Today's robots are still lacking comprehensive knowledge bases about objects and their properties. Yet, a lot of knowledge is required when performing manipulation tasks to identify abstract concepts like a "handle" or the "blade of a spatula" and to ground them into concrete coordinate frames that can be used to parametrize the robot's actions. In this paper, we present a system that enables robots to use CAD models of objects as a knowledge source and to perform logical inference about object

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... components that have automatically been identified in these models. The system includes several algorithms for mesh segmentation and geometric primitive fitting which are integrated into the robot's knowledge base as procedural attachments to the semantic representation. Bottomup segmentation methods are complemented by top-down, knowledge-based analysis of the identified components. The evaluation on a diverse set of object models, downloaded from the Internet, shows that the algorithms are able to reliably detect several kinds of object parts. * Moritz Tenorth, Balint-Benczedi and Michael Beetz are with the