Abstract
In order to explore and act autonomously in an environment, an agent can learn from the sensorimotor information that is captured while acting. By extracting the regularities in this sensorimotor stream, it can build a model of the world, which in turn can be used as a basis for action and exploration. It requires the acquisition of compact representations from possibly high dimensional raw observations. In this paper, we propose a model which integrates sensorimotor information over time, and project it in a sensory representation. It is trained by preforming sensorimotor prediction. We emphasize on a simple example the role of motor and memory for learning sensory representations.
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Kulak, T., Ortiz, M.G. (2018). Emergence of Sensory Representations Using Prediction in Partially Observable Environments. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_47
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