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  Learning task-space tracking control with kernels

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.

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 Creators:
Nguyen-Tuong, D1, Author              
Peters, J1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 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.

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 Dates: 2011-09
 Publication Status: Published in print
 Pages: -
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 Identifiers: DOI: 10.1109/IROS.2011.6094428
BibTex Citekey: NguyenTuongP2011_3
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Title: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Place of Event: San Francisco, CA, USA
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Title: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Source Genre: Proceedings
 Creator(s):
Amato, NM, Editor
Affiliations:
-
Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 704 - 709 Identifier: ISBN: 978-1-61284-454-1