by M Tenorth and M Beetz
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.
Reference:
Towards Practical and Grounded Knowledge Representation Systems for Autonomous Household Robots (M Tenorth and M Beetz), In Proceedings of the 1st International Workshop on Cognition for Technical Systems, München, Germany, 6-8 October, 2008.
Bibtex Entry:
@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.},
}