Robot learning [TC Spotlight]
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 adapt...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/60229 https://orcid.org/0000-0002-8712-7092 https://orcid.org/0000-0002-8293-0492 |
| _version_ | 1826207715292086272 |
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| author | Tedrake, Russell Louis Roy, Nicholas Peters, Jan Morimoto, Jun |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Tedrake, Russell Louis Roy, Nicholas Peters, Jan Morimoto, Jun |
| author_sort | Tedrake, Russell Louis |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:53:50Z |
| format | Article |
| id | mit-1721.1/60229 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:53:50Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/602292022-10-01T17:50:15Z Robot learning [TC Spotlight] Tedrake, Russell Louis Roy, Nicholas Peters, Jan Morimoto, Jun Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Tedrake, Russell Louis Tedrake, Russell Louis Roy, Nicholas 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. 2010-12-08T18:16:30Z 2010-12-08T18:16:30Z 2009-09 2009-09 Article http://purl.org/eprint/type/JournalArticle 1070-9932 INSPEC Accession Number: 10864241 http://hdl.handle.net/1721.1/60229 Peters, J. et al. “Robot learning [TC Spotlight].” Robotics & Automation Magazine, IEEE 16.3 (2009): 19-20. © 2009 IEEE. https://orcid.org/0000-0002-8712-7092 https://orcid.org/0000-0002-8293-0492 en_US http://dx.doi.org/10.1109/MRA.2009.933618 IEEE Robotics & Automation Magazine Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Tedrake, Russell Louis Roy, Nicholas Peters, Jan Morimoto, Jun Robot learning [TC Spotlight] |
| title | Robot learning [TC Spotlight] |
| title_full | Robot learning [TC Spotlight] |
| title_fullStr | Robot learning [TC Spotlight] |
| title_full_unstemmed | Robot learning [TC Spotlight] |
| title_short | Robot learning [TC Spotlight] |
| title_sort | robot learning tc spotlight |
| url | http://hdl.handle.net/1721.1/60229 https://orcid.org/0000-0002-8712-7092 https://orcid.org/0000-0002-8293-0492 |
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