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Learning grasping affordance using probabilistic and ontological approaches

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4 Author(s)
Barck-Holst, C. ; Centre for Autonomous Syst., KTH, Stockholm, Sweden ; Ralph, M. ; Holmar, F. ; Kragic, D.

We present two approaches to modeling affordance relations between objects, actions and effects. The first approach we present focuses on a probabilistic approach which uses a voting function to learn which objects afford which types of grasps. We compare the success rate of this approach to a second approach which uses an ontological reasoning engine for learning affordances. Our second approach employs a rule-based system with axioms to reason on grasp selection for a given object.

Published in:

Advanced Robotics, 2009. ICAR 2009. International Conference on

Date of Conference:

22-26 June 2009