Abstract
Service robots have to communicate with their human users in order to get commands, give reports, provide information, and get help in cases of failure. Service robots are not autonomous, but this does not mean that they can do without intelligence. They need intelligence in order to convert high-level human commands into their internal procedures and to adapt their execution to the actual environment. The concepts in commands and plans must be anchored in the “body” of the robot and at the same time be understandable to the human user. This means that robot and user must agree in a particular situation what a concept refers to, even though — because of their different sensory systems and action capabilities — the concept is defined completely different by robot and user. The concept definitions of the robot should include sensing and action so that the concepts become precise and specific when applied to a particular situation. We call such concepts operational concepts.
We apply machine learning in order to establish the link between high-level, human-oriented concepts and low-level perception and action of a mobile robot. Using the method of inductive logic programming, sets of rules for increasingly abstract concepts are learned. The rules are used for planning and plan execution in order to fulfill a user-given goal such as “move along the door”. The first-order logic representation allows time relations to be handled easily. Other advantages are given by unification and instantiation, which allow information that is unknown at the time of planning to be propagated from the environment through the levels of abstraction. The rules express relations — rather than particular value ranges — that must be valid for sensory patterns and for actions.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer Science+Business Media New York
About this chapter
Cite this chapter
Klingspor, V., Morik, K. (1999). Learning Understandable Concepts for Robot Navigation. In: Morik, K., Kaiser, M., Klingspor, V. (eds) Making Robots Smarter. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5239-0_12
Download citation
DOI: https://doi.org/10.1007/978-1-4615-5239-0_12
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7388-9
Online ISBN: 978-1-4615-5239-0
eBook Packages: Springer Book Archive