Interdisciplinary Approaches To Robot Learning
Saved in:
| Title: | Interdisciplinary Approaches To Robot Learning |
|---|---|
| Description: | 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. |
| Authors: | Andreas Birk, Yiannis Demiris |
| Resource Type: | eBook. |
| Subjects: | Machine learning, Robots--Control systems |
| Categories: | TECHNOLOGY & ENGINEERING / Robotics |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf Text: Availability: 0 |
|---|---|
| Header | DbId: nlebk DbLabel: eBook Collection (EBSCOhost) An: 514157 RelevancyScore: 966 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 965.702392578125 |
| IllustrationInfo | |
| ImageInfo | – Size: thumb Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$514157$PDF&s=r – Size: medium Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$514157$PDF&s=d |
| Items | – Name: Title Label: Title Group: Ti Data: Interdisciplinary Approaches To Robot Learning – Name: Abstract Label: Description Group: Ab Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Andreas+Birk%22">Andreas Birk</searchLink><br /><searchLink fieldCode="AR" term="%22Yiannis+Demiris%22">Yiannis Demiris</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Robots--Control+systems%22">Robots--Control systems</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Robotics%22">TECHNOLOGY & ENGINEERING / Robotics</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=514157 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 629.892631 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Robots--Control systems Type: general Titles: – TitleFull: Interdisciplinary Approaches To Robot Learning Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Andreas Birk – PersonEntity: Name: NameFull: Yiannis Demiris – PersonEntity: Name: NameFull: Andreas Birk – PersonEntity: Name: NameFull: Yiannis Demiris IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2000 – D: 04 M: 02 Type: profile Y: 2014 Identifiers: – Type: isbn-print Value: 9789810243203 – Type: isbn-electronic Value: 9789812792747 Numbering: – Type: volume Value: 00024 Titles: – TitleFull: Interdisciplinary Approaches To Robot Learning Type: main |
| ResultId | 1 |
