Controlling a robot manipulator with fuzzy voice commands guided by visual motor coordination learning

This paper proposes a method for learning and controlling an industrial robot manipulator through fuzzy voice commands guided by visual motor coordination. The visual motor coordination learning is implemented by a supervised self organizing map (SSOM). Study of human-robot communication is one of the most important research areas. The voice communication is significant in human robot interactions among various communication media. The fuzzy voice commands are used to control the robot and the visual feedback is used to learn the precision control based on visual motor coordination, where it is learned by the supervision of the teacher voice commands. The learned system is capable of positioning the robot manipulator to a point in 3D working space as instructed by the voice command. The proposed idea is demonstrated with a PA-10 industrial manipulator.