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Online Kernel-based Learning for Task-Space Tracking Robot Control

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84108

Nguyen-Tuong,  D
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Peters,  J
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Nguyen-Tuong, D., & Peters, J. (2012). Online Kernel-based Learning for Task-Space Tracking Robot Control. IEEE Transactions on Neural Networks and Learning Systems, 23(9), 1417-1425. doi:10.1109/TNNLS.2012.2201261.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-FDB5-D
Abstract
{Abstract—Task-space control of redundant robot systems based on analytical models is known to be susceptive to modeling errors. Here, data driven model learning methods may present an interesting alternative approach. However, learning models for task-space tracking control from sampled data is an illposed problem. In particular, the same input data point can yield many different output values, which can form a non-convex solution space. Because the problem is ill-posed, models cannot be learned from such data using common regression methods. While learning of task-space control mappings is globally illposed, it has been shown in recent work that it is locally a well-defined problem. In this paper, we use this insight to formulate a local, kernel-based learning approach for online model learning for task-space tracking control. We propose a parametrization for the local model which makes an application in task-space tracking control of redundant robots possible. The model parametrization further allows us to apply the kerneltrick and, therefore, enables a formulation within the kernel learning framework. For evaluations, we show the ability of the method for online model learning for task-space tracking control of redundant robots.}