Imitation System for Humanoid Robotics Heads

This paper presents a new system for recognition and imitating of a set of facial expressions using the visual information acquired by the robot. Besides, the proposed system detects and imitates the interlocutor head’s pose and motion. The approach described in this paper is used for human-robot interaction (HRI), and it consists of two consecutive stages: i) a visual analysis of the human facial expression in order to estimate the interlocutor’s emotional state (i.e., happiness, sadness, anger, fear, neutral) using a Bayesian approach, which is achieved in real time; and ii) an estimate of the user’s head pose and motion. This information updates the knowledge of the robot about the people in its field of view, and thus, allows the robot to use it for future actions and interactions. In this paper, both, human facial expression and head motion, are imitated by Muecas, a 12 degree of freedom (DOF) robotics Head. This paper also introduces the concept of the human and robot facial expression models, which are included inside of a new cognitive module that builds and updates selective representations of the robot and the agents in its environment for enhancing future HRI. Experimental results demonstrate the quality of the detection and imitation using different scenarios with Muecas.