Approaches to probabilistic model learning for mobile manipulation robots
Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots that implement large parts of the housework, and versatile industrial assistants that provide automation, transportat...
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| Autor principal: | |
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| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado em: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edição: | 1st ed. 2013. |
| Colecção: | Springer Tracts in Advanced Robotics
89 |
| Acesso em linha: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Approaches to Probabilistic Model Learning for Mobile Manipulation Robots, Texte imprimé, 9783642371592 • Approaches to Probabilistic Model Learning for Mobile Manipulation Robots, Texte imprimé, 9783642371615 • Approaches to Probabilistic Model Learning for Mobile Manipulation Robots, Texte imprimé, 9783642437144 • Approaches to Probabilistic Model Learning for Mobile Manipulation Robots, Texte imprimé, 9783642371592 |
| Resumo: | Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots that implement large parts of the housework, and versatile industrial assistants that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. This book presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The approaches presented in this book cover the following topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment the visual perception, and (4) learning novel manipulation tasks from human demonstrations. This book is an ideal resource for postgraduates and researchers working in robotics, computer vision, and artificial intelligence who want to get an overview on one of the following subjects: kinematic modeling and learning, self-calibration and life-long adaptation, tactile sensing and tactile object recognition, and imitation learning and programming by demonstration |
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| Descrição do item: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783642371608 |
| ISSN: | 1610-742X |
| Acesso: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |