Logo - springer
Slogan - springer

Engineering - Robotics | Learning Motor Skills - From Algorithms to Robot Experiments

Learning Motor Skills

From Algorithms to Robot Experiments

Kober, Jens, Peters, Jan

2014, XVI, 191 p. 56 illus., 54 illus. in color.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$79.99

(net) price for USA

ISBN 978-3-319-03194-1

digitally watermarked, no DRM

Included Format: PDF

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$109.00

(net) price for USA

ISBN 978-3-319-03193-4

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • Presents an overview of reinforcement learning as applied to robotics
  • Provides novel algorithms and novel applications for learning motor skills
  • Extensively evaluates the applications of the approaches on benchmark and robot tasks (including ball-in-a-cup, darts, table-tennis, throwing and ball-bouncing) with simulated and real robots

This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor

skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters, and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation, and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author’s doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award.

Content Level » Research

Keywords » Machine Learning - Motor Primitives - Policy Search - Reinforcement Learning - Robotics - Skill Learning

Related subjects » Artificial Intelligence - Robotics

Table of contents 

Reinforcement Learning in Robotics: A Survey.- Movement Templates for Learning of Hitting and Batting.- Policy Search for Motor Primitives in Robotics.- Reinforcement Learning to Adjust Parameterized Motor Primitives to New Situations.- Learning Prioritized Control of Motor Primitives.

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Robotics and Automation.