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 emphlearn to deal with unexpected environmental conditions. However an emphability to learn is not sufficient. For true extended autonomy, an agent must also be able to recognize emphwhen to abandon its current model in favor of learning a new one; and emphhow 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.

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In The 20th International Joint Conference on Artificial Intelligence, pp. 2212-2217, January 2007.

@InProceedings{IJCAI07-mohan, title={Color Learning on a Mobile Robot: Towards Full Autonomy under Changing Illumination}, author={Mohan Sridharan and Peter Stone}, booktitle={The 20th International Joint Conference on Artificial Intelligence}, month={January}, pages={2212-2217}, url="http://www.cs.utexas.edu/users/ai-lab?IJCAI07-mohan", year={2007} }