A Kernel-based Approach to Direct Action Perception

Kroemer, O; Ugur, E; Oztop, E; Peters, J

doi:10.1109/ICRA.2012.6224957

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A Kernel-based Approach to Direct Action Perception

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Kroemer, O
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Peters, J
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6224957
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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-B77A-D

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.