Abstract
We propose a novel neural architecture named PerAc which is a systematic way to decompose the control of an autonomous robot in perception and action flows. We first present an application of the PerAc architecture to the simulation of a vision system with a moving eye. Then we propose a second application where the robot learns to return from any starting place to a previously discovered and learned position without any a priori symbolic representation.
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