摘要：In this study, we define concepts as categories into which a robot classifies perceptual information obtained through interaction with others and the environment, and the inference of unobserved information through the concepts is defined as understanding. Furthermore, a robot can infer unobserved perceptual information from words by con- necting concepts and words. This inference is the understanding of word meanings. We propose probabilistic models that enable robots to learn concepts and language. In this paper, we present an overview of the proposed models.
