. At the end of the play the audience was involved in a discussion regarding issues such as acceptability of the scenario and the intra-household disclosure of information by the robot. Findings suggest that this methodology was effective in eliciting discussion with the audience and that problems related to intra-household disclosure of information were best resolved by clear-cut solutions tied to ownership and clear principles.

A long-term Human-Robot Proxemic (HRP) study was performed using a newly developed Autonomous Proxemic System (APS) for a robot to measure and control the approach distances to the human participants. The main findings were that most HRP adaptation occurred in the first two interaction sessions, and for the remaining four weeks, approach distance preferences remained relatively steady, apart from some short periods of increased distances for some participants. There were indications that these were associated with episodes where the robot malfunctioned, so this raises the possibility of users trust in the robot affecting HRP distance. The study also found that approach distances for humans approaching the robot and the robot approaching the human were comparable, though there were indications that humans preferred to approach the robot more closely than they allowed the robot to approach them in a physically restricted area. Two participants left the study prematurely, stating they were bored with the repetitive experimental procedures. This highlights issues related to the often incompatible demands of keeping experimental controlled conditions vs. having realistic, engaging and varied HRI trial scenarios.

In the field of Human-Robot Interaction (HRI), a novel experimental methodology is presented for carrying out studies which uses a theatrical presentation with an actor interacting and cooperating with robots in realistic scenarios before an audience. This methodology has been inspired by previous research in Human-Computer Interaction. The actor also stays in role for a post-theatre session, answering questions and encouraging the audience to discuss their respective opinions and viewpoints relating to the HRI scenario enactment. The development and running of a first exploratory pilot experiment using the new Theatre HRI (THRI) methodology is presented and critically reviewed. Based on this review and the associated findings from the audience discussion session, it is concluded that the Theatre-based HRI (THRI) methodology is viable for performing HRI user studies.

To evaluate the performance of a social robot, both the aspects of safety and technical efficiency as well as the effectiveness of the interaction with the robot from the users' points of view need to be considered. The work described in this paper derived from the IROMEC1project (Interactive Robotic Social Mediators as Companions) that investigates the design and role of an interactive, autonomous robotic toy in therapy and education contexts for children with special needs. The paper proposes a framework for the evaluation of robotic toys used as mediators for children with special needs and present its implementation with the specially designed IROMEC robot. Special attention is given to the interactions' effectiveness, considering the therapeutic and educational role that the robot can play for children with special needs in many different developmental areas.

The work presented in this paper is part of our investigation in the ROBOSKIN project. The project aims to develop and demonstrate a range of new robot capabilities based on the tactile feedback provided by a robotic skin. One of the project's objectives is to improve human-robot interaction capabilities in the application domain of robot-assisted play. This paper presents design challenges in augmenting a humanoid robot with tactile sensors specifically for interaction with children with autism. It reports on a preliminary study that includes requirements analysis based on a case study evaluation of interactions of children with autism with the child-sized, minimally expressive robot KASPAR. This is followed by the implementation of initial sensory capabilities on the robot that were then used in experimental investigations of tactile interaction with children with autism.

This paper presents the findings of a qualitative study exploring how mental models of a mechanoid robot using dog-inspired affective cues behaviour emerges and impacts the evaluation of the robot after the viewing of a video of an assistive robotics scenario interaction with the robot. It discusses this using contrasting case studies based on the analysis of explicitation interviews with three participants. The analysis suggests that while for some users zoomorphic cues may aid in initial interactions, they need to be framed in an authentic interaction, highlighting the actual capabilities of the robot as a technological artifact, and how these impact the everyday life and interests of the potential user.

This paper presents results from a video human-robot interaction (VHRI) study in which participants viewed a video in which an appearance-constrained Pioneer robot used dog-inspired affective cues to communicate affinity and relationship with its owner and a guest using proxemics, body movement and orientation and camera orientation. The findings suggest that even with the limited modalities for non-verbal expression offered by a Pioneer robot, which does not have a dog-like appearance, these cues were effective for non-verbal affective communication.

This article investigates the role of interaction kinesics in human–robot interaction (HRI). We adopted a bottom-up, synthetic approach towards interactive competencies in robots using simple, minimal computational models underlying the robot's interaction dynamics. We present two empirical, exploratory studies investigating a drumming experience with a humanoid robot (KASPAR) and a human. In the first experiment, the turn-taking behaviour of the humanoid is deterministic and the non-verbal gestures of the robot accompany its drumming to assess the impact of non-verbal gestures on the interaction. The second experiment studies a computational framework that facilitates emergent turn-taking dynamics, whereby the particular dynamics of turn-taking emerge from the social interaction between the human and the humanoid. The results from the HRI experiments are presented and analysed qualitatively (in terms of the participants’ subjective experiences) and quantitatively (concerning the drumming performance of the human–robot pair). The results point out a trade-off between the subjective evaluation of the drumming experience from the perspective of the participants and the objective evaluation of the drumming performance. A certain number of gestures was preferred as a motivational factor in the interaction. The participants preferred the models underlying the robot's turn-taking which enable the robot and human to interact more and provide turn-taking closer to ‘natural’ human–human conversations, despite differences in objective measures of drumming behaviour. The results are consistent with the temporal behaviour matching hypothesis previously proposed in the literature which concerns the effect that the participants adapt their own interaction dynamics to the robot's.

This paper presents five exploratory trials investigating scenarios likely to occur when a personal robot shares a home with a person. The scenarios are: a human and robot working on a collaborative task, a human and robot sharing a physical space in a domestic setting, a robot recording and revealing personal information, a robot interrupting a human in order to serve them, and finally, a robot seeking assistance from a human through various combinations of physical and verbal cues. Findings indicate that participants attribute more blame and less credit to a robot than compared to themselves when working together on a collaborative task. Safety is a main concern when determining participants' comfort when sharing living space with their robot. Findings suggest that the robot should keep its interruption of the user's activities to a minimum. Participants were happy for the robot to store information which is essential for the robot to improve its functionality. However, their main concerns were related to the storing of sensitive information and security measures to safeguard such information.

The general context of the work presented in this paper is assistive robotics with our long-term aim to support children with autism. This paper is part of the Aurora project that studies ways in which robotic systems can encourage basic communication and social interaction skills in children with autism. This paper investigates how a small minimally expressive humanoid robot KASPAR can assume the role of a social mediator - encouraging children with low functioning autism to interact with the robot, to break their isolation and importantly, to facilitate interaction with other people. The article provides a case study evaluation of segments of trials where three children with autism, who usually do not interact with other people in their day to day activity, interacted with the robot and with co-present adults. A preliminary observational analysis was undertaken which applied, in abbreviated form, certain principles from conversation analysis - notably attention to the context in which the target behaviour occurred. The analysis was conducted by a social psychologist with expertise in using conversation analysis to understand interactions involving persons with an ASD. The analysis emphasises aspects of embodiment and interaction kinetics and revealed unexpected competencies on the part of the children. It showed how the robot served as a salient object mediating and encouraging interaction between the children and co-present adults.

This paper presents an application of the Cascaded Information Bottleneck Method for real-time recognition of Human-Robot Interaction styles in robot-assisted play. This method, that we have developed, is implemented here for an adaptive robot that can recognize and adapt to children's play styles in real time. The robot rewards well-balanced interaction styles and encourages children to engage in the interaction. The potential impact of such an adaptive robot in robot-assisted play for children with autism is evaluated through a study conducted with seven children with autism in a school. A statistical analysis of the results shows the positive impact of such an adaptive robot on the children's play styles and on their engagement in the interaction with the robot.

It is thought that meaning may be grounded in early childhood language learning via the physical and social interaction of the infant with those around him or her, and that the capacity to use words, phrases and their meaning are acquired through shared referential dasiainferencepsila in pragmatic interactions. In order to create appropriate conditions for language learning by a humanoid robot, it would therefore be necessary to expose the robot to similar physical and social contexts. However in the early stages of language learning it is estimated that a 2-year-old child can be exposed to as many as 7,000 utterances per day in varied contextual situations. In this paper we report on the issues behind and the design of our currently ongoing and forthcoming experiments aimed to allow a robot to carry out language learning in a manner analogous to that in early child development and which effectively dasiashort cutspsila holophrase learning. Two approaches are used: (1) simulated babbling through mechanisms which will yield basic word or holophrase structures and (2) a scenario for interaction between a human and the humanoid robot where shared dasiaintentionalpsila referencing and the associations between physical, visual and speech modalities can be experienced by the robot. The output of these experiments, combined to yield word or holophrase structures grounded in the robot's own actions and modalities, would provide scaffolding for further proto-grammatical usage-based learning. This requires interaction with the physical and social environment involving human feedback to bootstrap developing linguistic competencies. These structures would then form the basis for further studies on language acquisition, including the emergence of negation and more complex grammar.

This article is a methodological paper that describes the therapeutic and educational objectives that were identified during the design process of a robot aimed at robot assisted play. The work described in this paper is part of the IROMEC project (Interactive Robotic Social Mediators as Companions) that recognizes the important role of play in child development and targets children who are prevented from or inhibited in playing. The project investigates the role of an interactive, autonomous robotic toy in therapy and education for children with special needs. This paper specifically addresses the therapeutic and educational objectives related to children with autism. In recent years, robots have already been used to teach basic social interaction skills to children with autism. The added value of the IROMEC robot is that play scenarios have been developed taking children's specific strengths and needs into consideration and covering a wide range of objectives in children's development areas (sensory, communicational and interaction, motor, cognitive and social and emotional). The paper describes children's developmental areas and illustrates how different experiences and interactions with the IROMEC robot are designed to target objectives in these areas.

This work proposes an initial memory model for a long-term artificial companion, which migrates among virtual and robot platforms based on the context of interactions with the human user. This memory model enables the companion to remember events that are relevant or significant to itself or to the user. For other events which are either ethically sensitive or with a lower long-term value, the memory model supports forgetting through the processes of generalisation and memory restructuring. The proposed memory model draws inspiration from the human short-term and long-term memories. The short-term memory will support companions in focusing on the stimuli that are relevant to their current active goals within the environment. The long-term memory will contain episodic events that are chronologically sequenced and derived from the companion's interaction history both with the environment and the user. There are two key questions that we try to address in this work: 1) What information should the companion remember in order to generate appropriate behaviours and thus smooth the interaction with the user? And, 2) What are the relevant aspects to take into consideration during the design of memory for a companion that can have different types of virtual and physical bodies? Finally, we show an implementation plan of the memory model, focusing on issues of information grounding, activation and sensing based on specific hardware platforms.

Investigating how people respond to and relate to robots is a multifaceted scientific challenge. This paper reports on an experimental investigation concerning movement interference effects between a human and a robot. We compare results with that obtained by Oztop et al. [1], however, in our study we used a small child-sized robot (KASPAR) with an overall human-like appearance. The experiment was conducted with both child and adult participants who interacted with a small humanoid robot using arm waving behaviours. The experimental setup was designed to be less constrained than in [1] with an emphasis on playful interaction. The experimental results did not show evidence for interference effects. This might be due to a more game-like and less constrained experimental environment or to the specific features of the robot or both. In addition to measurements of the variance of the movements, we investigated a measure for behavioural synchrony between human and robot movements based on the concept of information distance. The results of information distance analysis indicated that most of the human participants were affected by the robot's behavioural rhythms. While our experiments did not show a movement interference effect, we found behavioural adaptation of participants' movement timing to the robot's movements. Thus, the measure of behavioural synchrony that we introduced appears useful for complementing other measures (such as variance) previously used in the literature.

In this paper, we explore some conceptual issues, relevant for the design of robotic systems aimed at interacting with humans in domestic environments. More specifically, we study the role of the robot's feedback (positive or negative acknowledgment of understanding) on a human teacher's demonstration of a routine home task (laying a table). Both the human and the system's perspectives are considered in the analysis and discussion of results from a human-robot user study, highlighting some important conceptual and practical issues. These include the lack of explicitness and consistency on people's demonstration strategies. Furthermore, we discuss the need to investigate design strategies to elicit people's knowledge about the task and also successfully advertize the robot's abilities in order to promote people's ability to provide appropriate demonstrations.

This paper presents results of a novel study on interaction kinesics where 18 children interacted with a humanoid child-sized robot called KASPAR. Based on findings in psychology and social sciences we propose the temporal behaviour matching hypothesis which predicts that children will adapt to and match the robot's temporal behaviour. Each child took part in six experimental trials involving two games in which the dynamics of interactions played a key part: a body expression imitation game, where the robot imitated expressions demonstrated by the children, and a drumming game where the robot mirrored the children's drumming. In both games KASPAR responded either with or without a delay. Additionally, in the drumming game, KASPAR responded with or without exhibiting facial/gestural expressions. Individual case studies as well as statistical analysis of the complete sample are presented. Results show that a delay of the robot's drumming response lead to larger pauses (with and without robot nonverbal gestural expressions) and longer drumming durations (with nonverbal gestural expressions only). In the imitation game, the robot's delay lead to longer imitation eliciting behaviour with longer pauses for the children, but systematic individual differences are observed in regards to the effects on the children's pauses. Results are generally consistent with the temporal behaviour matching hypothesis, i.e. children adapted the timing of their behaviour, e.g. by mirroring to the robot's temporal behaviour.

We present results from an empirical study investigating emergent turn-taking in a drumming experience involving Kaspar, a humanoid child-sized robot, and adult participants. In this work, our aim is to have turn-taking and role switching which is not deterministic but emerging from the social interaction between the human and the humanoid. Therefore the robot is not just dasiafollowingpsila and imitating the human, but could be the leader in the game and being imitated by the human. Data from the first implementation of a human-robot interaction experiment are presented and analysed qualitatively (in terms of participantspsila subjective experiences) and quantitatively (concerning the drumming performance of the human-robot pair). Results are analysed statistically and show significant differences for the three games (with different probabilistic models) where the models enabling more interaction and more ldquonaturalrdquo turn-taking were preferred by the human participants.

Human-robot interaction (HRI) research is here presented into social robots that have to be able to interact with inexperienced users. In the design of these robots many research findings of human-human interaction and human-computer interaction are adopted but the direct applicability of these theories is limited because a robot is different from both humans and computers. Therefore, new methods have to be developed in HRI in order to build robots that are suitable for inexperienced users. In this paper we present a video study we conducted employing our robot BIRON (Bielefeld robot companion) which is designed for use in domestic environments. Subjects watched the system during the interaction with a human and rated two different robot behaviours (extrovert and introvert). The behaviours differed regarding verbal output and person following of the robot. Aiming to improve human-robot interaction, participantspsila ratings of the behaviours were evaluated and compared.

This paper describes the user-centred development of play scenarios for robot assisted play, as part of the IROMEC project that develops a novel robotic toy for children with special needs. The project investigates how robotic toys can become social mediators, encouraging children with special needs to discover a range of play styles, from solitary to collaborative play (with peers, carers/teachers, parents, etc). This paper presents the developmental process of constructing relevant play scenarios for children with different special needs. This process is driven by a) a comprehensive literature review that is related to play activities of children from different target user groups with existing technology, consultation with panel of experts (therapist, teachers, parents) and b) by the result of experimental investigations of user requirements in trials with children with special needs. An important step (reported here) towards the development of the final play scenarios is the development of Outline Play Scenarios - a set of abstract scenarios that reflect the users' requirements and which are not related to any specific technological solution. The general methodological approach, as well as the outline play scenarios, may benefit the development of scenarios for other human-robot interaction research in robot assisted play and related areas. In future, these outline scenarios will be further developed to reflect and utilise the specific functionalities to be implemented in the new IROMEC robot and its different modules.

Findings are presented from a Human Robot Interaction (HRI) Demonstration Trial where attendees approached a stationary mechanical looking robot to a comfortable distance. Instructions were given to participants by the robot using either a high quality male, a high quality female, a neutral synthesized voice, or by the experimenter (no robot voice). Approaches to the robot with synthesized voice were found to induce significantly further approach distances. Those who had experienced a previous encounter with the robot tended to approach closer to the robot. Possible reasons for this are discussed.

In this paper, we discuss the concept of autobiographic agent and how memory may extend an agent's temporal horizon and increase its adaptability. These concepts are applied to an implementation of a scenario where agents are interacting in a complex virtual artificial life environment. We present computational memory architectures for autobiographic virtual agents that enable agents to retrieve meaningful information from their dynamic memories which increases their adaptation and survival in the environment. The design of the memory architectures, the agents, and the virtual environment are described in detail. Next, a series of experimental studies and their results are presented which show the adaptive advantage of autobiographic memory, i.e. from remembering significant experiences. Also, in a multi-agent scenario where agents can communicate via stories based on their autobiographic memory, it is found that new adaptive behaviours can emerge from an individual's reinterpretation of experiences received from other agents whereby higher communication frequency yields better group performance. An interface is described that visualises the memory contents of an agent. From an observer perspective, the agents’ behaviours can be understood as individually structured, and temporally grounded, and, with the communication of experience, can be seen to rely on emergent mixed narrative reconstructions combining the experiences of several agents. This research leads to insights into how bottom-up story-telling and autobiographic reconstruction in autonomous, adaptive agents allow temporally grounded behaviour to emerge. The article concludes with a discussion of possible implications of this research direction for future autobiographic, narrative agents.

For robots to be more capable interaction partners they will necessarily need to adapt to the needs and requirements of their human companions. One way that the human could aid this adaptation may be by teaching the robot new ways of doing things by physically demonstrating different behaviours and tasks such that the robot learns new skills by imitating the learnt behaviours in appropriate contexts. In human–human teaching, the concept of scaffolding describes the process whereby the teacher guides the pupil to new competence levels by exploiting and extending existing competencies. In addition, the idea of event structuring can be used to describe how the teacher highlights important moments in an overall interaction episode. Scaffolding and event structuring robot skills in this way may be an attractive route in achieving robot adaptation; however, there are many ways in which a particular behaviour might be scaffolded or structured and the interaction process itself may have an effect on the robot's resulting performance. Our overall research goal is to understand how to design an appropriate human–robot interaction paradigm where the robot will be able to intervene and elicit knowledge from the human teacher in order to better understand the taught behaviour. In this article we examine some of these issues in two exploratory human–robot teaching scenarios. The first considers task structuring from the robot's viewpoint by varying the way in which a robot is taught. The experimental results illustrate that the way in which teaching is carried out, and primarily how the teaching steps are decomposed, has a critical effect on the efficiency of human teaching and the effectiveness of robot learning. The second experiment studies the problem from the human's viewpoint in an attempt to study the human teacher's spontaneous levels of event segmentation when analysing their own demonstrations of a routine home task to a robot. The results suggest the existence of some individual differences regarding the level of granularity spontaneously considered for the task segmentation and for those moments in the interaction which are viewed as most important.

This paper presents a proof-of-concept of a robot that is adapting its behaviour on-line, during interactions with a human according to detected play styles. The study is part of the AuRoRa project which investigates how robots may be used to help children with autism overcome some of their impairments in social interactions. The paper motivates why adaptation is a very desirable feature of autonomous robots in human-robot interaction scenarios in general, and in autism therapy in particular. Two different play styles namely 'strong' and 'gentle', which refer to the user, are investigated experimentally. The model relies on Self-Organizing Maps, used as a classifier, and on Fast Fourier Transform to preprocess the sensor data. First experiments were carried out which discuss the performance of the model. Related work on adaptation in socially assistive and therapeutic work are surveyed. In future work, with typically developing and autistic children, the concrete choice of the robot's behaviours will be tailored towards the children's interests and abilities.

Original paper can be found at: http://portal.acm.org/ Copyright ACM. DOI: 10.1145/1228716.1228759 [Full text of this article is not available in the UHRA]

This paper presents the results of video based human robot interaction (HRI) trials which investigated people's perceptions of different robot appearances and associated attention seeking features and behaviors displayed by the robot. The methodological approach highlights the "holistic" and embodied nature of robot appearance and behavior. Results show that people tend to rate a particular behavior less favorably when the behavior is not consistent with the robot's appearance. It is shown how participants' ratings of robot dynamic appearance are influenced by the robot's behavior. Relating participants' dynamic appearance ratings of individual robots to independently rated static appearance provides support for the left hand side of Mori's proposed "uncanny valley" diagram. We exemplify how to rate individual elements of a particular robot's behavior and then assess the contribution of those elements to the overall perception of the robot by people. Suggestions for future work are outlined.

This paper presents and discusses a longitudinal study which investigated habituation effects between humans and robots over a period of five weeks. Participants' preferences for the robot's approach distance with respect to its approach direction and appearance were investigated in a variety of domestic scenarios. These human-robot interaction (HRI) scenarios were also designed to explore the notions of autonomy and control. The results of this study show that participants' preferences change over time as the participants habituate to the robot. This trend was significant in terms of the robot's appearance and approach direction. Also, it seems to indicate that participants who are accustomed to the robot prefer to be more `in control' of the situation - in that they appreciated reduced robot autonomy in case of unexpected events.

The work presented in this paper was carried out within the IROMEC project that develops a robotic toy for children. Play has an important role in child development with many potential contributions to therapy, education and enjoyment. The project investigates how robotic toys can become social mediators, encouraging children with disabilities to discover a range of play styles, from solitary to social and cooperative play (with peers, carers/teachers, parents etc). This paper presents design issues for such robotic toys related specifically to children with autism as the end user target group. In order to understand the play needs of this user group, and to investigate how robotic toys could be used as a play tool to assist in the children's development, a panel of experts (therapists, teachers, parents) was formed and interviewed. Results of the expert panel interview s highlight key points characterizing the play of children with autism, and key points for consideration in the design of future robotic toys.

This study investigated the relationship between individual differences and proxemic behaviour in an HRI setting involving a robot approaching a person. In total 33 participants took part in three different scenarios; verbal interaction, physical interaction and no interaction. Participant control over the robot, and approach direction was also varied. Measurements of the preferred robot approach distance was obtained, and analysed along with the participants' demographic and personality data. The results indicate differences in approach direction preferences based on gender. Also, results show that the participants' personality traits of extraversion and conscientiousness are associated with changes in approach distance preferences according to robot autonomy. The results are discussed in light of relevant literature from the social sciences.

The author regrets the omission of the co-authors on this paper. The authorship is now reproduced correctly here.

This paper addresses the problem of body mapping in robotic imitation where the demonstrator and imitator may not share the same embodiment [degrees of freedom (DOFs), body morphology, constraints, affordances, and so on]. Body mappings are formalized using a unified (linear) approach via correspondence matrices, which allow one to capture partial, mirror symmetric, one-to-one, one-to-many, many-to-one, and many-to-many associations between various DOFs across dissimilar embodiments. We show how metrics for matching state and action aspects of behavior can be mathematically determined by such correspondence mappings, which may serve to guide a robotic imitator. The approach is illustrated and validated in a number of simulated 3-D robotic examples, using agents described by simple kinematic models and different types of correspondence mappings.

Imitation is a powerful learning tool when humans and robots interact in a social context. A series of experimental runs and a small pilot user study were conducted to evaluate the performance of a system designed for robot imitation. Performance assessments of similarity of imitative behaviours were carried out by machines and by humans: the system was evaluated quantitatively (from a machine-centric perspective) and qualitatively (from a human perspective) in order to study the reconciliation of these views. The experimental results presented here illustrate how the number of exceptions can be used as a performance measure by a robotic or software imitator of an object manipulation behaviour. (In this context, exceptions are events when the optimal displacement and/or rotation that minimize the dissimilarity metrics used to generate a corresponding imitative behaviour cannot be directly achieved in the particular context.) Results of the user study giving similarity judgments on imitative behaviours were used to examine how the quantitative measure of the number of exceptions (from a robot's perspective) corresponds to the qualitative evaluation of similarity (from a human's perspective) for the imitative behaviours generated by the jabberwocky system. Results suggest that there is a good alignment between this quantitive system centered assessment and the more qualitative human-centered assessment of imitative performance.

As robots enter everyday life and start to interact with ordinary people [5]the question of their appearance becomes increasingly important. A user's perception of a robot can be strongly influenced by its facial appearance [6]. The dimensions and issues of face design are illustrated in the design rationale, details of construction and intended uses of a new minimal expressive robot called KASPAR.

This paper presents the combined results of two studies that investigated how a robot should best approach and place itself relative to a seated human subject. Two live Human Robot Interaction (HRI) trials were performed involving a robot fetching an object that the human had requested, using different approach directions. Results of the trials indicated that most subjects disliked a frontal approach, except for a small minority of females, and most subjects preferred to be approached from either the left or right side, with a small overall preference for a right approach by the robot. Handedness and occupation were not related to these preferences. We discuss the results of the user studies in the context of developing a path planning system for a mobile robot.

This paper describes an extensive analysis of the comfort level data of 7 subjects with respect to 12 robot behaviours as part of a human-robot interaction trial. This includes robot action, proximity and motion relative to the subjects. Two researchers coded the video material, identifying visible states of discomfort displayed by subjects in relation to the robot's behaviour. Agreement between the coders varied from moderate to high, except for more ambiguous situations involving robot approach directions. The detected visible states of discomfort were correlated with the situations where the comfort level device (CLD) indicated states of discomfort. Results show that the uncomfortable states identified by both coders, and by either of the coders corresponded with 31% and 64% of the uncomfortable states identified by the subjects' CLD data (N=58), respectively. Conversely there was 72% agreement between subjects' CLD data and the uncomfortable states identified by both coders (N=25). Results show that the majority of the subjects expressed discomfort when the robot blocked their path or was on a collision course towards them, especially when the robot was within 3 meters proximity. Other observations include that the majority of subjects experienced discomfort when the robot was closer than 3m, within the social zone reserved for human-human face to face conversation, while they were performing a task. The advantages and disadvantages of the CLD in comparison to other techniques for assessing subjects' internal states are discussed and future work concludes the paper.

Programming robots to carry out useful tasks is both a complex and non-trivial exercise. A simple and intuitive method to allow humans to train and shape robot behaviour is clearly a key goal in making this task easier. This paper describes an approach to this problem based on studies of social animals where two teaching strategies are applied to allow a human teacher to train a robot by moulding its actions within a carefully scaffolded environment. Within these enviroments sets of competences can be built by building stateslash action memory maps of the robot's interaction within that environment. These memory maps are then polled using a k-nearest neighbour based algorithm to provide a generalised competence. We take a novel approach in building the memory models by allowing the human teacher to construct them in a hierarchical manner. This mechanism allows a human trainer to build and extend an action-selection mechanism into which new skills can be added to the robot's repertoire of existing competencies. These techniques are implemented on physical Khepera miniature robots and validated on a variety of tasks.

This paper addresses the problem of body mapping in robotic imitation where the demonstrator and imitator may not share the same embodiment (degrees of freedom (DOFs), body morphology, constraints, affordances and so on). Body mappings are formalized using a unified (linear) approach via correspondence matrices, which allow one to capture partial, mirror symmetric, one-to-one, one-to-many, many-to-one and many-to-many associations between various DOFs across dissimilar embodiments. We show how metrics for matching state and action aspects of behaviour can be mathematically determined by such correspondence mappings, which may serve to guide a robotic imitator. The approach is illustrated in a number of examples, using agents described by simple kinematic models and different types of correspondence mappings. Also, focusing on aspects of displacement and orientation of manipulated objects, a selection of metrics are presented, towards a characterization of the space of effect metrics

As robots enter everyday life and start to interact with ordinary people the question of their appearance becomes increasingly important. Our perception of a robot can be strongly influenced by its facial appearance. Synthesizing relevant ideas from narrative art design, the psychology of face recognition, and recent HRI studies into robot faces, we discuss effects of the uncanny valley and the use of iconicity and its relationship to the self-other perceptive divide, as well as abstractness and realism, classifying existing designs along these dimensions. A new expressive HRI research robot called KASPAR is introduced and the results of a preliminary study on human perceptions of robot expressions are discussed

When determining subject preferences for human-robot interaction, an important issue is the interpretation of the subjects' responses during the trials. Employing a non-intrusive approach, this paper discusses the methodological issues for annotating vision data by allowing the subjects to indicate their comfort using a handheld comfort level device during the trials. In previous research, the analysis of collected comfort and vision data was made difficult due to problems concerning the manual synchronization of different modalities. In the current paper, we overcome this issue by real-time integration of the subject's feedback on subjective comfort into the video stream. The implications for more efficient analysis of human-robot interaction data, as well as possible future developments of this approach are discussed

This paper presents an approach for human activity recognition focusing on gestures in a teaching scenario, together with the setup and results of user studies on human gestures exhibited in unconstrained human-robot interaction (HRI). The user studies analyze several aspects: the distribution of gestures, relations, and characteristics of these gestures, and the acceptability of different gesture types in a human-robot teaching scenario. The results are then evaluated with regard to the activity recognition approach. The main effort is to bridge the gap between human activity recognition methods on the one hand and naturally occuring or at least acceptable gestures for HRI on the other. The goal is two-fold: to provide recognition methods with information and requirements on the characteristics and features of human activities in HRI, and to identify human preferences and requirements for the recognition of gestures in human-robot teaching scenarios

This paper describes our general framework for the investigation of how human gestures can be used to facilitate the interaction and communication between humans and robots. More specifically, a study was carried out to reveal which "naturally occurring" gestures can be observed in a scenario where users had to explain to a robot how to perform a specific home task. The study followed a within-subjects design where ten participants had to demonstrate how to lay a table for two people using two different methods for their explanation: utilizing only gestures or gestures and speech. The experiments also served to validate a new coding scheme for human gestures in human-robot interaction, with good inter-rater reliability. Moreover, annotated video corpus was produced and characteristics such as frequency, duration, and co-occurrence of the different gestural classes have been gathered in order to capture requirements for the designers of HRI systems. The results regarding the frequencies of the different gestural types suggest an interaction between the order of presentation of the two methods and the actual type of gestures produced. Moreover, the results also suggest that there might be an interaction between the type of task and the type of gestures produced

The general context of the work presented in this paper is assistive robotics with our long-term aim to support children with autism. This paper is part of an investigation into what ways and to what extent a robot can assume the role of a social mediator - encouraging autistic children to interact with the robot, with each other and with co-present adults. The article focuses on the role of the experimenter in these triadic interactions, and provides a case study evaluation of segments of trials where a robot mediated both indirect and direct interactions between children with autism and the experimenter

An evolutionary predecessor to observational imitation may have been self-imitation. Self-imitation is where an agent is able to learn and replicate actions it has experienced through the manipulation of its body by another. This form of imitative learning has the advantage of avoiding some of the complexities encountered in observational learning such as the correspondence problem. We investigate how a system using self-imitation can be constructed with reference to psychological models of motor control including ideomotor theory and ideas from social scaffolding seen in animals to allow us to construct a robotic control system. The system allows a human trainer to teach a robot new skills and modify existing skills. Additionally the system allows the robot to notify the trainer when it is being taught skills it already possesses. We argue that this mechanism may be the first step towards the transformation from self-imitation to observational imitation. We demonstrate the system on a physical Pioneer robot with a 5-DOF arm and pan/tilt camera which is taught using self-imitation to track and point to coloured objects

The study presented in this paper explored the relationships between subject personality and preferences in the direction from which a robot approached the human participants (N=42) in order to deliver an object in a naturalistic `living room' setting. Personality was assessed using the Big Five Domain Scale. No consistent significant relationships were found between personality traits and preferred approach directions; however, a consistent nonsignificant trend was found in which high scores on the personality trait extraversion was associated with a higher degree of tolerance to the approach directions rated overall as most uncomfortable. The implications of the results are discussed both from a theoretical and methodological viewpoint

The main aim of this study was to confirm the findings from previous pilot studies that results obtained from the same human robot interaction (HRI) scenarios in trials using both video-based and live methodologies were comparable. We investigated how a robot should approach human subjects in various scenarios relevant to the robot fetching an object for the subject. These scenarios include a human subject sitting in an open space, sitting at a table, standing in an open space and standing against a wall. The subjects experienced the robot approaching from various directions for each of these contexts in HRI trials that were both live and video-based. There was a high degree of agreement between the results obtained from both the live and video based trials using the same scenarios. The main findings from both types of trial methodology were: Humans strongly did not like a direct frontal approach by a robot, especially while sitting (even at a table) or while standing with their back to a wall. An approach from the front left or front right was preferred. When standing in an open space a frontal approach was more acceptable and although a rear approach was not usually most preferred, it was generally acceptable to subjects if physically more convenient

The results from two empirical studies of human–robot interaction are presented. The first study involved the subject approaching the static robot and the robot approaching the standing subject. In these trials a small majority of subjects preferred a distance corresponding to the ‘personal zone’ typically used by humans when talking to friends. However, a large minority of subjects got significantly closer, suggesting that they treated the robot differently from a person, and possibly did not view the robot as a social being. The second study involved a scenario where the robot fetched an object that the seated subject had requested, arriving from different approach directions. The results of this second trial indicated that most subjects disliked a frontal approach. Most subjects preferred to be approached from either the left or right side, with a small overall preference for a right approach by the robot. Implications for future work are discussed.

In this paper, we extend our previous work in investigating the performance of different autobiographic memory control architectures which are developed based on a basic subsumption control architecture for artificial life autonomous agents surviving in a dynamic virtual environment. In our previous work we showed how autonomous agents' survival in a static virtual environment can benefit from autobiographic memory, with a kind of communication of experiences in multi-agent experiments. In the current work we extend the existing memory architecture by enhancing its functionalities and introducing long-term autobiographic memory, which is derived from the inspiration of human memory schema - categorical rules or scripts that psychologists in human memory research believe all humans possess to interpret the word, A large-scale and dynamic virtual environment is created to compare the performance of various types of agents with various memory control architectures, and each agent's behaviour is observed and analyzed together with lifespan measurements. Results confirm our previous research hypothesis that autobiographic memory can prove beneficial - indicating increases in the lifespan of an autonomous, autobiographic, minimal agent. Furthermore, the utility of combining long-term memory with short-term memory is established. We finally discuss the environmental factors influencing the performance of each architecture and the areas for future work

Information distance is used to measure how similar sensorimotor experience is to past experience within a certain temporal horizon. Applied to groups of sensors this gives a mathematical metric on sensorimotor experience over time. We show that for complex data from a robot, large scale similarity of experience can be discovered from the robot perspective, providing a means of building an experiential interaction history

The study presented in this paper explored people's perceptions and attitudes towards the idea of a future robot companion for the home. A human-centred approach was adopted using questionnaires and human-robot interaction trials to derive data from 28 adults. Results indicated that a large proportion of participants were in favour of a robot companion and saw the potential role as being an assistant, machine or servant. Few wanted a robot companion to be a friend. Household tasks were preferred to child/animal care tasks. Humanlike communication was desirable for a robot companion, whereas humanlike behaviour and appearance were less essential. Results are discussed in relation to future research directions for the development of robot companions.

This poster considers the use of storyboards, in a classroom setting with children in the 8-12 age group. The storyboarding method allowed children to both generate and evaluate scenarios for a virtual world populated by synthetic characters for exploring bullying issues. This approach has assisted children in the process of visualising agent design and verbalising opinions. It has resulted in design implications that have emerged from enabling children to have a voice in the technology process.

This paper proposes a technique for quantitatively describing and analysing robot-human interactions in terms of low-level behavioural criteria (so-called micro-behaviours). In order to demonstrate the usefulness of this technique, we describe a case study that was conducted as part of the Aurora project where we develop robotic toys as therapeutic tools for children with autism. In this project we made explicit choices of how to assess robot-human interaction and how to study the impact of interaction. Results of a comparative study with autistic children are shown where we focus on eye gaze behaviour. The results point out common tendencies as well as clear differences among the children, important information for future development of robots in the Aurora project. We propose that this technique is applicable to a wide range of application areas that involve robot-human interactions. The work presented in this paper is intended to open up a discussion on appropriate techniques to systematically assess robot-human interactions. Such research is important for the development of robots in human-inhabited environments.

Imitation is a powerful mechanism whereby knowledge may be transferred between agents (both biological and artificial). Key problems on the topic of imitation have emerged in various areas close to artificial intelligence, including the cognitive and social sciences, animal behavior, robotics, human-computer interaction, embodied intelligence, software engineering, programming by example and machine learning. Artificial systems used to study imitation can both test models of imitation derived from observational or neurobiological data on imitation in animals and then apply them to different kinds of nonbiological systems ranging from robots to software agents. A crucial problem in imitation is the correspondence problem, mapping action sequences of the demonstrator and the imitator agent. This problem becomes particularly obvious when the two agents do not share the same embodiment and affordances. This paper describes a new general imitation mechanism called ALICE (action learning for imitation via correspondence between embodiments) that specifically addresses the correspondence problem. The mechanism is implemented and its efficacy illustrated on the "chessworld" testbed that was created to study imitation from an agent-based perspective, i.e., by a particular agent in a particular environment.

April 01 2001 Guest Editors' Introduction: Special Issue on Sensor Evolution Kerstin Dautenhahn, Kerstin Dautenhahn University of Hertfordshire, UK Search for other works by this author on: This Site Google Scholar Daniel Polani, Daniel Polani Medical University Lübeck, Germany Search for other works by this author on: This Site Google Scholar Thomas Uthmann Thomas Uthmann University of Mainz, Germany Search for other works by this author on: This Site Google Scholar Author and Article Information Kerstin Dautenhahn University of Hertfordshire, UK Daniel Polani Medical University Lübeck, Germany Thomas Uthmann University of Mainz, Germany Online Issn: 1530-9185 Print Issn: 1064-5462 © 2001 Massachusetts Institute of Technology2001 Artificial Life (2001) 7 (2): 95–97. https://doi.org/10.1162/106454601753138952 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Kerstin Dautenhahn, Daniel Polani, Thomas Uthmann; Guest Editors' Introduction: Special Issue on Sensor Evolution. Artif Life 2001; 7 (2): 95–97. doi: https://doi.org/10.1162/106454601753138952 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2001 Massachusetts Institute of Technology2001 Article PDF first page preview Close Modal You do not currently have access to this content.

Imitation is a powerful mechanism for efficient learning of novel behaviors that both supports and takes advantage of sociality. A fundamental problem for imitation is to create an appropriate (partial) mapping between the body of the system being imitated and the imitator. By considering for each of these two systems an associated automaton (respectively, transformation semigroup) structure, attempts at such mapping can be considered (partial) relational homomorphisms. This article shows how mathematical techniques can be applied to characterize how far a behavior is from a successful imitation and how to evaluate attempts at imitation arising from a particular correspondence between the imitator and model. For the imitator and the imitated, affordances in the agent-environment structural coupling are likely to be different, all the more so in the case of dissimilar embodiment. We argue that the use of what is afforded to the imitator to attain corresponding effects or, as in dance, sequences of effects, is necessary and sufficient for successful imitation. However, the judged degree of success or failure of an attempted behavioral match depends on some externally imposed or in the case ofautonomous agents internally determined criteria on effects of the attempted imitative behavior (including effects attained successively as well as final effects). These criteria correspond to metrics measures of difference which can guide the evaluation of a correspondence, the learning of a correspondence, or learning how to apply one. Metrics on states and sequences of action events in the system-environment coupling allow judgment of similarity for observer-dependent' purposes. This allows one to formally define successful imitation with respect to such criteria. The resulting measures can be used to compare various candidate mappings (e.g., body plan or perception-action correspondences). Additionally, this may be applied in the automated construction and learning of mappings to be used in imitation for artificial, hardware, and software systems.

This paper discusses the phenomenological dimension of social understanding. The author's general hypothesis is that complex forms of social understanding that biological agents especially humans show are based on two mechanisms: 1 the bodily, experiential dynamics of emphatic resonance and 2 the biographic reconstruction of a communication situation. The latter requires the agent's bodily experiences as the point of reference for the reconstruction process. This hypothesis is derived from discussions in philosophy, natural sciences, and cognitive science on the social embodiment of cognition and understanding. Evidence comes from studies on social cognition in primates, infants, and autistic people that are interpreted in terms of the "mind-experiencing" hypothesis. The second part of the paper sketches an ''interactive'' experiment that investigates the dynamic coupling of a robot with its environment. This example is used to discuss the role of the human observer and designer as an active, embodied agent who is biased toward interpreting the world in terms of intentionality and explanation. The paper describes how this aspect can influence the processes of understanding and interpretation of the behavior of autonomous robotic agents. The author concludes by stressing the need to overcome the distinction between computationalism and phenomenology in order to develop complex artificial systems.