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  A Kernel-based Approach to Direct Action Perception

Kroemer, O., Ugur, E., Oztop, E., & Peters, J. (2012). A Kernel-based Approach to Direct Action Perception. In IEEE International Conference on Robotics and Automation (ICRA 2012) (pp. 2605-2610). Piscataway, NJ, USA: IEEE.

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 Creators:
Kroemer, O1, Author              
Ugur, E, Author
Oztop, E, Author
Peters, J1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: The direct perception of actions allows a robot to predict the afforded actions of observed objects. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing the similarity between different subparts. Using this kernel function, together with motor primitive actions, the robot can learn the required mappings to perform direct action perception. The proposed approach was successfully implemented on a real robot, which could then quickly learn to generalize grasping and pouring actions to novel objects.

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 Dates: 2012-05
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1109/ICRA.2012.6224957
BibTex Citekey: KroemerUOP2012
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Title: IEEE International Conference on Robotics and Automation (ICRA 2012)
Place of Event: St. Paul, MN, USA
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Title: IEEE International Conference on Robotics and Automation (ICRA 2012)
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2605 - 2610 Identifier: ISBN: 978-1-4673-1405-3