The role of prediction in structured learning of partner robots

Abstract

This paper discusses the role of prediction in the structured learning for the prediction-based perceptual system of partner robots. The perceptual system for a partner robot must perform many functions with many parameters for extracting necessary perceptual information from the viewpoint of embodiment. The robot requires the learnability and adaptability to regulate these parameters by itself, because the parameters cannot be pre-defined and fixed in communication with human. Predictive capability is also required to the robot. The robot can use each function in the perceptual system by reflecting the prediction result efficiently. Therefore we propose the prediction-based perceptual system. Each function in the proposed system enhances the learning of other functions by regulating parameters based on the concept of structured learning. Finally, we show experimental results on the interaction with a human to discuss the effectiveness of our proposed method.