2018-00413 - Post-doctoral - Unsupervised learning with deep nets for intrinsically motivated exploration of dynamical systems
ثبت نشده
چکیده
A propos du centre ou de la direction fonctionnelle The Flowers team studies computational mechanisms allowing robots and humans to acquire open-ended 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.
منابع مشابه
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 ...
متن کامل2018-00413 - 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 ...
متن کامل2018-00413 - 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 ...
متن کاملUnsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces. However, they have so far assumed that self-generated goals are sampl...
متن کاملIntrinsically Motivated Goal Exploration
Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces. However, they have so far assumed that self-generated goals are sampl...
متن کامل