Rapid Learning in Robotics

Jörg 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 Copyright: 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 thediscussion isanewlearningalgorithm, theParameterized 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. ii 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 forrapid learning will, therefore, becomean importantprerequisite 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. HelgeRitter, Kostas Daniilidis, JánJokusch, 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. ... - tailieumienphi.vn