Jeremy Stober

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Modern mobile robots navigate uncertain environments using complex compositions of camera, laser, and sonar sensor data. Manual calibration of these sensors is a tedious process that involves determining sensor behavior, geometry and location through model specification and system identification. Instead, we seek to automate the construction of sensor model(More)
—A baby experiencing the world for the first time faces a considerable challenge sorting through what William James called the " blooming, buzzing confusion " of the senses [1]. With the increasing capacity of modern sensors and the complexity of modern robot bodies, a robot in an unknown or unfamiliar body faces a similar and equally daunting challenge. In(More)
Robots with many sensors are capable of generating volumes of high-dimensional perceptual data. Making sense of this data and extracting useful knowledge from it is a difficult problem. For robots lacking proper models , trying to understand a stream of uninterpreted data is an especially acute problem. One critical step in linking raw uninterpreted(More)
The governor architecture is a new method for avoiding catatrophic forgetting in neural networks that is particularly useful in online robot learning. 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(More)
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