The authors develop a method of modeling a human manipulative skill using human linguistic knowledge about the task. A global nonlinear structure of human control behavior is constructed based on the linguistic information, and all functionalities used by the linguistic structure are identified from human demonstration data. Mapping between sensor space and human mental space for input signals is discussed to elucidate human skills. Techniques for selecting significant features extracted from sensor signals and reducing the dimension of the sensor space are developed. The techniques were applied to a direct-drive deburring robot to verify the feasibility of the method.
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