Robot-assisted feeding enables people with disabilities who require assistance eating to enjoy a meal independently and with dignity. However, existing systems have only been tested in-lab or in-home, leaving in-the-wild social dining contexts (e.g., restaurants) largely unexplored. Designing a robot for such contexts presents unique challenges, such as dynamic and unsupervised dining environments that a robot needs to account for and respond to. Through speculative participatory design with people with disabilities, supported by semi-structured interviews and a custom AI-based visual storyboarding tool, we uncovered ideal scenarios for in-the-wild social dining. Our key insight suggests that such systems should: embody the principles of a white glove service where the robot (1) supports multimodal inputs and unobtrusive outputs; (2) has contextually sensitive social behavior and prioritizes the user; (3) has expanded roles beyond feeding; (4) adapts to other relationships at the dining table. Our work has implications for in-the-wild and group contexts of robot-assisted feeding.

Human-Robot Interaction (HRI) research in real-world settings may lead to unanticipated, emotionally charged moments. While impacts of these moments on participants are reported in the literature, researchers’ emotions, which can affect participants’ experiences, are often left unreported. Learning from these moments is essential for advancing HRI quality and real-world deployment success. We introduce "Emotional Entanglements" as a lens in HRI to define a researcher's capacity to anticipate, absorb, respond to, and recover from emotionally impactful events. Collecting testimonials using collaborative autoethnography from eleven researchers, we surface recurring emotional entanglements experienced in HRI studies, including tears with mixed meaning, participant attachment and loss upon robot withdrawal, and consequential participant decisions attributed to the robot, as well as how researchers navigated them amidst protocol constraints. This paper provides an actionable guide to "Emotional Sustainability in HRI", raising awareness of these often unreported situations and offering strategies for mitigation.

We contribute to new design directions towards robots that can socially engage with pediatric patients while imaging their eyes reliably. Eye imaging is essential to diagnose and manage ocular diseases, but practically impossible to conduct due to children's fear and aversion during the exam. Pediatric patients frequently require an exam under anesthesia, adding significant medical risk, stress, delay, and cost of care. We explored the design space of character design for an eye-imaging robot system to make eye exams more fun for children. Using contextual inquiry, we collected needs from stakeholders around eye exams, leading to the understanding of pain points. We then conducted design explorations of robot characters that could mitigate negative effects while amplifying moments of fun for pediatric patients. We built two low-fidelity robot characters and showed them to stakeholders. Our research highlights the need for these approachable characters to realize eye exams in pediatric patients with engagement.

Social dining, i.e., eating with/in company, is replete with meaning and cultural significance. Unfortunately, for the 1.8 million Americans with motor impairments who cannot eat without assistance, challenges restrict them from enjoying this pleasant social ritual. In this work, we identify the needs of participants with motor impairments during social dining and how robot-assisted feeding can address them. Using speculative videos that show robot behaviors within a social dining context, we interviewed participants to understand their preferences. Following a community-based participatory research method, we worked with a community researcher with motor impairments throughout this study. We contribute (a) insights into how a robot can help overcome challenges in social dining, (b) design principles for creating robot-assisted feeding systems, (c) and an implementation guide for future research in this area. Our key finding is that robots' unique assistive qualities can address challenges people with motor impairments face during social dining, promoting empowerment and belonging.

The social robotics market is appealing yet challenging. Though social robots are built few remain on the market for long. Many reasons account for their short lifespan with costs and context-specificity ranking high amount them. In this work, we designed, fabricated, and developed FLEXI, a social robot embodiment kit that enabled unlimited customization, making it applicable for a broad range of use cases. The hardware and software of FLEXI were entirely developed by this research team from scratch. FLEXI includes a rich set of materials and attachment pieces to allow for a diverse range of hardware customizations that ensure the embodiment is appropriate for specific customer/researcher projects. It also includes an open-source end-user programming interface to lower the barrier of robotics access to interdisciplinary teams that populate the field of Human-Robot Interaction. We present an iterative development of this cost-effective kit through the lenses of case studies, conceptual research, and soft deployment of FLEXI in three application scenarios: community-support, mental health, and education. Additionally, we provide in open-access the full list of materials and a tutorial to fabricate FLEXI, making it accessible to any maker space, research lab, or workshop space interested in working with or learning about social robots.

While creativity has been previously studied in Child-Robot Interaction (cHRI), the effect of regulatory focus on creativity skills has not been investigated. This paper presents an exploratory study that, for the first time, uses the Regulatory Focus Theory (RFT) to assess children's creativity skills in an educational context with a social robot. We investigated whether two key emotional regulation techniques, promotion (approach) and prevention (avoidance), stimulate creativity during a story-telling activity between a child and a robot. We conducted a between-subjects field study with 69 children between the ages of 7 and 9 years old, divided between two study conditions: (1) promotion, where a social robot primes children for action by eliciting positive emotional states, and (2) prevention, where a social robot primes children for avoidance by evoking a states related to security and safety associated with blockage-oriented behaviors. To assess changes in creativity as a response to the priming interaction, children were asked to tell stories to the robot before (pre-test) and after (post-test) the priming interaction. We measured creativity levels by analyzing the verbal content of the stories. We coded verbal expressions related to creativity variables, including fluency, flexibility, elaboration, and originality. Our results show that children in the promotion condition generated significantly more ideas, and their ideas were on average more original in the stories they created in the post-test rather than in the pre-test. We also modeled the process of creativity that emerges during storytelling in response to the robot's verbal behavior. This paper enriches the scientific understanding of creativity emergence in child-robot collaborative interactions.

While the accumulation of practical knowledge provided researchers with much insight into successful human-robot interaction (HRI), a broader discussion about the role of theoretical knowledge is still lacking. It is unfortunate because it is also important to explicitly consider theory and theorizing in HRI as crucial contributions when aspiring to develop this field of research into a mature science. With our proposed interactive half-day workshop, we aim to provide a vibrant setting for the participants to discuss the “what, why, and how” of theoretical knowledge in HRI, as they share and learn from each other's experiences and competence. In the long-term perspective, the outcome of this workshop will lay the foundation for a supportive research community that encourages researchers to reflect and collaborate further on theory-grounded HRI work.

Social robots have been used to support mental health. In this work, we explored their potential as community-based tools. Visualizing mood data patterns of a community with a social robot might help the community raise awareness about the emotions people feel and affecting factors from life events. This could potentially lead to adaptation of suitable coping skills enhancing the sense of belonging and support among community members. We present preliminary findings and ongoing plans for this human-robot interaction (HRI) research work on data visualizations supporting community mental health. In a two-day study, twelve participants recruited from a university community engaged with a robot displaying mood data. Given the feedback from the study, we improved the data visualization in the robot to increase accessibility, universality, and usefulness of such visualizations. In the future, we plan on conducting studies with this improved version and deploying a social robot for a community setting.

Adolescents isolated at home during the COVID19 pandemic lockdown are more likely to feel lonely and in need of social connection. Social robots may provide a much needed social interaction without the risk of contracting an infection. In this paper, we detail our co-design process used to engage adolescents in the design of a social robot prototype intended to broadly support their mental health. Data gathered from our four week design study of nine remote sessions and interviews with 16 adolescents suggested the following design requirements for a home robot: (1) be able to enact a set of roles including a coach, companion, and confidant; (2) amplify human-to-human connection by supporting peer relationships; (3) account for data privacy and device ownership. Design materials are available in open-access, contributing to best practices for the field of Human-Robot Interaction.

We present FLEX-SDK: an open-source software development kit that allows creating a social robot from two simple tablet screens. FLEX-SDK involves tools for designing the robot face and its facial expressions, creating screens for input/output interactions, controlling the robot through a Wizard-of-Oz interface, and scripting autonomous interactions through a simple text-based programming interface. We demonstrate how this system can be used to replicate an interaction study and we present nine case studies involving controlled experiments, observational studies, participatory design sessions, and outreach activities in which our tools were used by researchers and participants to create and interact with social robots. We discuss common observations and lessons learned from these case studies. Our work demonstrates the potential of FLEX-SDK to lower the barrier to entry for Human-Robot Interaction research.

Mobile robots struggle to integrate seamlessly in crowded environments such as pedestrian scenes, often disrupting human activity. One obstacle preventing their smooth integration is our limited understanding of how humans may perceive and react to robot motion. Motivated by recent studies highlighting the benefits of intent-expressive motion for robots operating close to humans, we describe Social Momentum (SM), a planning framework for legible robot motion generation in multiagent domains. We investigate the properties of motion generated by SM via two large-scale user studies: an online, video-based study ( N = 180) focusing on the legibility of motion produced by SM and a lab study ( N = 105) focusing on the perceptions of users navigating next to a robot running SM in a crowded space. Through statistical and thematic analyses of collected data, we present evidence suggesting that (a) motion generated by SM enables quick inference of the robot’s navigation strategy; (b) humans navigating close to a robot running SM follow comfortable, low-acceleration paths; and (c) robot motion generated by SM is positively perceived and indistinguishable from a teleoperated baseline. Through the discussion of experimental insights and lessons learned, this article aspires to inform future algorithmic and experimental design for social robot navigation.

The word “robot” frequently conjures unrealistic expectations of utilitarian perfection: tireless, efficient and flawless agents. However, real-world robots are far from perfect—they fail and make mistakes. Thus, roboticists should consider altering their current assumptions and cultivating new perspectives that account for a more complete range of robot roles, behaviors, and interactions. To encourage this, we explore the use of metaphors for generating novel ideas and reframing existing problems, eliciting new perspectives of human-robot interaction. Our work makes two contributions. We (1) surface current assumptions that accompany the term “robots,” and (2) present a collection of alternative perspectives of interaction with robots through metaphors. By identifying assumptions, we provide a comprehensible list of aspects to reconsider regarding robots’ physicality, roles, and behaviors. Through metaphors, we propose new ways of examining how we can use, relate to, and co-exist with the robots that will share our future.

Robots have massively been introduced in children’s lives, showing promising effects on education and learning. Parallel to this, children’s creative levels show a decline related to different factors, including the standardized teaching and learning dynamics present in traditional school systems. This work aims to investigate if the activities with robots already present in schools affect children’s creativity levels. To study this, we compared creative levels of children across three study conditions: (1) Experimental condition 1: Children performed STEM activities in school by learning how to program robots; (2) Experimental condition 2: Children performed STEAM activities by learning how to design robots; (3) Control condition: Children engaged in a music class. We applied the Test for Creative Thinking-Drawing Production (TCT-DP), a validated test that measures creative potential, before and after the intervention. Our results showed that the creativity levels of children increased from pre- to post-testing, revealing the effect of all intervention groups in potentiating creativity. Additionally, results showed that creative levels were significantly higher in the control condition. This result was expected since this condition consisted of an artistic musical intervention where creativity is foreseen to be stimulated. When analyzing the effects of the interventions on the two dimensions of TCT-DP (i.e., adaptiveness and innovativeness), results showed that both the control and the programming condition stimulated innovativeness. This result seems to show that STEM activities can stimulate non-conventional ways of thinking, similarly to creative activities such as a music class. While much has been studied about how STEM activities influence the knowledge of children, little is known if STEM also contributes to the stimulation of their creativity. This study promotes investigation in this topic and shows the potential of using robots to unlock creative potentials in children.

We present the design process of the robot YOLO aimed at stimulating creativity in children. This robot was developed under a human-centered design approach with participatory design practices during two years and involving 142 children as active contributors at all design stages. The main contribution of this work is the development of methods and tools for child-centered robot design. We adapted existing participatory design practices used with adults to fit children's development stages. We followed the Double-Diamond Design Process Model and rested the design process of the robot on the following principles: low floor and wide walls, creativity provocations, open-ended playfulness, and disappointment avoidance through abstraction. The final product is a social robot designed for and with children. Our results show that YOLO increases their creativity during play, demonstrating a successful robot design project. We identified several guidelines that made the design process successful: the use of toys as tools, playgrounds as spaces, the emphasis of playfulness for child expression, and child policies as allies for design studies. The design process described empowers children's in the design of robots.

Creativity is an intrinsic human ability with multiple benefits across lifespan. Despite its importance, societies not always are well equipped to provide contexts for creativity stimulation; as a consequence, a major decline in creative abilities occurs at the age of 7 years old. In this paper, we investigated the effectiveness of using a robotic system named YOLO, as an intervention tool to stimulate creativity in children. During the intervention, children used YOLO as a character for their stories and through the interaction with the robot, creative abilities were stimulated. Our study (n = 62) included 3 experimental conditions: i) YOLO displayed behaviors based on creativity techniques; ii) YOLO displayed behaviors based on creativity techniques plus social behaviors; iii) YOLO was turned off, not displaying any behaviors. We measured children's creative abilities at pre and posttesting and their creative process through behavior analysis. Results showed that the interaction with YOLO contributed to higher creativity levels in children, specifically contributing to the generation of more original ideas during story creation. This study shows the potential of using social robots as tools to empower intrinsic human abilities, such as the ability of being creative.

Detecting, modeling, and making sense of multimodal data from human users in the wild still poses numerous challenges. Starting from aspects of data quality and reliability of our measurement instruments, the multidisciplinary endeavor of developing intelligent adaptive systems in human-computer or human-robot interaction (HCI, HRI) requires a broad range of expertise and more integrative efforts to make such systems reliable, engaging, and user-friendly. At the same time, the spectrum of applications for machine learning and modeling of multimodal data in the wild keeps expanding. From the classroom to the robot-assisted operation theatre, our workshop aims to support a vibrant exchange about current trends and methods in the field of modeling multimodal data in the wild.

Robots are being increasingly developed as social actors, entering public and personal spaces such as airports, shopping malls, care centres, and even homes, and using human or animal-like social techniques to work with people. Some even aim to engineer social situations, or are designed specifically for an emotional response (e.g., comforting a person). However, if we consider these robots as social interventions, then it is important to recognize that the robots design - its behavior, its application, its appearance, even its marketing image - will have an impact on the society and in the spaces it enters. While in some cases this may be a positive effect, social robots can also contribute negatively, e.g., reinforcing gender stereotypes or promoting ageist views. This full-day workshop aims to offer a forum for Human-Robot Interaction (HRI) researchers to explore this issue, and to work toward potential opportunities for the field. Ultimately, we want to promote robots for social good that can contribute to positive social changes for socio-political issues (e.g., ageism, feminism, homelessness, environmental issues). The political aspects of technologies have long been scrutinized in related areas such as Science and Technology Studies (STS) and Human-Computer Interaction (HCI). In particular, critical design explicitly targets the design of technologies that can contribute to our understanding of how technology can impact society. This workshop aims to strengthen this discussion in the HRI community, with the goal of working toward initial recommendations for how HRI designers can include elements of critical design in their work.

State-of-the-art social robot navigation algorithms often lack a thorough experimental validation in human environments: simulated evaluations are often conducted under unrealistically strong assumptions that prohibit deployment in real world environments; experimental demonstrations that are limited in sample size do not provide adequate evidence regarding the user experience and the robot behavior; field studies may suffer from the noise imposed by uncontrollable factors from the environment; controlled lab experiments often fail to properly enforce challenging interaction settings. This paper contributes a first step towards addressing the outlined gaps in the literature. We present an original experiment, designed to test the implicit interaction between a mobile robot and a group of navigating human participants, under challenging settings in a controlled lab environment. We conducted a large-scale, within-subjects design study with 105 participants, exposed to three different conditions, corresponding to three distinct navigation strategies, executed by a telepresence robot (two autonomous, one teleoperated). We analyzed observed human and robot trajectories, under close interaction settings and participants' impressions regarding the robot's behavior. Key findings, extracted from a comparative statistical analysis include: (1) evidence that human acceleration is lower when navigating around an autonomous robot compared to a teleoperated one; (2) the lack of evidence to support the conventional expectation that teleoperation would be humans' preferred strategy. To the best of our knowledge, our study is unique in terms of goals, settings, thoroughness of evaluation and sample size.

This work explores a group learning scenario with an autonomous empathic robot. We address two research questions: (1) Can an autonomous robot designed with empathic competencies foster collaborative learning in a group context? (2) Can an empathic robot sustain positive educational outcomes in long-term collaborative learning interactions with groups of students? To answer these questions, we developed an autonomous robot with empathic competencies that is able to interact with a group of students in a learning activity about sustainable development. Two studies were conducted. The first study compares learning outcomes in children across three conditions: learning with an empathic robot; learning with a robot without empathic capabilities; and learning without a robot. The results show that the autonomous robot with empathy fosters meaningful discussions about sustainability, which is a learning outcome in sustainability education. The second study features groups of students who interact with the robot in a school classroom for 2 months. The long-term educational interaction did not seem to provide significant learning gains, although there was a change in game-actions to achieve more sustainability during game-play. This result reflects the need to perform more long-term research in the field of educational robots for group learning.

This study investigated non-verbal behavior and socioemotional interactions in small-groups of humans and robots. Sixty-participants were involved in a group setting in which they were required to play a card game with another human and two robots (playing as partners or as opponents). The two robots displayed different goal orientations: a competitive robot (named Emys-) and a relationship-driven cooperative robot (named Glin+). Video recordings of the interactions were analyzed in three game play sessions. Eye gaze and socioemotional support behaviors were coded based on Bales» Interaction Process Analysis. Results indicated that gaze behavior towards partners was more frequently displayed to the relationship-driven robot than to the competitive robot and the human partners. In contrast, gaze towards opponents occurred more often towards the competitive robot than to the relationship-driven robot and the human opponents. Socioemotional support occurred more frequently towards partners than opponents, and was also displayed more often towards humans than towards robots. Moreover, in the sessions where the robots were opponents, participants provided more support to the competitive robot. This investigation in small groups of humans and robots provided evidence of different interaction patterns towards robots displaying distinct orientation goals, which can be useful in guiding the successful design of social robots.

This paper describes the design and development of YOLO, a social robot aimed at boosting creativity in children. Creativity is one of the most sought-after competencies as we move from industrialized economies, in which standardized knowledge was imperative, to creative economies, where the ability to innovate is crucial for the workforce. YOLO is a robot to be used by children as a tool to boost new ideas and stimulate their creativity. This paper describes how established educational strategies that enhance creativity were combined with co-designing with children as informants to reach the the prototype design of the robot.

This paper presents a study on collaborative manipulation between an autonomous robot and multiple users. We investigate how different motion types impact people's ability to understand the robot's goals in a multi-user scenario. We propose an approach based on Collaborative Probabilistic Movement Primitives to generate the robot's movements, exploiting predictability and legibility of movement to express intentions through motion. We compare the impact on the interaction of using only either predictable or legible movements, and propose a third approach - hybrid motion - that selects, in each situation, whether to execute a predictable motion or a legible motion, depending on what the robot perceives as more efficient for the multi-user collaboration effort. To test the impact of the three motion types in the context of a collaborative task, we run a user study using a Baxter robot that autonomously serves cups of water to three users upon request. Our results show that, in the particular case where all users simultaneously request water, the hybrid motion performs better than the other two.

Science fiction media has had a long lasting influence on the progression of interactive technology, however recently contradictions are emerging in the development of the two disciplines. Therefore, in this exploratory position paper we report on the insights attained through a day long workshop amongst scientists and researchers on how the collaboration between science fiction and Human Computer Interaction (HCI) can be advanced. Discussions in the workshop focused on detailing the relationship between HCI and science fiction. In conclusion, as our main contribution an action plan and agenda is presented for facilitating deeper influences amongst the two disciplines.

Creativity is one of the most important and pervasive of all human abilities. However, it seems to decline during school age years, in a phenomenon entitled “creative crisis”. As developed societies are shifting from an industrialized economy to a creative economy, there is a need to support creative abilities through life. With this work, we aim to use social robots as boosters for creative-driven behaviors with children.

In this video we present a social robotic player that is able to play a traditional card game in a social manner. The interaction takes place in a rich environment in which two teams of two players each compete to win the card game. Therefore, the robotic game player has a partner, and an opponent team of two other players. During each game, the robot explores both competitiveness with the opponent team and cooperation with its partner, conciliating the performance of players and the social dynamics that emerge during the game-play.

In this video submission, we describe a scenario developed in the EMOTE project. The overall goal of the EMOTE project is to develop an empathic robot tutor for 11-13 year old school students in an educational setting. The pedagogical domain here is to assist students in learning and testing their map-reading skills typically learned as part of the geography curriculum in schools. We show this scenario with a NAO robot interacting with the students whilst performing map-reading tasks on a touch-screen device in this video.

In this paper we propose a methodology for the creation of social interaction strategies for human-robot interaction based on restricted-perception Wizard-of-Oz studies (WoZ). This novel experimental technique involves restricting the wizard's perceptions over the environment and the behaviors it controls according to the robot's inherent perceptual and acting limitations. Within our methodology, the robot's design lifecycle is divided into three consecutive phases, namely data collection, where we perform interaction studies to extract expert knowledge and interaction data; strategy extraction, where a hybrid strategy controller for the robot is learned based on the gathered data; strategy refinement, where the controller is iteratively evaluated and adjusted. We developed a fully-autonomous robotic tutor based on the proposed approach in the context of a collaborative learning scenario. The results of the evaluation study show that, by performing restricted-perception WoZ studies, our robots are able to engage in very natural and socially-aware interactions.

Human beings naturally assign roles to one another while interacting. Role assignment is a way to organize interpersonal encounters and can result in uncertainty decrease when facing a novel interaction with someone we just met, or even to rediscover new roles within previous relationships. When people interact with synthetic characters - such as robots - it seems they also assign roles to these agents, just as they do with humans. Within the field of human-robot interaction (HRI), robots are being developed to fulfill specific roles. This enables researchers to design concrete behaviors that match the desired role that a robot will play in a given task. It would then be expected that if a robot is developed with such a specific role, users too would assign the same role to that robot. In this paper, we study how children assign roles to an educational robot whose role is established from the beginning of the interaction. Our results show that although the role that the robot played was explicitly presented to children, they end up perceiving and assigning different roles for that robot. Moreover, we conclude that role assignment in educational HRI is a dynamic process in which the perceptions of children regarding the robot change over time as a consequence of continuous interactions.

Research in education has long established how children mutually influence and support each other's learning trajectories, eventually leading to the development and widespread use of learning methods based on peer activities. In order to explore children's learning behavior in the presence of a robotic facilitator during a collaborative writing activity, we investigated how they assess their peers in two specific group learning situations: peer-tutoring and peer-learning. Our scenario comprises of a pair of children performing a collaborative activity involving the act of writing a word/letter on a tactile tablet. In the peer-tutoring condition, one child acts as the teacher and the other as the learner, while in the peer-learning condition, both children are learners without the attribution of any specific role. Our experiment includes 40 children in total (between 6 and 8 years old) over the two conditions, each time in the presence of a robot facilitator. Our results suggest that the peer-tutoring situation leads to significantly more corrective feedback being provided, as well as the children more disposed to self-disclosure to the robot.

Robots are currently being developed to enter our lives and interact with us in different tasks. For humans to be able to have a positive experience of interaction with such robots, they need to trust them to some degree. In this paper, we present the development and evaluation of a social robot that was created to play a card game with humans, playing the role of a partner and opponent. This type of activity is especially important, since our target group is elderly people - a population that often suffers from social isolation. Moreover, the card game scenario can lead to the development of interesting trust dynamics during the interaction, in which the human that partners with the robot needs to trust it in order to succeed and win the game. The design of the robot's behavior and game dynamics was inspired in previous user-centered design studies in which elderly people played the same game. Our evaluation results show that the levels of trust differ according to the previous knowledge that players have of their partners. Thus, humans seem to significantly increase their trust level towards a robot they already know, whilst maintaining the same level of trust in a human that they also previously knew. Henceforth, this paper shows that trust is a multifaceted construct that develops differently for humans and robots.

In recent years, robots have gradually become incorporated in our society and therefore play more relevant role in social environments. These robots vary in form, some being more anthropomorphic than others. This, creates a need to study their interaction with the world. In this paper we used Sphero and BB-8, two robots with a simple spherical body devoid of verbal and other complex communication methods, to investigate how they can communicate intention to people. A set of behaviors based on pet behaviors was designed and tested in a controlled experiment, where the robot's aim was to convince a participant to follow it. We concluded that the use of these behaviors allows a robot to effectively communicate intention as well as create a bond with the participant, who would treat it as an equal, thereby engaging it in social interactions such as playing with it or talking to it.

Is a robot that shares explicitly its emotions with users more believable and friendly? In a previous study addressing this question, results suggested that an emotion sharing feature in a robot may have negative effects in the perception of that robot. Here, we address the same question but also take into account the “competence” of a robot executing a task, to understand if some kind of interaction occurs.

The way gaze cues are used in social interactions is by no means irrelevant because they are fundamentally important for understanding social interactions. In this paper, we argue that social conflict is a form of relating and that gaze clues are critical to understanding the underlying cognitive processes in this phenomenon. To learn more about conflict, we created an experimental setting that reduces real life to a mixed-motive game. We analyse the gaze patterns of 22 10- to 12-year-old children in specific game moments that could have been conductive to conflict. Our aim is to understand how subtle forms of conflict unfold, by analysing micro-level behaviours and establishing a link to high-level psychological constructs. Their gazes show that children are being more competitive or cooperative at different stages of the game. Children tend to avoid confrontation by averting face-directed gazes when they are asking for larger profits, and they gaze longer to attempt to persuade the other child.

In order to explore the impact of integrating a robot as a facilitator in a collaborative activity, we examined interpersonal distancing of children both with a human adult and a robot facilitator. Our scenario involves two children performing a collaborative learning activity, which included the writing of a word/letter on a tactile tablet. Based on the learning-by-teaching paradigm, one of the children acted as a teacher when the other acted as a learner. Our study involved 40 children between 6 and 8 years old, in two conditions (robot or human facilitator). The results suggest first that the child acting as a teacher feel more responsible when the facilitator is a robot, compared to a human; they show then that the interaction between a (teacher) child and a robot facilitator can be characterized as being a reciprocity-based interaction, whereas a human presence fosters a compensation-based interaction.

There has been some studies in applying robots to education and recent research on socially intelligent robots show robots as partners that collaborate with people. On the other hand, serious games and interaction technologies have also proved to be important pedagogical tools, enhancing collaboration and interest in the learning process. This paper relates to the collaborative scenario in EMOTE EU FP7 project and its main goal is to develop and present the dialogue dimensions for a robotic tutor in a collaborative learning scenario grounded in human studies. Overall, seven dialogue dimensions between the teacher and students interaction were identified from data collected over 10 sessions of a collaborative serious game. Preliminary results regarding the teachers perspective of the students interaction suggest that student collaboration led to learning during the game. Besides, students seem to have learned a number of concepts as they played the game. We also present the protocol that was followed for the purposes of future data collection in human-human and human-robot interaction in similar scenarios.