Probabilistic Neural Network Based Learning from Fuzzy Voice Commands for Controlling a Robot

Study of human-robot communication is one of the most important research areas. Among various communication media, any useful law we find in voice communication in human-human interactions, is significant in human-robot interactions too. Control strategy of most of such systems available at present is on/off control. These robots activate a function if particular word or phrase associated with that function can be recognized in the user utterance. Recently, there have been some researches on controlling robots using information rich fuzzy commands such as "go little slowly". However, in those works, although the voice command interpretation has been considered, learning from such commands has not been treated. In this paper, learning from such information rich voice commands for controlling a robot is studied. New concepts of the coach-player model and the sub-coach are proposed and such concepts are also demonstrated for a PA-10 redundant manipulator.