Social robots have the potential to provide support in a number of practical domains, such as learning and behaviour change. This potential is particularly relevant for children, who have proven receptive to interactions with social robots. To reach learning and therapeutic goals, a number of issues need to be investigated, notably the design of an effective child-robot interaction (cHRI) to ensure the child remains engaged in the relationship and that educational goals are met. Typically, current cHRI research experiments focus on a single type of interaction activity (e.g. a game). However, these can suffer from a lack of adaptation to the child, or from an increasingly repetitive nature of the activity and interaction. In this paper, we motivate and propose a practicable solution to this issue: an adaptive robot able to switch between multiple activities within single interactions. We describe a system that embodies this idea, and present a case study in which diabetic children collaboratively learn with the robot about various aspects of managing their condition. We demonstrate the ability of our system to induce a varied interaction and show the potential of this approach both as an educational tool and as a research method for long-term cHRI.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.In order for robots to be socially accepted and generate empathy they must display emotions. For robots such as Nao, body language is the best medium available, as they do not have the ability to display facial expressions. Displaying emotional body language that can be interpreted whilst interacting with the robot should greatly improve its acceptance. This research investigates the creation of an "Affect Space" [1] for the generation of emotional body language that could be displayed by robots. An Affect Space is generated by "blending" (i.e. interpolating between) different emotional expressions to create new ones. An Affect Space for body language based on the Circumplex Model of emotions [2] has been created. The experiment reported in this paper investigated the perception of specific key poses from the Affect Space. The results suggest that this Affect Space for body expressions can be used to improve the expressiveness of humanoid robots. In addition, early results of a pilot study are described. It revealed that the context helps human subjects improve their recognition rate during a human-robot imitation game, and in turn this recognition leads to better outcome of the interactions

Based on research in developmental robotics and psychology findings in attachment theory in young infants, we designed an arousal-based model controlling the behaviour of a Sony AIBO robot during the exploration of a children play mat. When the robot experiences too many new perceptions, the increase of arousal triggers calls for attention from its human caregiver. The caregiver can choose to either calm the robot down by providing it with comfort, or to leave the robot coping with the situation on its own . When the arousal of the robot has decreased, the robot moves on to further explore the play mat. We present here the results of two experiments using this arousal-driven control architecture. In the first setting, we show that such a robotic architecture allows the human caregiver to influence greatly the learning outcomes of the exploration episode, with some similarities to a primary caregiver during early childhood. In a second experiment, we tested how human adults behaved in a similar setup with two different robots: one needy, often demanding attention, and one more independent, requesting far less care or assistance. Our results show that human adults recognise each profile of the robot for what they have been designed, and behave accordingly to what would be expected, caring more for the needy robot than the other. Additionally, the subjects exhibited a preference and more positive affect whilst interacting and rating the robot we designed as needy. This experiment leads us to the conclusion that our architecture and setup succeeded in eliciting positive and caregiving behaviour from adults of different age groups and technological background. Finally, the consistency and reactivity of the robot during this dyadic interaction appeared crucial for the enjoyment and engagement of the human partner.In the remainder of this paper, we present early research trying to bridge the field of developmental robotics with attachment theory, which besides a thought experiment [Kaplan 2001] and a fairly remote use of the theory [Arkin 1998], has largely remained an unexplored area of research. The main goal is to address how a robotic platform could use the properties of this bond in order to thrive from it, as children most often manage to do. To that end, we present a body of related work that we took inspiration from, then describe what properties of attachment we used and why they are relevant to the design of robotic architectures. We finally present the results of two experiments that bring together our findings, and assess to what extent we improved the state of the art of the field concerned with developing robots and improving human-robot interactions.

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The use of robots had already been proven to encourage the promotion of social interaction and skills lacking in children with Autism Spectrum Disorders (ASD), who typically have difficulties in recognizing facial expressions and emotions. The main goal of this research is to study the influence of a humanoid robot to develop socio-emotional skills in children with ASD. The children’s performance in game scenarios aiming to develop facial expressions recognition skills is presented. Along the sessions, children who performed the game scenarios with the robot and the experimenter had a significantly better performance than the children who performed the game scenarios without the robot. The main conclusions of this research support that a humanoid robot is a useful tool to develop socio-emotional skills in the intervention of children with ASD, due to the engagement and positive learning outcome observed.