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The state-based learning method presented is applicable to virtually any vision-based control problem. We use navigation as an example. In a controlled environment, we can define a few known landmarks before system design, and the navigation system can employ landmark detectors. Such navigation systems typically employ a model-based design method. However these methods have difficulties dealing with learning in complex, changing environments. To overcome these limitations, we have developed Shoslif (Self-organizing Hierarchical Optimal Subspace Learning and Inference Framework), a model-free, learning-based approach. Shoslif introduces mechanisms such as automatic feature derivation, a self-organizing tree structure to reach a very low logarithmic time complexity, one-instance learning, and incremental learning without forgetting prior memorized information. In addition, we have created a state-based version of Shoslif that lets humans teach robots to use past history and local views that are useful for disambiguation. Shoslif-N is a prototype autonomous navigation system using Shoslif. We have tested Shoslif-N primarily indoors. Indoor navigation encounters fewer lighting changes than outdoor navigation. However, it offers other, considerable challenges for vision-based navigation. Shoslif-N has shown that it can navigate in real time reliably in an unaltered indoor environment for an extended amount of time and distance, without any special image-processing hardware.