Bigram-based natural language model and statistical motion symbol model for scalable language of humanoid robots

Abstract

The language is a symbolic system unique to human being. The acquisition of language, which has its meanings in the real world, is important for robots to understand the environment and communicate with us in our daily life. This paper proposes a novel approach to establish a fundamental framework for the robots which can understand language through their whole body motions. The proposed framework is composed of three modules: “motion symbol”, “motion language model”, and “natural language model”. In the motion symbol module, motion data are symbolized by Hidden Markov Models (HMMs). Each HMM represents abstract motion patterns. Then the HMMs are defined as motion symbols. The motion language model is stochastically designed for links between motion symbols and words. This model consists of three layers of motion symbols, latent states and words. The connections between the motion symbol and the latent state, and between the latent state and the words are denoted by two kinds of probabilities respectively. One connection is represented by the probability that the motion symbol generates the latent state, and the other connection is represented by the probability that the latent state generates the word. Therefore, the motion language model can connect the motion symbols to the words through the latent state. The natural language model stochastically represents sequences of words. In this paper, a bigram, which is a special case of N-gram model, is adopted as the natural language model. This model has the words as nodes and transitions between two words as edges. Therefore sentence structure is expressed as transitions among words. The integration of the motion language model and natural language model can be implemented by the search computation for sentences corresponding to motions and for motions corresponding to sentences. Especially, the usage of the bigram as the natural language model provides a simple search computation so that appropriate and fast bidirectional computation between the motions and language can be achieved. Our approach makes it possible for humanoid robots not only to interpret motions as sentences but also to generate motions from sentences. The tests by using various motions and words validate our framework for the language acquisition of humanoid robots.