Robot navigation using a sparse distributed memory
Despite all the progress that has been made in Robotics and Artificial Intelligence, traditional approaches seem unsuitable to build truly intelligent robots, exhibiting human-like behaviours. Many authors agree that the source of intelligence is, to a great extent, the use of a huge memory, where s...
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| Veröffentlicht in: | 2008 IEEE International Conference on Robotics and Automation S. 53 - 58 |
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| Format: | Tagungsbericht |
| Sprache: | Englisch |
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IEEE
01.05.2008
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| ISBN: | 1424416469, 9781424416462 |
| ISSN: | 1050-4729 |
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| Abstract | Despite all the progress that has been made in Robotics and Artificial Intelligence, traditional approaches seem unsuitable to build truly intelligent robots, exhibiting human-like behaviours. Many authors agree that the source of intelligence is, to a great extent, the use of a huge memory, where sequences of events that guide our later behaviour are stored. Inspired by that idea, our approach is to navigate a robot using sequences of images stored in a Sparse Distributed Memory-a kind of associative memory based on the properties of high dimensional binary spaces, which, in theory, exhibits some human-like behaviours. The robot showed good ability to correctly follow most of the sequences learnt, with small errors and good immunity to the kidnapped robot problem. |
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| AbstractList | Despite all the progress that has been made in Robotics and Artificial Intelligence, traditional approaches seem unsuitable to build truly intelligent robots, exhibiting human-like behaviours. Many authors agree that the source of intelligence is, to a great extent, the use of a huge memory, where sequences of events that guide our later behaviour are stored. Inspired by that idea, our approach is to navigate a robot using sequences of images stored in a Sparse Distributed Memory-a kind of associative memory based on the properties of high dimensional binary spaces, which, in theory, exhibits some human-like behaviours. The robot showed good ability to correctly follow most of the sequences learnt, with small errors and good immunity to the kidnapped robot problem. |
| Author | Mendes, M. Crisostomo, M. Coimbra, A.P. |
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| SubjectTerms | Artificial intelligence Brain modeling Humanoid robots Humans Image retrieval Intelligent robots Navigation Orbital robotics Predictive models robot navigation Robotics and automation sparse distributed memory view sequence |
| Title | Robot navigation using a sparse distributed memory |
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