End-to-End Learning of Social Behaviors for Humanoid Robots

Abstract

Social robots should understand the user's nonverbal behavior and respond appropriately. Machine learning is one way of implementing the social intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. This paper proposes an end-to-end machine learning method to learn social behaviors for humanoid robots. We adapt sequence-to-sequence architecture consisting of two long short-term memory (LSTM) units. One is an LSTM encoder for encoding the previous sequence of human poses, and the other is an LSTM decoder for generating the next sequence of robot poses. The weights of the LSTMs are trained using human-human interaction data such as greeting and handshaking. The trained model is implemented in a humanoid robot, Pepper, to show its feasibility. Experimental results show that the robot can generate gestures appropriate to the situation and recognize subtle differences in user behavior. In addition, when a user's behavior changes, the transition to another behavior occurs naturally.