Computational intelligence for structured learning of a partner robot based on imitation

Imitation is a powerful tool for gestural interaction between children and for teaching behaviors to children by parent. Furthermore, others' action can be a hint for acquiring a new behavior that might not be the same as the original action. The importance is how to map or represent others' action into new one in the internal state space. A good instructor can teach an action to a learner by understanding the mapping or imitating method of the learner. This indicates a robot also can acquire various behaviors using interactive learning based on imitation. This paper proposes structured learning for a partner robot based on the interactive teaching mechanism. The proposed method is composed of a spiking neural network, self-organizing map, steady-state genetic algorithm, and softmax action selection. Furthermore, we discuss the interactive learning of a human and a partner robot based on the proposed method through experiment results.