Friendly Motion Learning towards Sustainable Human Robot Interaction

摘要

For generating interactive behavior of robot to build a long-term relationship between humans and robots, we focus on the difference in familiarity of the human behaviors during conversation. It is difficult to extract interaction motion features correlated to such familiarity as a model in manual. Therefore, we use a machine learning technique: convolution neural network to learn and generate interaction behavior with different familiarity. In the evaluation experiment, we generated interaction behavior using a convolution neural network, which learned from the behaviors of friendship and unknown relationship, who have high and low familiarity respectively. We evaluated how much such interaction behavior affect the human impression by questionnaire survey.