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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 N. Amato (Ed.), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011) (pp. 698-703). Piscataway, NJ, USA: IEEE.

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 Creators:
Bocsi, B, Author              
Nguyen-Tuong, D1, Author              
Csato, L, Author              
Schölkopf, B1, Author              
Peters, J1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent System, 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: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: 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
 Creator(s):
Amato, NM, Editor
Affiliations:
-
Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 698 - 703 Identifier: ISBN: 978-1-61284-454-1