Technical University of Munich

@inproceedings{tenorth_towards_2008,
 author = {M Tenorth and M Beetz},
 title = {Towards Practical and Grounded Knowledge Representation Systems for
	Autonomous Household Robots},
 booktitle = {Proceedings of the 1st International Workshop on Cognition for Technical
	Systems, München, Germany, 6-8 October},
 year = {2008},
 abstract = {Mobile household robots need much knowledge about objects, places
	and actions when performing more and more complex tasks. They must
	be able to recognize objects, know what they are and how they can
	be used. This knowledge can often be specified more easily in terms
	of action-related concepts than by giving declarative descriptions
	of the appearance of objects. Defining chairs as objects to sit on,
	for instance, is much more natural than describing how chairs in
	general look like. Having grounded symbolic models of its actions
	and related concepts allows the robot to reason about its activities
	and improve its problem solving performance. In order to use action-related
	concepts, the robot must be able to find them in its environment.
	We present a practical approach to robot knowledge representation
	that combines description logics knowledge bases with data mining
	and (self-) observation modules. The robot collects experiences while
	executing actions and uses them to learn models and aspects of action-related
	concepts grounded in its perception and action system. We demonstrate
	our approach by learning places that are involved in mobile robot
	manipulation actions.},
}