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Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals
Continuous online adaptation is an essential ability for the vision of fully autonomous and lifelong-learning robots. Robots need to be able to adapt to changing environments and constraints while this adaption should be performed without interrupting the robot's motion. In this paper, we introduce a framework for probabilistic online motion planning and learning based on a bio-inspired stochastic recurrent neural network. Furthermore, we show that the model can adapt online anddoi:10.26083/tuprints-00020580 fatcat:xdbo4vkmhbe6zdipnapxyas7re