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If a robot is supposed to roam an environment and interact with objects, itis often necessary to know all possible objects in advance, so that a databasewith models of all objects can be generated for visual identification. However,this constraint cannot always be fulfilled. Due to that reason, a model basedobject recognition cannot be used to guide the robot's interactions. Therefore,this paper proposes a system that analyzes features of encountered objects andthen uses these features to compare unknown objects to already known ones. Fromthe resulting similarity appropriate actions can be derived. Moreover, thesystem enables the robot to learn object categories by grouping similar objectsor by splitting existing categories. To represent the knowledge a hybrid formis used, consisting of both symbolic and subsymbolic representations.

Online Vision- and Action-Based Object Classification Using Both Symbolic and Subsymbolic Knowledge Representations