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:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; River Edge, N.J. :
World Scientific,
©2000.
|
| Schriftenreihe: | World Scientific series in robotics and intelligent systems ;
vol. 24. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| 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: | JM20001121. |
| Beschreibung: | 1 online resource (ix, 208 pages) : illustrations |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9789812792747 9812792740 |
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| 505 | 0 | |a Interdisciplinary approaches to robot learning : introduction / J. Demiris and A. Birk -- Bootstrapping the developmental process : the filter hypothesis / L. Berthouze -- Biomimetic gaze stabilization / T. Shibata and S. Schaal -- Experiments and models about cognitive map learning for motivated navigation / P. Gaussier [and others] -- Learning selection of action for cortically-inspired robot control / H. Frezza-Buet and F. Alexandre -- Transferring learned knowledge in a lifelong learning mobile robot agent / J. O'Sullivan -- Of hummingbirds and helicopters : an algebraic framework for interdisciplinary studies of imitation and its applications / C. Nehaniv and K. Dautenhahn -- Evolving complex visual behaviours using genetic programming and shaping / S. Perkins and G.M. Hayes -- Preston : a system for the evaluation of behaviour sequences / M. Wilson. | |
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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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| contents | Interdisciplinary approaches to robot learning : introduction / J. Demiris and A. Birk -- Bootstrapping the developmental process : the filter hypothesis / L. Berthouze -- Biomimetic gaze stabilization / T. Shibata and S. Schaal -- Experiments and models about cognitive map learning for motivated navigation / P. Gaussier [and others] -- Learning selection of action for cortically-inspired robot control / H. Frezza-Buet and F. Alexandre -- Transferring learned knowledge in a lifelong learning mobile robot agent / J. O'Sullivan -- Of hummingbirds and helicopters : an algebraic framework for interdisciplinary studies of imitation and its applications / C. Nehaniv and K. Dautenhahn -- Evolving complex visual behaviours using genetic programming and shaping / S. Perkins and G.M. Hayes -- Preston : a system for the evaluation of behaviour sequences / M. Wilson. |
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| series2 | World Scientific series in robotics and intelligent systems ; |
| spelling | Interdisciplinary approaches to robot learning / edited by J. Demiris, A Birk. Singapore ; River Edge, N.J. : World Scientific, ©2000. 1 online resource (ix, 208 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier World Scientific series in robotics and intelligent systems ; vol. 24 JM20001121. Interdisciplinary approaches to robot learning : introduction / J. Demiris and A. Birk -- Bootstrapping the developmental process : the filter hypothesis / L. Berthouze -- Biomimetic gaze stabilization / T. Shibata and S. Schaal -- Experiments and models about cognitive map learning for motivated navigation / P. Gaussier [and others] -- Learning selection of action for cortically-inspired robot control / H. Frezza-Buet and F. Alexandre -- Transferring learned knowledge in a lifelong learning mobile robot agent / J. O'Sullivan -- Of hummingbirds and helicopters : an algebraic framework for interdisciplinary studies of imitation and its applications / C. Nehaniv and K. Dautenhahn -- Evolving complex visual behaviours using genetic programming and shaping / S. Perkins and G.M. Hayes -- Preston : a system for the evaluation of behaviour sequences / M. Wilson. Print version record. Includes bibliographical references. 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. http://id.loc.gov/authorities/subjects/sh89001406 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Robots Systèmes de commande. Apprentissage automatique. TECHNOLOGY & ENGINEERING Robotics. bisacsh Machine learning fast Robots Control systems fast Demiris, John, 1969- https://id.oclc.org/worldcat/entity/E39PCjJtBvdyjcxFYCFjRRvcrm http://id.loc.gov/authorities/names/n98110342 Birk, Andreas, 1969- https://id.oclc.org/worldcat/entity/E39PBJmpbpGVT8KvqFCj7qFR8C http://id.loc.gov/authorities/names/n98110340 has work: Interdisciplinary approaches to robot learning (Text) https://id.oclc.org/worldcat/entity/E39PCGBqxKy9MccFmDxBW97gDq https://id.oclc.org/worldcat/ontology/hasWork Print version: Interdisciplinary approaches to robot learning. Singapore ; River Edge, N.J. : World Scientific, ©2000 9810243200 (OCoLC)45408815 World Scientific series in robotics and intelligent systems ; vol. 24. http://id.loc.gov/authorities/names/n95092973 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=514157 Volltext |
| spellingShingle | Interdisciplinary approaches to robot learning / World Scientific series in robotics and intelligent systems ; Interdisciplinary approaches to robot learning : introduction / J. Demiris and A. Birk -- Bootstrapping the developmental process : the filter hypothesis / L. Berthouze -- Biomimetic gaze stabilization / T. Shibata and S. Schaal -- Experiments and models about cognitive map learning for motivated navigation / P. Gaussier [and others] -- Learning selection of action for cortically-inspired robot control / H. Frezza-Buet and F. Alexandre -- Transferring learned knowledge in a lifelong learning mobile robot agent / J. O'Sullivan -- Of hummingbirds and helicopters : an algebraic framework for interdisciplinary studies of imitation and its applications / C. Nehaniv and K. Dautenhahn -- Evolving complex visual behaviours using genetic programming and shaping / S. Perkins and G.M. Hayes -- Preston : a system for the evaluation of behaviour sequences / M. Wilson. Robots Control systems. http://id.loc.gov/authorities/subjects/sh89001406 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Robots Systèmes de commande. Apprentissage automatique. TECHNOLOGY & ENGINEERING Robotics. bisacsh Machine learning fast Robots Control systems fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh89001406 http://id.loc.gov/authorities/subjects/sh85079324 |
| 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 / edited by J. Demiris, A Birk. |
| title_fullStr | Interdisciplinary approaches to robot learning / edited by J. Demiris, A Birk. |
| title_full_unstemmed | Interdisciplinary approaches to robot learning / edited by J. Demiris, A Birk. |
| title_short | Interdisciplinary approaches to robot learning / |
| title_sort | interdisciplinary approaches to robot learning |
| topic | Robots Control systems. http://id.loc.gov/authorities/subjects/sh89001406 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Robots Systèmes de commande. Apprentissage automatique. TECHNOLOGY & ENGINEERING Robotics. bisacsh Machine learning fast Robots Control systems fast |
| topic_facet | Robots Control systems. Machine learning. Robots Systèmes de commande. Apprentissage automatique. TECHNOLOGY & ENGINEERING Robotics. Machine learning Robots Control systems |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=514157 |
| work_keys_str_mv | AT demirisjohn interdisciplinaryapproachestorobotlearning AT birkandreas interdisciplinaryapproachestorobotlearning |