Machine learning for interactive systems and robots: a brief introduction
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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Humans are very good in perceiving all kinds of high-dimensional sensory inputs, extracting the meaningful information and acting on that information to pursue their goals. Having this in mind, our vision is a learning system, that takes raw, potentially high-dimensional sensory inputs (e.g. raw image data), extracts the relevant information, and learns to act by experiencing success or failure. In this talk I will provide some first successful examples along this line of research. In particular, I will discuss neural network based architectures and algorithms that are the basic building blocks of our neural control architecture.