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Planning Actions to Enable Color Learning on a Mobile Robot.
Mohan Sridharan
and Peter Stone.
International Journal of Information and Systems Sciences,
3(3):510–25, 2007.
official
published version
(unavailable)
Color segmentation is a challenging yet integral subtask of mobile robot systems that use visual sensors, especially since such systems typically have limited computational and memory resources. We present an online approach for a mobile robot to autonomously learn the colors in its environment without any explicitly labeled training data, thereby making it robust to re-colorings in the environment. The robot plans its motion and extracts structure from a color-coded environment to learn colors autonomously and incrementally, with the knowledge acquired at any stage of the learning process being used as a bootstrap mechanism to aid the robot in planning its motion during subsequent stages. With our novel representation, the robot is able to use the same algorithm both within the constrained setting of our lab and in much more uncontrolled settings such as indoor corridors. The segmentation and localization accuracies are comparable to that obtained by a time-consuming offline training process. The algorithm is fully implemented and tested on SONY Aibo robots.
@article{IJISS07,
author="Mohan Sridharan and Peter Stone",
title="Planning Actions to Enable Color Learning on a Mobile Robot",
journal="International Journal of Information and Systems Sciences",
year="2007",volume="3",number="3",pages="510--25",
abstract={
Color segmentation is a challenging yet integral
subtask of mobile robot systems that use visual
sensors, especially since such systems typically
have limited computational and memory resources. We
present an online approach for a mobile robot to
autonomously learn the colors in its environment
without any explicitly labeled training data,
thereby making it robust to re-colorings in the
environment. The robot plans its motion and extracts
structure from a color-coded environment to learn
colors autonomously and incrementally, with the
knowledge acquired at any stage of the learning
process being used as a bootstrap mechanism to aid
the robot in planning its motion during subsequent
stages. With our novel representation, the robot is
able to use the same algorithm both within the
constrained setting of our lab and in much more
uncontrolled settings such as indoor corridors. The
segmentation and localization accuracies are
comparable to that obtained by a time-consuming
offline training process. The algorithm is fully
implemented and tested on SONY Aibo robots.
},
wwwnote={<a href="http://www.math.ualberta.ca/ijiss/SS-Volume-3-2007/No-3-07/SS-07-03-11.pdf">official published version</a>},
}
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