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Robots typically have many sensors, which are underutilized. This is usually because no simple mathematical models of the sensors have been developed or the sensors are too noisy to use techniques, which require simple noise models. We propose to use these underutilized sensors to determine the state of the environment in which the robot is operating. Being able to identify the state of the environment allows the robot to adapt to current operating conditions and the actions of other agents. Adapting to current operating conditions makes robot robust to changes in the environment by constantly adapting to the current conditions. This is useful for adapting to different lighting conditions or different flooring conditions amongst many other possible desirable adaptations. The strategy we propose for utilizing these sensors is to group sensor readings into statistical probability distributions and then compare the probability distributions to detect repeated states of the environment.