Neural Subgoal Generation using Backpropagation

Building a world model takes exponential computational costs with the number of obstacles. In real world applications are usually many obstacles, possibly changing their positions over time. In order to cope with a changing environment, a solution has to be adaptive. In order to plan complex trajectories, a system that plans hierarchically shows many advantages. In this article we report on results with a neural (therefore inherently adaptive) subgoal generation system. We show that meaningful subgoals can be produced for two joint manipulators in an environment with obstacles. Unlike many other approaches our approach works (once trained) fast and remains adaptive. 1 Motivation and Introduction Trajectory generation for manipulators is a difficult problem, up to now not satisfyingly solved. Of course there are several classical algorithms that are able to construct collision free paths (e.g. [4]) using only very limited computational resources. Normally these algorithms are based on ..

Hierarchical Planning Using Neural Subgoal Generation

Building a world model takes exponential computational costs in the dimension of the configuration space. Furthermore complexity increases with the number of obstacles, which in real world applications usually is high. Conventional algorithms can not even cope with slowly changing environments. In order to plan complex trajectories, a system that plans hierarchically shows many advantages. In this article we report on results with a neural (therefore inherently adaptive) subgoal generation system. We show that meaningful subgoals can be produced for manipulators in an environment with obstacles. Opposite to many other approaches our approach works (once trained) fast but remains adaptive. 1 Motivation and Introduction Trajectory generation for manipulators is a difficult problem, up to now not satisfyingly solved. Of course there are several classical algorithms that are able to construct collision free paths (e.g. [10], [6]) using only very limited computational resources, therefore ..

Classification of non-linear-separable real-world-problems using #DELTA#-rule, perceptrons, and topologically distributed encoding

This is a revised version of an article, which was originally published in the 'Proceedings of the 1992 ACM/SIGAPP symposium on applied computing', vol. II, p. 1098-1104, ACM Press, ISBN 0-89791-502-XAvailable from TIB Hannover: RO9403(167) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

NERES - Neuronale Regelung und Steuerung von Industrierobotern Abschlussbericht

This report describes the part of the BMFT-Project NERES research, which was carried out at the Technische Universitaet Muenchen. The neural approaches, which were subject of research, such as causality detection, adaptive subgoal generation, system identification with combined networks and trajectory generation with reinforcement learning, were successfully applied to simple environments. It turned out, however, that these approaches could not be scaled up for use in more complex real-world applications. Thus, the experiences won during these studies were used for the development of new, more powerful methods, which combine neural (subsymbolic) elements with symbolic AI-techniques. Various basic neural methods were extended and improved in the process. In the area of symbolic AI, approaches for the qualitative representation of multidimensional spatial knowledge were developed, which preserve essential properties of the spatial domain. They also include cognitive aspects, which allow to handle the problem of mapping user-supplied knowledge into the machine representation and vice versa. (orig.)SIGLEAvailable from TIB Hannover: F96B66+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman