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

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 curiosity-driven active learning), autonomous unsupervised exploration, imitation and social learning, multimodal statistical inference, embodiment and maturation and self-organization.