Verwandte Artikel zu Approaches to Probabilistic Model Learning for Mobile...
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots: 89 (Springer Tracts in Advanced Robotics) - Hardcover
Inhaltsangabe
This book presents techniques that enable mobile manipulation robots to autonomously adapt to new situations. Covers kinematic modeling and learning; self-calibration; tactile sensing and object recognition; imitation learning and programming by demonstration.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Von der hinteren Coverseite
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.„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Neu kaufen
Diesen Artikel anzeigen
EUR 92,27
Gratis für den Versand innerhalb von/der Deutschland
Versandziele, Kosten & DauerWeitere beliebte Ausgaben desselben Titels
Suchergebnisse für Approaches to Probabilistic Model Learning for Mobile...
Beispielbild für diese ISBN
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
Verlag:
Springer Berlin Heidelberg, 2013
ISBN 10: 3642371590
ISBN 13: 9783642371592
Gebraucht
Hardcover
Anbieter: Buchpark, Trebbin, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher. Bestandsnummer des Verkäufers 23745518/12
Anzahl: 1 verfügbar
Foto des Verkäufers
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
Anbieter: moluna, Greven, Deutschland
Verkäuferbewertung 4 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers 5058523
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
ISBN 10: 3642371590
ISBN 13: 9783642371592
Neu
Hardcover
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Buch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 232 pp. Englisch. Bestandsnummer des Verkäufers 9783642371592
Anzahl: 2 verfügbar
Foto des Verkäufers
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Bestandsnummer des Verkäufers 9783642371592
Anzahl: 1 verfügbar
Foto des Verkäufers
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
ISBN 10: 3642371590
ISBN 13: 9783642371592
Neu
Hardcover
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 232 pp. Englisch. Bestandsnummer des Verkäufers 9783642371592
Anzahl: 2 verfügbar
Beispielbild für diese ISBN
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots (Springer Tracts in Advanced Robotics, 89)
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. In. Bestandsnummer des Verkäufers ria9783642371592_new
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots (Springer Tracts in Advanced Robotics, 89)
Anbieter: Best Price, Torrance, CA, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. SUPER FAST SHIPPING. Bestandsnummer des Verkäufers 9783642371592
Anzahl: 2 verfügbar
Beispielbild für diese ISBN
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
Anbieter: Books Puddle, New York, NY, USA
Verkäuferbewertung 4 von 5 Sternen
Zustand: New. pp. 250. Bestandsnummer des Verkäufers 2697681556
Anzahl: 4 verfügbar
Beispielbild für diese ISBN
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
Print-on-DemandAnbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. PRINT ON DEMAND pp. 250. Bestandsnummer des Verkäufers 1897681566
Anzahl: 4 verfügbar
Beispielbild für diese ISBN
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
Print-on-DemandAnbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Print on Demand pp. 250. Bestandsnummer des Verkäufers 94715723
Anzahl: 4 verfügbar