Motion capture systems have been commonly used to enable humanoid robots or CG characters to perform human-like motions. However, prerecorded motion capture data cannot be reused efficiently because picking a specific motion from a large database and modifying the motion data to fit the desired motion patterns are difficult tasks. We have developed an imitative learning framework based on the symbolization of motion patterns using Hidden Markov Models (HMMs), where each HMM (hereafter referred to as “motion symbol”) abstracts the dynamics of a motion pattern and allows motion recognition and generation. This paper describes a symbolically structured motion database that consists of original motion data, motion symbols, and motion words. Each motion data is labeled with motion symbols and motion words. Moreover, a network is formed between two layers of motion symbols and motion words based on their probability association. This network makes it possible to associate motion symbols with motion words and to search for motion datasets using motion symbols. The motion symbols can also generate motion data. Therefore, the developed framework can provide the desired motion data when only the motion words are input into the database. 
Symbolically structured database for human whole body motions based on association between motion symbols and motion words