An autonomous robot involved in long and complex missions should be able to generate, update and process its own plans of action. In this perspective, it is not plausible for the meaning of the representations used by the robot to be given from outside the system itself. Rather, the meaning of internal symbols must be firmly anchored to the world through the perceptual abilities and the overall activities of the robot. According to these premises, we present an approach to action representation that is based on a "conceptual" level of representation, acting as an intermediate level between the symbols and data coming from the sensors. Symbolic representations are interpreted by mapping them onto the conceptual level through a mapping mechanism based on artificial neural networks. Examples of the proposed framework are reported, based on experiments performed on a RWI-B12 autonomous robot.
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