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Towards Learning Path Planning for Solving Complex Robot Tasks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84035

Lal,  TN
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Frontzek, T., Lal, T., & Eckmiller, R. (2001). Towards Learning Path Planning for Solving Complex Robot Tasks. In Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001) Vienna (pp. 943-950).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-E37E-4
Zusammenfassung
For solving complex robot tasks it is necessary to incorporate path planning methods that are able to operate within different high-dimensional configuration spaces containing an unknown number of obstacles. Based on Advanced A*-algorithm (AA*) using expansion matrices instead of a simple expansion logic we propose a further improvement of AA* enabling the capability to learn directly from sample planning tasks. This is done by inserting weights into the expansion matrix which are modified according to a special learning rule. For an examplary planning task we show that Adaptive AA* learns movement vectors which allow larger movements than the initial ones into well-defined directions of the configuration space. Compared to standard approaches planning times are clearly reduced.