Abstract. Applications of recurrent neural networks (RNNs) tend to be rare because training is difficult. A recent theoretical breakthrough [Jae01b] called Echo State Networks (ESNs) has made RNN training easy and fast and makes RNNs a versatile tool for many problems. The key idea is training the output weights only of an otherwise topologically unrestricted but contractive network. After outlining the mathematical basics, we apply ESNs to two examples namely to the generation of a dynamical model for a differential drive robot using supervised learning and secondly to the training of a respective motor controller.
In this paper we show how a combination of multiple neuromorphic vision sensors can achieve the same higher level visual processing tasks as carried out by a conventional vision system. We process the multiple neuromorphic sensory signals with a standard auto-regression method in order to fuse the sensory signals and to achieve higher level vision processing tasks at a very high update rate. We also argue why this result is of great relevance for the application domain of reactive and lightweight mobile robotics, at the hands of a soccer robot, where the fastest sensory-motor feedback loop is imperative for a successful participation in a RoboCup soccer competition.
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