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Perception / Monocular vision / Depth perception / Binocular vision / Monocular / Autostereogram / Texture mapping / Vision / Optics / Stereoscopy


High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning Jeff Michels Ashutosh Saxena Andrew Y. Ng
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Document Date: 2008-12-01 11:15:07


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File Size: 1,03 MB

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MIT Press / /

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Stanford University / /

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car view / car control problem / jmichels@stanford.edu asaxena@stanford.edu ang@cs.stanford.edu obstacle detection systems / supervised learning algorithm / policy search algorithm / texture energy / stereo correspondence algorithms / higher level control algorithm / policy search method / car driving / binocular vision algorithms / texture energy coefficients / reinforcement learning algorithm / robotic applications / remote control car / neural network / greedy search / monocular vision obstacle detection algorithm / learning/policy search / policy search / trained algorithms / learning algorithm / /

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MIT / Stanford University / US Federal Reserve / Reinforcement Learning Jeff Michels Ashutosh Saxena Andrew Y. Ng Computer Science Department / /

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Brian Gerkey / Khian Lim / Harris Laws / Wenmiao Lu / Kohl / Vijay Pradeep / Jeff Michels Ashutosh Saxena Andrew / Morgan Kaufmann / Andrew Y. Ng / Larry Jackel / Sebastian Thrun / /

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learned controller / reinforcement learning controller / level controller / author / controller / /

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California / /

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Machine Learning / Nature Neuroscience / /

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Stanford University / /

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Neuroscience / dense two-frame stereo correspondence algorithms / separately trained algorithms / binocular vision algorithms / 3-d / Machine Learning / html / Image Processing / learning algorithm / laser / higher level control algorithm / monocular vision obstacle detection algorithm / resulting algorithm / Machine vision / neural network / supervised learning algorithm / reinforcement learning algorithm / Pegasus policy search algorithm / /

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