The foundation of efficient robot learning
The past 10 years have seen enormous breakthroughs in machine learning, resulting in game-changing applications in computer vision and language processing. The field of intelligent robotics, which aspires to construct robots that can perform a broad range of tasks in a variety of environments with g...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/130244 |
| _version_ | 1826208479020318720 |
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| author | Kaelbling, Leslie P |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Kaelbling, Leslie P |
| author_sort | Kaelbling, Leslie P |
| collection | MIT |
| description | The past 10 years have seen enormous breakthroughs in machine learning, resulting in game-changing applications in computer vision and language processing. The field of intelligent robotics, which aspires to construct robots that can perform a broad range of tasks in a variety of environments with general human-level intelligence, has not yet been revolutionized by these breakthroughs. A critical difficulty is that the necessary learning depends on data that can only come from acting in a variety of real-world environments. Such data are costly to acquire because there is enormous variability in the situations a general-purpose robot must cope with. It will take a combination of new algorithmic techniques, inspiration from natural systems, and multiple levels of machine learning to revolutionize robotics with general-purpose intelligence. |
| first_indexed | 2024-09-23T14:06:19Z |
| format | Article |
| id | mit-1721.1/130244 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:06:19Z |
| publishDate | 2021 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | dspace |
| spelling | mit-1721.1/1302442022-09-28T18:28:34Z The foundation of efficient robot learning Kaelbling, Leslie P Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Center for Brains, Minds, and Machines The past 10 years have seen enormous breakthroughs in machine learning, resulting in game-changing applications in computer vision and language processing. The field of intelligent robotics, which aspires to construct robots that can perform a broad range of tasks in a variety of environments with general human-level intelligence, has not yet been revolutionized by these breakthroughs. A critical difficulty is that the necessary learning depends on data that can only come from acting in a variety of real-world environments. Such data are costly to acquire because there is enormous variability in the situations a general-purpose robot must cope with. It will take a combination of new algorithmic techniques, inspiration from natural systems, and multiple levels of machine learning to revolutionize robotics with general-purpose intelligence. 2021-03-25T22:41:17Z 2021-03-25T22:41:17Z 2020-08 2021-03-24T14:59:33Z Article http://purl.org/eprint/type/JournalArticle 0036-8075 1095-9203 https://hdl.handle.net/1721.1/130244 Kaelbling, Leslie Pack et al. "The foundation of efficient robot learning." Science 369, 6506 (August 2020): 915-916. © 2020 The Author en http://dx.doi.org/10.1126/science.aaz7597 Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Association for the Advancement of Science (AAAS) Prof. Kaelbling via Phoebe Ayers |
| spellingShingle | Kaelbling, Leslie P The foundation of efficient robot learning |
| title | The foundation of efficient robot learning |
| title_full | The foundation of efficient robot learning |
| title_fullStr | The foundation of efficient robot learning |
| title_full_unstemmed | The foundation of efficient robot learning |
| title_short | The foundation of efficient robot learning |
| title_sort | foundation of efficient robot learning |
| url | https://hdl.handle.net/1721.1/130244 |
| work_keys_str_mv | AT kaelblinglesliep thefoundationofefficientrobotlearning AT kaelblinglesliep foundationofefficientrobotlearning |