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Conference Paper

Learning task-space tracking control with kernels

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

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

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Citation

Nguyen-Tuong, D., & Peters, J. (2011). Learning task-space tracking control with kernels. In N. Amato (Ed.), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011) (pp. 704-709). Piscataway, NJ, USA: IEEE.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BA56-3
Abstract
Task-space tracking control is essential for robot manipulation. In practice, task-space control of redundant robot systems is known to be susceptive to modeling errors. Here, data driven learning methods may present an interesting alternative approach. However, learning models for task-space tracking control from sampled data is an ill-posed 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 ill-posed, 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 taskspace tracking control. For evaluations, we show in simulation the ability of the method for online model learning for task-space tracking control of redundant robots.