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Ontology-Assisted Generalisation of Robot Action Execution Knowledge

  • When an autonomous robot learns how to execute actions, it is of interest to know if and when the execution policy can be generalised to variations of the learning scenarios. This can inform the robot about the necessity of additional learning, as using incomplete or unsuitable policies can lead to execution failures. Generalisation is particularly relevant when a robot has to deal with a large variety of objects and in different contexts. In this paper, we propose and analyse a strategy for generalising parameterised execution models of manipulation actions over different objects based on an object ontology. In particular, a robot transfers a known execution model to objects of related classes according to the ontology, but only if there is no other evidence that the model may be unsuitable. This allows using ontological knowledge as prior information that is then refined by the robot’s own experiences. We verify our algorithm for two actions – grasping and stowing everyday objects – such that we show that the robot can deduce cases in which an existing policy can generalise to other objects and when additional execution knowledge has to be acquired.

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Document Type:Conference Object
Author:Alex MitrevskiORCiD, Paul G. Plöger, Gerhard Lakemeyer
Parent Title (English):2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 27 Sept.-1 Oct. 2021, Prague, Czech Republic
First Page:6763
Last Page:6770
ArXiv Id:http://arxiv.org/abs/2107.09353
Date of first publication:2021/12/16
Keyword:Cognitive robotics; Continual robot learning; Explainable robotics; Learning from experience
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2021/07/01