2018-00413 - Post-doctoral - Unsupervised learning with deep nets for intrinsically motivated exploration of dynamical systems

  • Published 2018

Abstract

The Flowers team studies computational mechanisms allowing robots and humans to acquire openended repertoires of skills through life-long learning. This includes the processes for progressively discovering their bodies and interaction with objects, tools and others. In particular, we study mechanisms of intrinsically motivated learning (also called… (More)

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