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  Combining learned and analytical models for predicting action effects from sensory data

Kloss, A., Schaal, S., & Bogh, J. (2022). Combining learned and analytical models for predicting action effects from sensory data. The International Journal of Robotics Research, 41(8), 778-797. doi:10.1177/0278364920954896.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000F-DFEC-D Version Permalink: https://hdl.handle.net/21.11116/0000-000F-DFED-C
Genre: Journal Article

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Locator:
https://doi.org/10.1177/0278364920954896 (Publisher version)
Description:
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OA-Status:
Hybrid
Locator:
https://doi.org/10.48550/arXiv.1710.04102 (Preprint)
Description:
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OA-Status:
Green

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 Creators:
Kloss, Alina1, Author           
Schaal, Stefan1, 2, Author           
Bogh, Jeannette 2, Author
Affiliations:
1Dept. Autonomous Motion, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497646              
2External Organizations, ou_persistent22              

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Free keywords: Abt. Schaal
 Abstract: -

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Language(s): eng - English
 Dates: 2020-09-122022-07
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: BibTex Citekey: kloss_ijrr
DOI: 10.1177/0278364920954896
arXiv: 1710.04102
 Degree: -

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Title: The International Journal of Robotics Research
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge, MA : Sage Publications, Inc.
Pages: - Volume / Issue: 41 (8) Sequence Number: - Start / End Page: 778 - 797 Identifier: ISSN: 0278-3649
CoNE: https://pure.mpg.de/cone/journals/resource/954925506289