A cognitive approach to goal-level imitation

Imitation in robotics is seen as a powerful means to reduce the complexity of robot programming. It allows users to instruct robots by simply showing them how to execute a given task. Through imitation robots can learn from their environment and adapt to it just as human newborns do. In order to be useful as human companions, robots must act for a purpose by achieving goals and fulfilling human expectations. In this paper we present an architecture for goal-level imitation in robotics where focus is put on final effects of actions on objects. The architecture tightly links low-level data with high-level knowledge, and integrates, in a unified framework, several aspects of imitation, such as perception, learning, knowledge representation, action generation and robot control. Some preliminary experimental results on an anthropomorphic arm/hand robotic system are shown