Constructive Learning for Human-Robot Interaction

摘要

This article focuses on maintaining a positive learning rate of a student being taught in a classroom using facial expression recognition and tree augmented naive (TAN) Bayes classifier on a biped robot platform. A TAN Bayes classifier is used to recognize the affective emotional states. Once the emotional state of the student is recognized, his/her learning rate is observed using the theory of constructive learning. The tutor continuously aims for maintaining a healthy learning rate. The actions performed by the tutor in order to maintain a healthy learning rate are mimicked by the wireless biped robot using TAN and facial expression recognition. It is envisaged that in the future, robots and not human teachers will be tutoring students. To this end, the proposed system incorporates a biped robot to mimick the human teacher's action. The biped robot will thus help understand better human-robot interaction and its effects on the learning process of students.