Interactive learning for partner robot based on behavior knowledge

This paper deals with action learning for communication of a partner robot interacting with a human. Fuzzy inference rule sets are used as temporal behavior knowledge for the partner robot, while modular neural networks are used for learning action rules through interaction with the human. Consequently, the robot extracts action rules from behavior knowledge. Finally, we discuss the communication of the partner robot with the human through navigation experiments.