Towards long-term social child-robot interaction: using multi-activity switching to engage young users

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

Children’s responses and opinion on three bots that motivate, educate and play

Social robots may help children in their daily health-care related activities, such as adherence to diet and exercises of diabetics. Based on a domain and literature study, we specified three support roles with corresponding bot behaviors: motivator, educator and buddy. These behaviors, such as showing attentiveness, could be implemented well in a physical character (the iCat robot), somewhat less well in a virtual character, and least well in a text interface. Twenty-eight to nine years old-children participated in a controlled experiment to evaluate the bots. They proved to value the support roles positively, in particular the buddy role. Objective and subjective data showed that they highly appreciated both the physical and virtual characters (more than the text interface). Furthermore, children proved to interact faster with the character than with the text interface. There is a clear added value of robots compared to conventional text interfaces.IOP-MMI SenterNovem a program of the Dutch Ministry of Economics, partially funding the SuperAssist project

Personalising game difficulty to keep children motivated to play with a social robot: a Bayesian approach

Playing games with a social robot should be engaging and keep a child motivated to play the games with the robot for a longer period of time. One aspect that can affect the motivation of a child is the difficulty of the game. A game should be perceived as challenging, while at the same time, the child should be confident to meet the challenge. We designed a user modelling system that adapts the difficulty of a game to the child's skill, in order to provide children with the optimal challenge. To this end, we used a Bayesian rating system to estimate the child's skill and performance. In the experiment, we used our user modelling system to test whether children whom are optimally challenged are more intrinsically motivated to play games with the robot, than children whom are not optimally challenged. 22 children participated in the experiment, aged between 10 and 12 years old. While we were not able to provide the optimal challenge, this study shows that using a Bayesian rating system to measure the skill and performance of children in playing a game is feasible, even without accurate estimates of the difficulty of items and skill of the children. We out- line multiple ways in which a rating system can be used to improve the child-robot interaction, other than adapting the difficulty of games, illustrating the possible benefits of using a rating system on a social robot

How to improve human-robot interaction with Conversational Fillers

Conversation Fillers (CFs), such as `um', `hmm', and `ah', may help to improve the human-robot interaction by smoothening the robot's responses. This paper presents the design and test of such CFs - alongside iconic pensive or acknowledging gestures - for Wizard of Oz (WoZ) controlled open-ended dialogues in child-robot interactions. A controlled experiment with 26 children showed that these CFs can improve the perceived speediness, aliveness, humanness, and likability of the robot, without decreasing perceptions of intelligence, trustworthiness, or autonomy

Personalising game difficulty to keep children motivated to play with a social robot:a Bayesian approach

For effective child education, playing games with a social robot should be motivating for a longer period of time. One aspect that can affect the motivation of a child is the difficulty of a game. The game should be perceived as challenging, while at the same time, the child should be confident to meet the challenge. We designed a user modelling module that adapts the difficulty of a game to the child’s skill level, in order to provide children with the optimal challenge. This module applies a Bayesian rating method that estimates the child’s skill and game item’s difficulty levels to personalise the game progress. In an experiment with 22 children (aged between 10 and 12 years old), we tested whether the personalisation leads to a higher motivation to play with the robot. Although the personalised system did not challenge the participants optimally, this study shows that the Bayesian rating system is in principle able to measure the skill and performance of children in playing a game with a robot (even without accurate estimates of the difficulty of items). We outline multiple ways in which the rating method and module can be used to further personalise and enhance the child-robot interaction, other than adapting the difficulty of games (e.g. by adapting the dialogue and feedback)

Adaptive emotional expression in robot-child interaction

Expressive behaviour is a vital aspect of human interaction. A model for adaptive emotion expression was developed for the Nao robot. The robot has an internal arousal and va- lence value, which are in uenced by the emotional state of its interaction partner and emotional occurrences such as win- ning a game. It expresses these emotions through its voice, posture, whole body poses, eye colour and gestures. An ex- periment with 18 children (mean age 9) and two Nao robots was conducted to study the in uence of adaptive emotion expression on the interaction behaviour and opinions of chil- dren. In a within-subjects design the children played a quiz with both an aective robot using the model for adaptive emotion expression and a non-aective robot without this model. The aective robot reacted to the emotions of the child using the implementation of the model, the emotions of the child were interpreted by aWizard of Oz. The dependent variables, namely the behaviour and opinions of the children, were measured through video analysis and questionnaires. The results show that children react more expressively and more positively to a robot which adaptively expresses itself than to a robot which does not. The feedback of the children in the questionnaires further suggests that showing emotion through movement is considered a very positive trait for a robot. From their positive reactions we can conclude that children enjoy interacting with a robot which adaptively ex- presses itself through emotion and gesture more than with a robot which does not do this