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Autonomous Color Learning on a Mobile Robot.
Mohan Sridharan and Peter Stone.
In Proceedings of the Twentieth National Conference on Artificial
Intelligence, July 2005.
Some videos of
the robot referenced in the paper.
AAAI 2005
[PDF]901.5kB [postscript]6.4MB
Color segmentation is a challenging subtask in computer vision. Most popular approaches are computationally expensive, involve an extensive off-line training phase and/or rely on a stationary camera. This paper presents an approach for color learning on-board a legged robot with limited computational and memory resources. A key defining feature of the approach is that it works without any labeled training data. Rather, it trains autonomously from a color-coded model of its environment. The process is fully implemented, completely autonomous, and provides high degree of segmentation accuracy.
@InProceedings(AAAI05-vision,
author="Mohan Sridharan and Peter Stone",
title="Autonomous Color Learning on a Mobile Robot",
booktitle="Proceedings of the Twentieth National Conference on Artificial Intelligence",
month="July",year="2005",
abstract={Color segmentation is a challenging subtask in computer vision. Most popular approaches are computationally expensive, involve an extensive off-line training phase and/or rely on a stationary camera. This paper presents an approach for color learning on-board a legged robot with limited computational and memory resources. A key defining feature of the approach is that it works without any labeled training data. Rather, it trains autonomously from a color-coded model of its environment. The process is fully implemented, completely autonomous, and provides high degree of segmentation accuracy.},
wwwnote={Some <a href="http://www.cs.utexas.edu/users/AustinVilla/?p=research/auto_vis">videos of the robot</a> referenced in the paper.<br>
<a href="http://www.aaai.org/Conferences/National/2005/aaai05.html">AAAI 2005</a>},
)
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