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Humanoid robot / Science / Motor control / Reinforcement learning / Robotics / Trajectory optimization / Machine learning / Robot / Control theory / Cybernetics / Behavior / Ethology


Reinforcement Learning of Full-Body Humanoid Motor Skills
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Document Date: 2011-02-17 01:25:17

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Nashville / London / New York / /

Company

Neural Information Processing Systems / ATR Computational Neuroscience Laboratories / MIT Press / /

Country

United States / /

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pence / USD / /

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Facility

University of Southern California / Stanford University / /

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dimensional systems / continuous stateaction systems / real-time control software package / post-processing steps / reinforcement learning algorithms / nonlinear dynamical systems / learning systems / stochastic multi-agent systems / less energy / energy efficiency / humanoid systems / learning algorithms / control law / /

Organization

Army Research Office / Okawa Foundation / National Science Foundation / MIT / Stanford University / University of Southern California / Los Angeles / German Research Foundation / Computational Learning and Motor Control Lab / /

Person

Jonas Buchli / Evangelos Theodorou / /

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Position

rt / researcher / straight-forward / controller / /

Product

PI2 / /

ProvinceOrState

Southern California / California / New York / /

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Machine Learning / Journal of Machine Learning Research / Journal of Artificial Intelligence Research / /

Region

Southern California / /

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Stanford University / /

Technology

neuroscience / reinforcement learning algorithms / resulting algorithm / PI2 algorithm / 2 ALGORITHM / Machine Learning / simulation / /

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http /

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