Abstract of FKI-195-94

Document-Name:  fki-195-94.ps.gz
Title:          Supervised Subgoal Generation for Manipulators
Authors:        Martin Eldracher, Boris Baginski
Revision-Date:  1996/07/12
Category:       Technical Report (Forschungsberichte Künstliche Intelligenz)
Abstract:       Building a model for an environment with a specific 
                manipulator takes exponential computational costs in the 
                dimension of the manipulator's configuration space. 
                Furthermore complexity increases with the number of obstacles,
                which in real world applications usually is high.
                Therefore many classical trajectory planning algorithms, 
                based on world models, can not cope with a changing 
                environment.  In order to plan complex trajectories, a system 
                that plans hierarchically shows many advantages. The single 
                sub-trajectories may be simple and can often be recombined 
                for new tasks without further low-level planning. In this 
                article we report on results with different neural network
                based (and therefore inherently adaptive), hierarchical 
                trajectory planning systems. Trajectories are built in 
                combining known sub-trajectories by choosing subgoals. 
                The neural systems are trained with the (supervised) back-
                propagation learning rule. Nevertheless the subgoals are 
                produced by the system itself, without any pre-specifications.
                We show that useful subgoals can be produced for manipulators 
                in different (but still static) environments with obstacles.  
                Opposite to many classical approaches our approach works (once
                trained) fast but remains adaptive. In order to show the 
                capability of our system we compare the results to
                a recently introduced model free stochastic search technique.
Keywords:       neural networks, supervised learning, subgoal generation,
                path planning, manipulator, configuration space, hierarchical
                planning
Size:           22 pages
Language:       English
ISSN:           0941-6358
Copyright:      The ``Forschungsberichte Künstliche Intelligenz''
                series includes primarily preliminary publications,
                specialized partial results, and supplementary
                material. In the interest of a subsequent final
                publication these reports should not be copied. All
                rights and the responsability for the contents of the
                report are with the authors, which would appreciate
                critical comments.

Gerhard Weiss