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Springer Tracts in Advanced Robotics #89: Approaches to Probabilistic Model Learning for Mobile Manipulation Robots

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Springer Tracts in Advanced Robotics #89: Approaches to Probabilistic Model Learning for Mobile Manipulation Robots Cover

 

Synopses & Reviews

Publisher Comments:

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: ·

Synopsis:

This book presents novel learning techniques that enable mobile platforms with one or more robotic manipulators to autonomously adapt to new or changing situations.

Synopsis:

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.

Table of Contents

Introduction.- Basics.- Body Schema Learning.- Learning Kinematic Models of Articulated Objects.- Vision-based Perception of Articulated Objects.- Object Recognition using Tactile Sensors.- Object State Estimation using Tactile Sensors.- Learning Manipulation Tasks by Demonstration.- Conclusions.

Product Details

ISBN:
9783642371592
Author:
Sturm, Jurgen
Publisher:
Springer
Author:
Sturm, Jürgen
Subject:
Robotics
Subject:
Adaptive Systems
Subject:
Bayesian inference
Subject:
Computer Vision
Subject:
Kinematic Modeling
Subject:
Machine learning
Subject:
Mobile Manipulation
Subject:
Probabilistic Robotics
Subject:
Service Robotics
Subject:
Robotics and Automation
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Image Processing and Computer Vision
Subject:
Computers-Reference - General
Copyright:
Edition Description:
2013
Series:
Springer Tracts in Advanced Robotics
Series Volume:
89
Publication Date:
20130501
Binding:
HARDCOVER
Language:
English
Pages:
230
Dimensions:
235 x 155 mm

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Artificial Intelligence » Robotics
Computers and Internet » Computers Reference » General
Computers and Internet » Graphics » General
Computers and Internet » Networking » General

Springer Tracts in Advanced Robotics #89: Approaches to Probabilistic Model Learning for Mobile Manipulation Robots New Hardcover
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$109.00 In Stock
Product details 230 pages Springer - English 9783642371592 Reviews:
"Synopsis" by , This book presents novel learning techniques that enable mobile platforms with one or more robotic manipulators to autonomously adapt to new or changing situations.
"Synopsis" by , 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.
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