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  Learning inverse kinematics with structured prediction

Bocsi, B., Nguyen-Tuong, D., Csato, L., Schölkopf, B., & Peters, J. (2011). Learning inverse kinematics with structured prediction. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011) (pp. 698-703). Piscataway, NJ, USA: IEEE.

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 Creators:
Bocsi, B1, Author           
Nguyen-Tuong, D1, Author           
Csato, L1, 2, Author           
Schölkopf, B1, Author           
Peters, J1, 3, Author           
Amato, N.M., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647              
3Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: Learning inverse kinematics of robots with redundant degrees of freedom (DoF) is a difficult problem in robot learning. The difficulty lies in the non-uniqueness of the inverse kinematics function. Existing methods tackle non-uniqueness by segmenting the configuration space and building a global solution from local experts. The usage of local experts implies the definition of an oracle, which governs the global consistency of the local models; the definition of this oracle is difficult. We propose an algorithm suitable to learn the inverse kinematics function in a single global model despite its multivalued nature. Inverse kinematics is approximated from examples using structured output learning methods. Unlike most of the existing methods, which estimate inverse kinematics on velocity level, we address the learning of the direct function on position level. This problem is a significantly harder. To support the proposed method, we conducted real world experiments on a tracking control task and tested our algorithms on these models.

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 Dates: 2011-09
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: ISBN: 978-1-61284-454-1
URI: http://www.iros2011.org/
DOI: 10.1109/IROS.2011.6094666
BibTex Citekey: BocsiNCSP2011
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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
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 698 - 703 Identifier: -