A Kernel-based Approach to Direct Action Perception

Oliver Kroemer, Emre Ugur, Erhan Oztop and Jan Peters
Conference Paper, International Conference on Robotics and Automation (ICRA), January, 2012

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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.

author = {Oliver Kroemer and Emre Ugur and Erhan Oztop and Jan Peters},
title = {A Kernel-based Approach to Direct Action Perception},
booktitle = {International Conference on Robotics and Automation (ICRA)},
year = {2012},
month = {January},
} 2019-03-12T14:58:12-04:00