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Our new #Corl18 paper:
How curiosity-driven autonomous goal setting enables to discover
independantly controllable features of environments
pdf: arxiv.org/abs/1807.01521
Blog: openlab-flowers.inria.fr/t/discovery-of…
Colab: colab.research.google.com/drive/176q8pns…
#machinelearning #AI #NeuralNetworks
How curiosity-driven autonomous goal setting enables to discover
independantly controllable features of environments
pdf: arxiv.org/abs/1807.01521
Blog: openlab-flowers.inria.fr/t/discovery-of…
Colab: colab.research.google.com/drive/176q8pns…
#machinelearning #AI #NeuralNetworks
Imagine a robot perceiving a scene through low-level pixels.
It has no prior knowledge of its body (it only knows it can send numerical
numbers as motor commands that produce unknown movements of unknow body parts)
It has no prior knowledge of its body (it only knows it can send numerical
numbers as motor commands that produce unknown movements of unknow body parts)
It also does not know that the scene is composed of "objects". Yet, around it there are several objects: some of them can be controlled, others not (distractors)
How could it discover and represent entities, and find out which ones are controllable (and learn to control them)?
How could it discover and represent entities, and find out which ones are controllable (and learn to control them)?
