Robots performing tasks constantly encounter changing environmental conditions. These changes in the environment vary from the dramatic, such as rearrangement of furniture, to the subtle, such as a burnt out light bulb or a different carpeting. We do not recognize many of these changes, especially subtle changes, but robots do. These changes often lead to the failure of robots. In this thesis, we develop an algorithm for detecting these changes. Traditional sensor models do not capture all of the dependencies in the sensor data and are not capable of detecting all types of signal changes while maintaining a strong probabilistic foundation. This thesis corrects these shortcomings. We show how detecting the current conditions in which the robot is operating can lead to increased performance and lower failure rates. The methods in this thesis are tested on real tasks performed by a real robot, namely a Sony AIBO robot.

167 pages

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