Rapid Learning in Robotics.pdf

Jörg Walter

J rg Walter

Die Deutsche Bibliothek — CIP Data

Walter, Jörg

Rapid Learning in Robotics / by Jörg Walter, 1st ed.

Göttingen: Cuvillier, 1996

Zugl.: Bielefeld, Univ., Diss. 1996

ISBN 3-89588-728-5

c 1997, 1996 for electronic publishing: Jörg Walter

Technische Fakultät, Universität Bielefeld, AG Neuroinformatik

PBox 100131, 33615 Bielefeld, Germany

Email: walter@techfak.uni-bielefeld.de

Url: http://www.techfak.uni-bielefeld.de/ walter/

c 1997 for hard copy publishing: Cuvillier Verlag

Nonnenstieg 8, D-37075 Göttingen, Germany, Fax: +49-551-54724-21

Jörg A. Walter

Rapid Learning in Robotics

Robotics deals with the control of actuators using various types of sensors

and control schemes. The availability of precise sensorimotor mappings

– able to transform between various involved motor, joint, sensor, and

physical spaces – is a crucial issue. These mappings are often highly non-

linear and sometimes hard to derive analytically. Consequently, there is a

strong need for rapid learning algorithms which take into account that the

acquisition of training data is often a costly operation.

The present book discusses many of the issues that are important to make

learning approaches in robotics more feasible. Basis for the major part of

the discussion is a new learning algorithm, the Parameterized Self-Organizing

Maps , that is derived from a model of neural self-organization. A key

feature of the new method is the rapid construction of even highly non-

linear variable relations from rather modestly-sized training data sets by

exploiting topology information that is not utilized in more traditional ap-

proaches. In addition, the author shows how this approach can be used in

a modular fashion, leading to a learning architecture for the acquisition of

basic skills during an “investment learning” phase, and, subsequently, for

their rapid combination to adapt to new situational contexts.

Foreword

The rapid and apparently effortless adaptation of their movements to a

broad spectrum of conditions distinguishes both humans and animals in

an important way even from nowadays most sophisticated robots. Algo-

rithms for rapid learning will, therefore, become an important prerequisite

for future robots to achieve a more intelligent coordination of their move-

ments that is closer to the impressive level of biological performance.

The present book discusses many of the issues that are important to

make learning approaches in robotics more feasible. A new learning al-

gorithm, the Parameterized Self-Organizing Maps , is derived from a model

of neural self-organization. It has a number of beneﬁts that make it par-

ticularly suited for applications in the ﬁeld of robotics. A key feature of

the new method is the rapid construction of even highly non-linear vari-

able relations from rather modestly-sized training data sets by exploiting

topology information that is unused in the more traditional approaches.

In addition, the author shows how this approach can be used in a mod-

ular fashion, leading to a learning architecture for the acquisition of basic

skills during an “investment learning” phase, and, subsequently, for their

rapid combination to adapt to new situational contexts.

The author demonstrates the potential of these approaches with an im-

pressive number of carefully chosen and thoroughly discussed examples,

covering such central issues as learning of various kinematic transforms,

dealing with constraints, object pose estimation, sensor fusion and camera

calibration. It is a distinctive feature of the treatment that most of these

examples are discussed and investigated in the context of their actual im-

plementations on real robot hardware. This, together with the wide range

of included topics, makes the book a valuable source for both the special-

ist, but also the non-specialist reader with a more general interest in the

ﬁelds of neural networks, machine learning and robotics.

Helge Ritter

Bielefeld

iii

Acknowledgment

The presented work was carried out in the connectionist research group

headed by Prof. Dr. Helge Ritter at the University of Bielefeld, Germany.

First of all, I'd like to thank Helge: for introducing me to the exciting

ﬁeld of learning in robotics, for his conﬁdence when he asked me to build

up the robotics lab, for many discussions which have given me impulses,

and for his unlimited optimism which helped me to tackle a variety of

research problems. His encouragement, advice, cooperation, and support

have been very helpful to overcome small and larger hurdles.

In this context I want to mention and thank as well Prof. Dr. Gerhard

Sagerer, Bielefeld, and Prof. Dr. Sommer, Kiel, for accompanying me with

their advises during this time.

Thanks to Helge and Gerhard for refereeing this work.

Helge Ritter, Kostas Daniilidis, Ján Jokusch, Guido Menkhaus, Christof

Dücker, Dirk Schwammkrug, and Martina Hasenjäger read all or parts of

the manuscript and gave me valuable feedback. Many other colleagues

and students have contributed to this work making it an exciting and suc-

cessful time. They include Jörn Clausen, Andrea Drees, Gunther Heide-

mannn, Hartmut Holzgraefe, Ján Jockusch, Stefan Jockusch, Nils Jung-

claus, Peter Koch, Rudi Kaatz, Michael Krause, Enno Littmann, Rainer

Orth, Marc Pomplun, Robert Rae, Stefan Rankers, Dirk Selle, Jochen Steil,

Petra Udelhoven, Thomas Wengereck, and Patrick Ziemeck. Thanks to all

of them.

Last not least I owe many thanks to my Ingrid for her encouragement

and support throughout the time of this work.

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