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The governor architecture is a new method for avoiding catatrophic forgetting in neural networks that is particularly useful in online robot learn- ing. The governor architecture uses a categorizer to identify events and excise long sequences of repetitive data that cause catastrophic forgetting in neural networks trained on robot-based tasks. We examine the performance of several variations of the governor architecture on a number of re- lated localization tasks using a simulated robot. The results show that governed networks perform far better than ungoverned networks. Governored networks are able to reliably and robustly prevent catastrophic forgetting in robot learning tasks.