Botond Attila Bocsi

ei Bocsi, B., Csato, L., Peters, J. Alignment-based Transfer Learning for Robot Models In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN 2013), pages: 1-7, 2013 (inproceedings)

PDF DOI [BibTex]

2011

Learning inverse kinematics with structured prediction

Bocsi, B., Nguyen-Tuong, D., Csato, L., Schölkopf, B., Peters, J.

In pages: 698-703 , (Editors: NM Amato), IEEE, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2011 (inproceedings)

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.

PDF Web DOI [BibTex]

2011

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

PDF Web DOI [BibTex]