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  Robot Learning

Peters, J., Morimoto, J., Tedrake, R., & Roy, N. (2009). Robot Learning. IEEE Robotics and Automation Magazine, 16(3), 19-20. doi:10.1109/MRA.2009.933618.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C2F0-A Version Permalink: http://hdl.handle.net/21.11116/0000-0002-BE36-C
Genre: Journal Article

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 Creators:
Peters, J1, 2, Author              
Morimoto, J, Author
Tedrake, R, Author
Roy, N, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Creating autonomous robots that can learn to act in unpredictable environments has been a long-standing goal of robotics, artificial intelligence, and the cognitive sciences. In contrast, current commercially available industrial and service robots mostly execute fixed tasks and exhibit little adaptability. To bridge this gap, machine learning offers a myriad set of methods, some of which have already been applied with great success to robotics problems. As a result, there is an increasing interest in machine learning and statistics within the robotics community. At the same time, there has been a growth in the learning community in using robots as motivating applications for new algorithms and formalisms. Considerable evidence of this exists in the use of learning in high-profile competitions such as RoboCup and the Defense Advanced Research Projects Agency (DARPA) challenges, and the growing number of research programs funded by governments around the world.

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 Dates: 2009-09
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1109/MRA.2009.933618
BibTex Citekey: 5905
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Title: IEEE Robotics and Automation Magazine
Source Genre: Journal
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Publ. Info: -
Pages: - Volume / Issue: 16 (3) Sequence Number: - Start / End Page: 19 - 20 Identifier: -