Symbol Grounding from Natural Conversation for Human-Robot Communication

This paper proposes a new approach for research on chat-like conversational systems that enable robots to acquire physically grounded knowledge through natural interaction with humans. The proposed approach combines research on chat-like conversational systems, language acquisition, and symbol grounding in order to realize physically situated and natural human-robot interaction. In contrast to previous approaches for chat-like conversation, the proposed approach focuses on utterances which are situated in physical environments surrounding humans and robots. Based on the proposed approach, we develop a concrete method that enables robots to learn object image concepts and the words describe them from object-teaching utterances made by humans. The method is composed of two processes:(1) the detection of object-teaching utterances from chat-like conversation and (2) the learning of object image concepts and the words describing them. It applies a linear support vector machine, multimodal hierarchical Dirichlet process, and term frequency-inverse document frequency process. The experimental results show that the method enabled robots to learn object image concepts and the words that describe them through multimodal chat-like interactions with humans.