Approaches to Probabilistic Model Learning for Mobile Manipulation Robots

  • Jürgen Sturm

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 89)

Table of contents

  1. Front Matter
    Pages 1-18
  2. Jürgen Sturm
    Pages 1-11
  3. Jürgen Sturm
    Pages 13-33
  4. Jürgen Sturm
    Pages 35-63
  5. Jürgen Sturm
    Pages 125-139
  6. Jürgen Sturm
    Pages 141-160
  7. Jürgen Sturm
    Pages 161-178
  8. Jürgen Sturm
    Pages 179-183
  9. Back Matter
    Pages 185-203

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.


Adaptive Systems Bayesian Inference Computer Vision Kinematic Modeling Machine Learning Mobile Manipulation Probabilistic Robotics Robotics Service Robotics

Authors and affiliations

  • Jürgen Sturm
    • 1
  1. 1.Informatik 9, Institute of Computer ScienceTechnische Universität MünchenGarchingGermany

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-37159-2
  • Online ISBN 978-3-642-37160-8
  • Series Print ISSN 1610-7438
  • Series Online ISSN 1610-742X
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