Peter Stone's Selected Publications

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Color Learning on a Mobile Robot: Towards Full Autonomy under Changing Illumination

Mohan Sridharan and Peter Stone. Color Learning on a Mobile Robot: Towards Full Autonomy under Changing Illumination. In The 20th International Joint Conference on Artificial Intelligence, pp. 2212–2217, January 2007.
IJCAI-07

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Abstract

A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. It is commonly asserted that in order to do so, the agent must be able to learn to deal with unexpected environmental conditions. However an ability to learn is not sufficient. For true extended autonomy, an agent must also be able to recognize when to abandon its current model in favor of learning a new one; and how to learn in its current situation. This paper presents a fully implemented example of such extended autonomy in the context of color map learning on a vision-based mobile robot for the purpose of image segmentation. Past research established the ability of a robot to learn a color map in a single fixed lighting condition when manually given a ``curriculum'', an action sequence designed to facilitate learning. This paper introduces algorithms that enable a robot to i) devise its own curriculum; and ii) recognize for itself when lighting conditions have changed sufficiently to warrant learning a new color map.

BibTeX Entry

@InProceedings{IJCAI07-mohan,
        author="Mohan Sridharan and Peter Stone",
	title="Color Learning on a Mobile Robot: Towards Full Autonomy under Changing Illumination",
	BookTitle="The 20th International Joint Conference on Artificial Intelligence",
	month="January",year="2007",
	pages="2212--2217",
	abstract={ 
                  A central goal of robotics and AI is to be able to
                  deploy an agent to act autonomously in the real
                  world over an extended period of time. It is
                  commonly asserted that in order to do so, the agent
                  must be able to \emph{learn} to deal with unexpected
                  environmental conditions.  However an \emph{ability}
                  to learn is not sufficient.  For true extended
                  autonomy, an agent must also be able to recognize
                  \emph{when} to abandon its current model in favor of
                  learning a new one; and \emph{how} to learn in its
                  current situation.  This paper presents a fully
                  implemented example of such extended autonomy in the
                  context of color map learning on a vision-based
                  mobile robot for the purpose of image
                  segmentation. Past research established the ability
                  of a robot to learn a color map in a single fixed
                  lighting condition when manually given a
                  ``curriculum'', an action sequence designed to
                  facilitate learning. This paper introduces
                  algorithms that enable a robot to i) devise its own
                  curriculum; and ii) recognize for itself when
                  lighting conditions have changed sufficiently to
                  warrant learning a new color map.
	         },
	wwwnote={<a href="http://www.ijcai-07.org/">IJCAI-07</a>},
}

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