Interdisciplinary approaches to robot learning:
Robots are being used in increasingly complicated and demanding tasks, often in environments that are complex or even hostile. Underwater, space and volcano exploration are just some of the activities that robots are taking part in, mainly because the environments that are being explored are dangero...
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific Pub. Co.
c2000
|
| Schriftenreihe: | World Scientific series in robotics and intelligent systems
vol. 24 |
| Schlagworte: | |
| Online-Zugang: | FHN01 URL des Erstveroeffentlichers |
| Zusammenfassung: | Robots are being used in increasingly complicated and demanding tasks, often in environments that are complex or even hostile. Underwater, space and volcano exploration are just some of the activities that robots are taking part in, mainly because the environments that are being explored are dangerous for humans. Robots can also inhabit dynamic environments, for example to operate among humans, not just in factories, but also taking on more active roles. Recently, for instance, they have made their way into the home entertainment market. Given the variety of situations that robots will be placed in, learning becomes increasingly important. Robot learning is essentially about equipping robots with the capacity to improve their behaviour over time, based on their incoming experiences. The papers in this volume present a variety of techniques. Each paper provides a mini-introduction to a subfield of robot learning. Some also give a fine introduction to the field of robot learning as a whole. There is one unifying aspect to the work reported in the book, namely its interdisciplinary nature, especially in the combination of robotics, computer science and biology. This approach has two important benefits: first, the study of learning in biological systems can provide robot learning scientists and engineers with valuable insights into learning mechanisms of proven functionality and versatility; second, computational models of learning in biological systems, and their implementation in simulated agents and robots, can provide researchers of biological systems with a powerful platform for the development and testing of learning theories |
| Beschreibung: | ix, 208 p. ill |
| ISBN: | 9789812792747 |
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| 520 | |a Robots are being used in increasingly complicated and demanding tasks, often in environments that are complex or even hostile. Underwater, space and volcano exploration are just some of the activities that robots are taking part in, mainly because the environments that are being explored are dangerous for humans. Robots can also inhabit dynamic environments, for example to operate among humans, not just in factories, but also taking on more active roles. Recently, for instance, they have made their way into the home entertainment market. Given the variety of situations that robots will be placed in, learning becomes increasingly important. Robot learning is essentially about equipping robots with the capacity to improve their behaviour over time, based on their incoming experiences. The papers in this volume present a variety of techniques. Each paper provides a mini-introduction to a subfield of robot learning. Some also give a fine introduction to the field of robot learning as a whole. There is one unifying aspect to the work reported in the book, namely its interdisciplinary nature, especially in the combination of robotics, computer science and biology. This approach has two important benefits: first, the study of learning in biological systems can provide robot learning scientists and engineers with valuable insights into learning mechanisms of proven functionality and versatility; second, computational models of learning in biological systems, and their implementation in simulated agents and robots, can provide researchers of biological systems with a powerful platform for the development and testing of learning theories | ||
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| id | DE-604.BV044635500 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:57:47Z |
| institution | BVB |
| isbn | 9789812792747 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030033472 |
| oclc_num | 1012706503 |
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| physical | ix, 208 p. ill |
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| series2 | World Scientific series in robotics and intelligent systems |
| spelling | Interdisciplinary approaches to robot learning editors, J. Demiris, A Birk Singapore World Scientific Pub. Co. c2000 ix, 208 p. ill txt rdacontent c rdamedia cr rdacarrier World Scientific series in robotics and intelligent systems vol. 24 Robots are being used in increasingly complicated and demanding tasks, often in environments that are complex or even hostile. Underwater, space and volcano exploration are just some of the activities that robots are taking part in, mainly because the environments that are being explored are dangerous for humans. Robots can also inhabit dynamic environments, for example to operate among humans, not just in factories, but also taking on more active roles. Recently, for instance, they have made their way into the home entertainment market. Given the variety of situations that robots will be placed in, learning becomes increasingly important. Robot learning is essentially about equipping robots with the capacity to improve their behaviour over time, based on their incoming experiences. The papers in this volume present a variety of techniques. Each paper provides a mini-introduction to a subfield of robot learning. Some also give a fine introduction to the field of robot learning as a whole. There is one unifying aspect to the work reported in the book, namely its interdisciplinary nature, especially in the combination of robotics, computer science and biology. This approach has two important benefits: first, the study of learning in biological systems can provide robot learning scientists and engineers with valuable insights into learning mechanisms of proven functionality and versatility; second, computational models of learning in biological systems, and their implementation in simulated agents and robots, can provide researchers of biological systems with a powerful platform for the development and testing of learning theories Robots / Control systems Machine learning Demiris, John 1969- Sonstige oth Birk, Andreas 1969- Sonstige oth Erscheint auch als Druck-Ausgabe 9789810243203 Erscheint auch als Druck-Ausgabe 9810243200 http://www.worldscientific.com/worldscibooks/10.1142/4436#t=toc Verlag URL des Erstveroeffentlichers Volltext |
| spellingShingle | Interdisciplinary approaches to robot learning Robots / Control systems Machine learning |
| title | Interdisciplinary approaches to robot learning |
| title_auth | Interdisciplinary approaches to robot learning |
| title_exact_search | Interdisciplinary approaches to robot learning |
| title_full | Interdisciplinary approaches to robot learning editors, J. Demiris, A Birk |
| title_fullStr | Interdisciplinary approaches to robot learning editors, J. Demiris, A Birk |
| title_full_unstemmed | Interdisciplinary approaches to robot learning editors, J. Demiris, A Birk |
| title_short | Interdisciplinary approaches to robot learning |
| title_sort | interdisciplinary approaches to robot learning |
| topic | Robots / Control systems Machine learning |
| topic_facet | Robots / Control systems Machine learning |
| url | http://www.worldscientific.com/worldscibooks/10.1142/4436#t=toc |
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