Overview
- 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
- Describes experiments, which have been conducted to analyze and validate the properties of the developed algorithms
Part of the book series: Springer Tracts in Advanced Robotics (STAR, volume 89)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Keywords
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)
Authors and Affiliations
Bibliographic Information
Book Title: Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
Authors: Jürgen Sturm
Series Title: Springer Tracts in Advanced Robotics
DOI: https://doi.org/10.1007/978-3-642-37160-8
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN: 978-3-642-37159-2Published: 25 May 2013
Softcover ISBN: 978-3-642-43714-4Published: 20 June 2015
eBook ISBN: 978-3-642-37160-8Published: 12 December 2013
Series ISSN: 1610-7438
Series E-ISSN: 1610-742X
Edition Number: 1
Number of Pages: XXV, 204
Topics: Robotics and Automation, Artificial Intelligence, Image Processing and Computer Vision