Hybrid computing using a neural network with dynamic external memory

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Deep Learning in Robotics - There are two major branches in applying deep learning techniques in robotics. - One is to combine DL with Q learning algorithms. For example, awesome work on playing Atari games done by deep mind is a representative study. While this approach can effectively handle several problems that can hardly be solved via traditional methods, these methods are not appropriate for real manipulators as it often requires an enormous number of training data. - The other branch of work uses a concept of guided policy search. It combines trajectory optimization methods with supervised learning algorithm like CNNs to come up with a robust 'policy' function that can actually be used in real robots, e.g., Baxter of PR2.

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