身体運動と言語を結ぶロボットの統計的情報処理

Abstract

This paper describes a novel approach to linguistic mutual inference, which enables robots not only to linguistically interpret the motion patterns as sentences but also to recall the motions from the sentences. The inference can be established based on the motion language model and the natural language model. The motion language model stochastically connects the symbols of motion patterns to the words through the latent states which represent the underlying linguistic structure. The natural language model represents sequences of words. The motion language model and the natural language model correspond to semantics and syntax respectively. The integration of the motion language model and the natural language model allows the linguistic mutual inference for the robots . The two kinds of inference can be made by solving search problems: search for a sequence of words and a symbol of motion pattern. The proposed approach to interpretation of motion patterns as sentences and recall of motion patterns from the sentences through integration of the motion language model and the natural language model is validated by the experiment on the human behavioral data.