Learning High-Level Manipulative Tasks through Imitation

This paper presents ConSCIS, conceptual space based cognitive imitation system, which tightly links low-level data processing with knowledge representation in the context of robot imitation. Our focus is on the program-level imitation: we are interested in the final effects of actions on objects, and not on the particular kinematic or dynamic properties of the motion. The same architecture is used both to analyze and represent the task to be imitated, and to perform the imitation by generalizing in novel and different circumstances. The implemented experimental scenario is a two dimensional world populated with various objects in which observation/imitation takes place. To validate our approach, we report some results concerned with the problem of teaching a humanoid hand/arm robotic system tasks of assembling different workspace objects