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 environmentand 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.
|Stato di pubblicazione||Published - 2008|
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