Concept Oriented Imitation Towards Verbal Human-Robot Interaction

Imitation equips robots with a simple and natural interface to learn new tasks. Although abstraction is a remarkable feature of imitation that discriminates it from mimicking, there has been no enough research on this dimension of imitation. Relational concepts are the simplest type of abstract concepts and can be an appropriate start point. These concepts may be learned by combining perceptual categorization and classical conditioning. The paper will first formalize relational concept learning within an imitative context. Internal modules of the learning agent are considered to be functions. We will prove that in this case the concept-motor mapping becomes one-to-one which simplifies learning. A learning algorithm for the model will be also proposed and evaluated in a phoneme acquisition experiment with a large number of highly overlapped samples.