Towards a general theory of action and time
Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
The agent-based perspective on imitation
Imitation in animals and artifacts
Imitation: a means to enhance learning of a synthetic protolanguage in autonomous robots
Imitation in animals and artifacts
Perceptual anchoring of symbols for action
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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In order to have a robotic system able to effectively learn by imitation, and not merely reproduce the movements of a human teacher, the system should have the capabilities of deeply understanding the perceived actions to be imitated. This paper deals with the development of a cognitive architecture for learning by imitation in which a rich conceptual representation of the observed actions is built. The purpose of the following discussion is to show how the same conceptual representation can be used both in a bottom-up approach, in order to learn sequences of actions by imitation learning paradigm, and in a top-down approach, in order to anchor the symbolical representations to the perceptual activities of the robotic system. The proposed architecture has been tested on the robotic system composed of a PUMA 200 industrial manipulator and an anthropomorphic robotic hand. The system demonstrated the ability to learn and imitate a set of movement primitives acquired through the vision system for simple manipulative purposes.