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Session Information

Deep Learning in Movement Modelling and Robotics


Prof. Dr. Patrick van der Smagt (Technische Universität München (TUM) / fortiss)
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A traditional way of representing movement of human or robotic limbs is by solving dynamical models, estimating their parameters, and combining those with the available neuronal or mathematical controllers. Opposing this systemic approach, we venture to represent movement using generative probabilistic models, generated through deep learning. Exploiting deep autoencoders and recurrent neural networks, we can use these to accurately model human or robot movement, based on measured movement data alone. Moreover, these movements can be reconstructed from different types of sensors, which when combined increase accuracy and reduce error. In this talk I will focus on machine-learning methodologies for movement representation and show how their results can be used in robot control, human movement prediction, assistive robotics, and human--machine interfacing.
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Time slot: 
2016-04-20 10:00
Channel 5

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