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.

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In The Ninth International Conference on Control, Automation, Robotics and Vision, December 2006.

@InProceedings{ICARCV06, title={Autonomous Planned Color Learning on a Mobile Robot Without Labeled Data}, author={Mohan Sridharan and Peter Stone}, booktitle={The Ninth International Conference on Control, Automation, Robotics and Vision}, month={December}, url="http://www.cs.utexas.edu/users/ai-lab?ICARCV06", year={2006} }