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Learning vehicular dynamics models with application to helicopter modeling and control

Learning vehicular dynamics models with application to helicopter modeling and control

This video was recorded at International Workshop on Regression in Robotics - Approaches and Applications, Seattle 2009. Function approximation from noisy data is a central task in robot learning. Relevant problems include sensor modeling, manipulation, control, and many others. A large number of function approximation methods have been proposed from statistics, machine learning, and control system theory to address robotics-related issues such as online updates, active sampling, high dimensionality, non-homogeneous noise, and missing features. In this workshop, we would like to develop a common understanding of the benefits and drawbacks of different function approximation approaches and to derive practical guidelines for selecting a suitable approach to a given problem. In addition, we... Show More

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