Springer Tracts in Advanced Robotics

Approaches to Probabilistic Model Learning for Mobile Manipulation Robots

Authors: Sturm, Jürgen

  • Presents recent research in Probabilistic Model Learning for Mobile Manipulation Robots
  •  
  • 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
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  • Describes experiments, which have been conducted to analyze and validate the properties of the developed algorithms
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About this book

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.

Reviews

From the reviews:

“This book is convenient for research purposes. It has a clear structure and is fairly readable. The topic may be appropriate for graduate studies.” (Ramon Gonzalez Sanchez, Computing Reviews, January, 2014)


Table of contents (9 chapters)

Buy this book

eBook $109.00
price for USA (gross)
  • ISBN 978-3-642-37160-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $139.00
price for USA
  • ISBN 978-3-642-37159-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $139.00
price for USA
  • ISBN 978-3-642-43714-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
Authors
Series Title
Springer Tracts in Advanced Robotics
Series Volume
89
Copyright
2013
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-37160-8
DOI
10.1007/978-3-642-37160-8
Hardcover ISBN
978-3-642-37159-2
Softcover ISBN
978-3-642-43714-4
Series ISSN
1610-7438
Edition Number
1
Number of Pages
XXV, 204
Topics