NEW BOOK: Robot Learning

[thrun+@heaven.learning.cs.cmu.edu]

I have the pleasure to announce the following book:


 
            ****  Recent Advances in Robot Learning  ****
 


edited by 
Judy A. Franklin 
GTE Laboratories, Waltham, MA, USA 
Tom M. Mitchell 
Carnegie Mellon University, Pittsburgh, PA, USA 
Sebastian Thrun 
Carnegie Mellon University, Pittsburgh, PA, USA 
 
Reprinted from MACHINE LEARNING, 23:2-3 
 
THE KLUWER INTERNATIONAL SERIES IN ENGINEERING AND COMPUTER SCIENCE  
VOLUME 368  
Recent Advances in Robot Learning contains seven papers on robot
learning written by leading researchers in the field. As the selection
of papers illustrates, the field of robot learning is both active and
diverse. A variety of machine learning methods, ranging from inductive
logic programming to reinforcement learning, is being applied to many
subproblems in robot perception and control, often with objectives as
diverse as parameter calibration and concept formulation.
While no unified robot learning framework has yet emerged to cover the
variety of problems and approaches described in these papers and other
publications, a clear set of shared issues underlies many robot
learning problems.
 
- Machine learning, when applied to robotics, is situated: it is embedded  
  into a real-world system that tightly integrates perception, decision  
  making and execution.  
- Since robot learning involves decision making, there is an inherent  
  active learning issue.  
- Robotic domains are usually complex, yet the expense of using actual  
  robotic hardware often prohibits the collection of large amounts of training  
  data.  
- Most robotic systems are real-time systems. Decisions must be made within  
  critical or practical time constraints.  
These characteristics present challenges and constraints to the
learning system. Since these characteristics are shared by other
important real-world application domains, robotics is a highly
attractive area for research on machine learning.

Recent Advances in Robot Learning is an edited volume of peer-reviewed
original research comprising seven invited contributions by leading
researchers. This research work has also been published as a special
issue of Machine Learning (Volume 23, Numbers 2 and 3).

Kluwer Academic Publishers, Boston 
 
Date of publishing: June 1996 
224 pp. 
Hardbound 
ISBN: 0-7923-9745-2 
Prices:
NLG: 175.00 
USD: 94.00 
GBP: 66.75 
 
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                             CONTENTS


 o Real-World Robotics: Learning To Plan for Robust Execution, Scott
   W. Bennett, and Gerald F. DeJong

 o Robot Programming by Demonstration (RPD): Supporting the Induction,
   by Human Interaction, by Stefan Muench, Ruediger Dillmann,
   Siegfried Bocionek, and Michael Sassin

 o Performance Improvement of Robot Continuous-Path Operation through
   Iterative Learning Using Neural Networks, by Peter C.Y. Chen, James
   K. Mills, and Kenneth C. Smith

 o Learning Controllers for Industrial Robots, by C. Baroglio,
   A. Giordana, M. Kaiser, M. Nuttin, and R. Piola

 o Active Learning for Vision-Based Robot Grasping, by Marcos
   Salganicoff, Lyle H. Ungar, and Ruzena Bajcsy

 o Purposive Behavior Acquisition for a Real Robot by Vision-Based
   Reinforcement Learning, by Minoru Asada, Shoichi Noda, Sukoya
   Tawaratsumita, and Koh Hosoda

 o Learning Operational Concepts from Sensor Data of a Mobile Robot,
   by Volker Klingspor, Katharina J. Morik, and Anke Rieger


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See 
	http://www.cs.cmu.edu/~thrun/papers/franklin.book.html 

for more information (paper abstracts, order form).