Abstract

n order to explore and act autonomously in an environment,an agent can learn from the sensorimotor information that is capturedwhile 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 foraction and exploration. It requires the acquisition of compact representa-tions from possibly high dimensional raw observations. In this paper, wepropose a model which integrates sensorimotor information over time,and project it in a sensory representation. It is trained by preformingsensorimotor prediction. We emphasize on a simple example the role ofmotor and memory for learning sensory representations.

Publication Type:	Conference or Workshop Item (UNSPECIFIED)
Additional Information:	This is the accepted version of a conference paper published in Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science, vol 11140. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01421-6_47.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science

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