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)
We identify two properties of the human vision system, the foveated retina, and the ability to sac-cade, and show how these two properties are sufficient to simultaneously learn the structure of receptive fields in the retina and a saccade policy that centers the fovea on points of interest in a scene. We consider a novel learning algorithm under this(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)
To everyone who has helped and supported me over the years Acknowledgments First and foremost, I would like to thank my advisor, Ben Kuipers, for his guidance in my research work. I greatly appreciate his stimulation, optimism, encouragement, and patience in my work, without which I would not have been able to make as much progress as I have achieved. His(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)
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