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  An iterative path integral stochastic optimal control approach for learning robotic tasks

Theodorou, E., Stulp, F., Buchli, J., & Schaal, S. (2011). An iterative path integral stochastic optimal control approach for learning robotic tasks. In Proceedings of the 18th IFAC World Congress (pp. 11594-11601). International Federation of Automatic Control.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-4EDF-3 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-4EE0-E
Genre: Conference Paper

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 Creators:
Theodorou, E.1, Author
Stulp, F.1, Author
Buchli, J.1, Author
Schaal, S.2, Author           
Affiliations:
1Computational Learning and Motor Control Lab, University of Southern California, USA; Department of Advanced Robotics, Italian Institute of Technology; ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan, ou_persistent22              
2Dept. Autonomous Motion, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497646              

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Free keywords: MPI für Intelligente Systeme; Abt. Schaal;
 Abstract: -

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Language(s): eng - English
 Dates: 2011
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 590806
DOI: 10.3182/20110828-6-IT-1002.02249
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Title: 18th IFAC World Congress
Place of Event: Milano, Italy
Start-/End Date: 2011-08-28 - 2011-09-02

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Title: Proceedings of the 18th IFAC World Congress
Source Genre: Proceedings
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Publ. Info: International Federation of Automatic Control
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 11594 - 11601 Identifier: -