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Planning Actions to Enable Color Learning on a Mobile Robot

Mohan Sridharan and Peter Stone. Planning Actions to Enable Color Learning on a Mobile Robot. International Journal of Information and Systems Sciences, 3(3):510–25, 2007.
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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.

BibTeX

@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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