Please note that eDoc will be permanently shut down in the first quarter of 2021!      Home News About Us Contact Contributors Disclaimer Privacy Policy Help FAQ

Quick Search
My eDoc
Session History
Support Wiki
Direct access to
document ID:

          Institute: MPI für Intelligente Systeme (ehemals Max-Planck-Institut für Metallforschung)     Collection: Abt. Schölkopf (Empirical Inference)     Display Documents

ID: 596183.0, MPI für Intelligente Systeme (ehemals Max-Planck-Institut für Metallforschung) / Abt. Schölkopf (Empirical Inference)
Learning inverse kinematics with structured prediction
Authors:Bocsi, B.; Nguyen-Tuong, D.; Csato, L.; Schölkopf, B.; Peters, J.
Place of Publication:San Francisco, CA, USA
Date of Publication (YYYY-MM-DD):2011-09-01
Title of Proceedings:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Start Page:698
End Page:703
Physical Description:5
Review Status:not specified
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Heide Klooz
Affiliations:MPI für Intelligente Systeme/Abt. Schölkopf
The scope and number of records on eDoc is subject to the collection policies defined by each institute - see "info" button in the collection browse view.