Biblio

In view of the problem that the convolution neural network research of facial expression recognition ignores the internal relevance of the key links, which leads to the low accuracy and speed of facial expression recognition, and can't meet the recognition requirements, a series cascade algorithm model for expression recognition of educational robot is constructed and enables the educational robot to recognize multiple students' facial expressions simultaneously, quickly and accurately in the process of movement, in the balance of the accuracy, rapidity and stability of the algorithm, based on the cascade convolution neural network model. Through the CK+ and Oulu-CASIA expression recognition database, the expression recognition experiments of this algorithm are compared with the commonly used STM-ExpLet and FN2EN cascade network algorithms. The results show that the accuracy of the expression recognition method is more than 90%. Compared with the other two commonly used cascade convolution neural network methods, the accuracy of expression recognition is significantly improved.

Humanoid robots, with a focus on personalised social behaviours, are increasingly being deployed in educational settings to support learning. However, crafting pedagogical HRI designs and robot interventions that have a real, positive impact on participants' learning, as well as effectively measuring such impact, is still an open challenge. As a first effort in tackling the issue, in this paper we propose a novel robot-mediated, collaborative problem solving activity for school children, called JUSThink, aiming at improving their computational thinking skills. JUSThink will serve as a baseline and reference for investigating how the robot's behaviour can influence the engagement of the children with the activity, as well as their collaboration and mutual understanding while working on it. To this end, this first iteration aims at investigating (i) participants' engagement with the activity (Intrinsic Motivation Inventory-IMI), their mutual understanding (IMIlike) and perception of the robot (Godspeed Questionnaire); (ii) participants' performance during the activity, using several performance and learning metrics. We carried out an extensive user-study in two international schools in Switzerland, in which around 100 children participated in pairs in one-hour long interactions with the activity. Surprisingly, we observe that while a teams' performance significantly affects how team members evaluate their competence, mutual understanding and task engagement, it does not affect their perception of the robot and its helpfulness, a fact which highlights the need for baseline studies and multi-dimensional evaluation metrics when assessing the impact of robots in educational activities.

The purpose of this study was to propose a model of development of trust in social robots. Insights in interpersonal trust were adopted from social psychology and a novel model was proposed. In addition, this study aimed to investigate the relationship among trust development and self-esteem. To validate the proposed model, an experiment using a communication robot NAO was conducted and changes in categories of trust as well as self-esteem were measured. Results showed that general and category trust have been developed in the early phase. Self-esteem is also increased along the interactions with the robot.