抄録
Language is a symbolic system unique to human being. Human understands a real world through this symbolic system, makes inference, and communicates with others. Language underlies human intelligence. This paper describes a novel approach to combining motion primitives with words, and extracting relations among the words based on the motion primitives. The motion primitives are symbolized by Hidden Markov Models (HMMs). The HMM is hereafter referred to as ``motion symbol". Observation of human whole body motions is automatically segmented into motion primitives, recognized by using the motion symbols and converted to a sequence of the motion symbols. A sequence of words is also manually assigned to the observation. The association between motion symbols and words can be derived from pairs of these sequences as probability parameters, and dissimilarities among the words can be extracted. Words are located in a multidimensional space so that distances between the words in the space can become as close as possible to the dissimilarities. Thus, ``language space" is formed. The mapping of motion primitives onto the language space enables robots to understand human behaviors as words. The association between motion symbols and words can be also applied to generation of motion primitives from words. The validities of our proposed methods are demonstrated on a motion capture dataset.
