Multimodal Fuzzy Assessment for Robot Behavioral Adaptation in Educational Children-Robot Interaction

Abstract

Social robots' contributions to education are notorious but, in times, limited by the difficulty in their programming by regular teachers. Our framework named R-CASTLE aims to overcome this problem by providing the teachers with an easy way to program their content and the robot's behavior through a graphical interface. However, the robot's behavior adaptation algorithm maybe still not the best intuitive method for teachers' understanding. Fuzzy systems have the advantage of being modeled in a more human-like way than other methods due to their implementation based on linguistic variables and terms. Thus, fuzzy modeling for robot behavior adaptation in educational children-robot interactions is proposed for this framework. The modeling resulted in an adaptation algorithm that considers a multimodal and autonomous assessment of the students' skills: attention, communication, and learning. Furthermore, preliminary experiments were performed considering videos with the robot in a school environment. The adaptation was set to change the content approach difficulty to produce a suitably challenging behavior according to each students' reactions. Results were compared to a Rule-Based adaptive method. The fuzzy modeling showed similar accuracy to the ruled-based method with a suggestion of a more intuitive interpretation of the process.