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Learning inverse kinematics with structured prediction


Conference Paper


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.

Author(s): Bocsi, B. and Nguyen-Tuong, D. and Csato, L. and Schölkopf, B. and Peters, J.
Pages: 698-703
Year: 2011
Month: September
Day: 0
Editors: NM Amato
Publisher: IEEE

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Address: Piscataway, NJ, USA
Digital: 0
DOI: 10.1109/IROS.2011.6094666
Event Name: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Event Place: San Francisco, CA, USA
ISBN: 978-1-61284-454-1

Links: PDF


  title = {Learning inverse kinematics with structured prediction },
  author = {Bocsi, B. and Nguyen-Tuong, D. and Csato, L. and Sch{\"o}lkopf, B. and Peters, J.},
  pages = {698-703 },
  editors = {NM Amato},
  publisher = {IEEE},
  address = {Piscataway, NJ, USA},
  month = sep,
  year = {2011},
  month_numeric = {9}