A Novel Reinforcement-Based Paradigm for Children to Teach the Humanoid Kaspar Robot

© The Author(s) 2019. This is the final published version of an article published in Psychological Research, licensed under a Creative Commons Attri-bution 4.0 International License. Available online at: https://doi.org/10.1007/s12369-019-00607-xThis paper presents a contribution to the active field of robotics research with the aim of supporting the development of social and collaborative skills of children with Autism Spectrum Disorders (ASD). We present a novel experiment where the classical roles are reversed: in this scenario the children are the teachers providing positive or negative reinforcement to the Kaspar robot in order for the robot to learn arbitrary associations between different toy names and the locations where they are positioned. The objective of this work is to develop games which help children with ASD develop collaborative skills and also provide them tangible example to understand that sometimes learning requires several repetitions. To facilitate this game we developed a reinforcement learning algorithm enabling Kaspar to verbally convey its level of uncertainty during the learning process, so as to better inform the children interacting with Kaspar the reasons behind the successes and failures made by the robot. Overall, 30 Typically Developing (TD) children aged between 7 and 8 (19 girls, 11 boys) and 6 children with ASD performed 22 sessions (16 for TD; 6 for ASD) of the experiment in groups, and managed to teach Kaspar all associations in 2 to 7 trials. During the course of study Kaspar only made rare unexpected associations (2 perseverative errors and 1 win-shift, within a total of 272 trials), primarily due to exploratory choices, and eventually reached minimal uncertainty. Thus the robot's behavior was clear and consistent for the children, who all expressed enthusiasm in the experiment.Peer reviewe

Learning Dimensions: Lessons from Field Studies

In this paper, we describe work to investigate the creation of engaging programming learning experiences. Background research informed the design of four fieldwork studies involving a range of age groups to explore how programming tasks could best be framed to motivate learners. Our empirical findings from these four studies, described here, contributed to the design of a set of programming "Learning Dimensions" (LDs). The LDs provide educators with insights to support key design decisions for the creation of engaging programming learning experiences. This paper describes the background to the identification of these LDs and how they could address the design and delivery of highly engaging programming learning tasks. A web application has been authored to support educators in the application of the LDs to their lesson design

The perception of emotion in artificial agents

Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

Designing Engaging Learning Experiences in Programming

In this paper we describe work to investigate the creation of engaging programming learning experiences. Background research informed the design of four fieldwork studies to explore how programming tasks could be framed to motivate learners. Our empirical findings from these four field studies are summarized here, with a particular focus upon one – Whack a Mole – which compared the use of a physical interface with the use of a screen-based equivalent interface to obtain insights into what made for an engaging learning experience. Emotions reported by two sets of participant undergraduate students were analyzed, identifying the links between the emotions experienced during programming and their origin. Evidence was collected of the very positive emotions experienced by learners programming with a physical interface (Arduino) in comparison with a similar program developed using a screen-based equivalent interface. A follow-up study provided further evidence of the motivation of personalized design of programming tangible physical artefacts. Collating all the evidence led to the design of a set of ‘Learning Dimensions’ which may provide educators with insights to support key design decisions for the creation of engaging programming learning experiences

Physical extracurricular activities in educational child-robot interaction

In an exploratory study on educational child-robot interaction we investigate the effect of alternating a learning activity with an additional shared activity. Our aim is to enhance and enrich the relationship between child and robot by introducing "physical extracurricular activities". This enriched relationship might ultimately influence the way the child and robot interact with the learning material. We use qualitative measurement techniques to evaluate the effect of the additional activity on the child-robot relationship. We also explore how these metrics can be integrated in a highly exploratory cumulative score for the relationship between child and robot. This cumulative score suggests a difference in the overall child-robot relationship between children who engage in a physical extracurricular activity with the robot, and children who only engage in the learning activity with the robot.Comment: 5th International Symposium on New Frontiers in Human-Robot Interaction 2016 (arXiv:1602.05456

Adapting robot task planning to user preferences: an assistive shoe dressing example

The final publication is available at link.springer.comHealthcare robots will be the next big advance in humans’ domestic welfare, with robots able to assist elderly people and users with disabilities. However, each user has his/her own preferences, needs and abilities. Therefore, robotic assistants will need to adapt to them, behaving accordingly. Towards this goal, we propose a method to perform behavior adaptation to the user preferences, using symbolic task planning. A user model is built from the user’s answers to simple questions with a fuzzy inference system, and it is then integrated into the planning domain. We describe an adaptation method based on both the user satisfaction and the execution outcome, depending on which penalizations are applied to the planner’s rules. We demonstrate the application of the adaptation method in a simple shoe-fitting scenario, with experiments performed in a simulated user environment. The results show quick behavior adaptation, even when the user behavior changes, as well as robustness to wrong inference of the initial user model. Finally, some insights in a non-simulated world shoe-fitting setup are also provided.Peer ReviewedPostprint (author's final draft