Quantification of Robot Actions Based on Fuzzy-Neural Network in Voice Commanded Coach-Player Systems

抄録

As a robot control system by voice commands, a fuzzy-coach player system is considered to deal with the fuzzy implication of human natural languages. In any voice commands, it can be easily imagined that the quantity (e.g., the movement distance) desired by a user differs depending on the relative relationship (or situation) between the robot and the environment, even if the user reuses the same command as used before. Therefore, in order to reflect the human intention to determining the operational quantity of the robot, a method for determining an action of the robot, which can learn the command features of the human, is here proposed by introducing a reasoning mechanism composed of a fuzzy-neural network, whenever the movement distance of the robot is determined. The effectiveness of the present approach is illustrated through some experiments for a block piling task.