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@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={official published version},
}