Color segmentation is a challenging subtask in computer vision. Most popular approaches are computationally expensive, involve an extensive off-line training phase and/or rely on a stationary camera. This paper presents an approach for color learning on-board a legged robot with limited computational and memory resources. A key defining feature of the approach is that it works without any labeled training data. Rather, it trains autonomously from a color-coded model of its environment. The process is fully implemented, completely autonomous, and provides high degree of segmentation accuracy.

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In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.

@InProceedings{AAAI05-vision, title={Autonomous Color Learning on a Mobile Robot}, author={Mohan Sridharan and Peter Stone}, booktitle={Proceedings of the Twentieth National Conference on Artificial Intelligence}, month={July}, url="http://www.cs.utexas.edu/users/ai-lab?AAAI05-vision", year={2005} }