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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 propose 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 is 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 variables and words. The connections between the motion symbol and the latent state, and between the latent state and the words is 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 words. 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 natura
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