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Gaussian filter
Results: 175

#	Item
111	Belief space planning assuming maximum likelihood observations Robert Platt Jr., Russ Tedrake, Leslie Kaelbling, Tomas Lozano-Perez Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Techn Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2012-06-11 20:17:08 Estimation theory Optimal control Stochastic control Robot control Kalman filter Partially observable Markov decision process Linear-quadratic-Gaussian control Algebraic Riccati equation Maximum likelihood Control theory Systems theory Cybernetics
112	Enhanced Real-time Stereo Using Bilateral Filtering∗ Adnan Ansar Andres Castano Larry Matthies Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 {ansar, andres, lhm}@jpl.nasa.gov Add to Reading List Source URL: www-robotics.jpl.nasa.gov Language: English - Date: 2013-03-28 13:19:18 Covariance and correlation Statistics Imaging Bilateral filter Gaussian function Digital photography Cross-correlation Gaussian filter Image noise Computer vision Image processing Signal processing
113	Non-Gaussian Belief Space Planning: Correctness and Complexity Robert Platt, Leslie Kaelbling, Tomas Lozano-Perez, Russ Tedrake Abstract— We consider the partially observable control problem where it is potentially nec Add to Reading List Source URL: lis.csail.mit.edu Language: English - Date: 2012-07-07 18:01:19 Estimation theory Robot control Probability and statistics Normal distribution Particle filter Pi Statistics Mathematical analysis Monte Carlo methods
114	Efficient planning in non-Gaussian belief spaces and its application to robot grasping Robert Platt, Leslie Kaelbling, Tomas Lozano-Perez, and Russ Tedrake Abstract The limited nature of robot sensors make many important Add to Reading List Source URL: lis.csail.mit.edu Language: English - Date: 2013-10-03 18:52:16 Estimation theory Cybernetics Linear filters Markov models Kalman filter Particle filter Partially observable Markov decision process Normal distribution Recursive Bayesian estimation Statistics Control theory Robot control
115	Non-Gaussian Belief Space Planning: Correctness and Complexity Robert Platt, Leslie Kaelbling, Tomas Lozano-Perez, Russ Tedrake Abstract— We consider the partially observable control problem where it is potentially nec Add to Reading List Source URL: groups.csail.mit.edu Language: English - Date: 2012-02-11 15:07:06 Estimation theory Robot control Probability and statistics Normal distribution Particle filter Pi Statistics Mathematical analysis Monte Carlo methods
116	Efficient planning in non-Gaussian belief spaces and its application to robot grasping Robert Platt, Leslie Kaelbling, Tomas Lozano-Perez, and Russ Tedrake Abstract The limited nature of robot sensors make many important Add to Reading List Source URL: groups.csail.mit.edu Language: English - Date: 2011-10-03 13:11:09 Robot control Cybernetics Linear filters Markov models Kalman filter Particle filter Partially observable Markov decision process Normal distribution Recursive Bayesian estimation Statistics Control theory Estimation theory
117	Belief space planning assuming maximum likelihood observations Robert Platt Jr., Russ Tedrake, Leslie Kaelbling, Tomas Lozano-Perez Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Techn Add to Reading List Source URL: groups.csail.mit.edu Language: English - Date: 2011-02-22 11:30:08 Stochastic control Optimal control Estimation theory Robot control Kalman filter Partially observable Markov decision process Linear-quadratic-Gaussian control Algebraic Riccati equation Maximum likelihood Control theory Statistics Systems theory
118	Reinforcement learning with Gaussian processes Yaakov Engel Dept. of Computing Science, University of Alberta, Edmonton, Canada Shie Mannor Dept. of Electrical and Computer Engineering, McGill University, Montreal, Cana Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2008-12-01 11:15:01 Control theory Linear filters Stochastic differential equations Kalman filter Markov decision process Normal distribution Gaussian process Q-learning SARSA Statistics Markov models Stochastic processes
119	Gaussian Process Product Models for Nonparametric Nonstationarity Ryan Prescott Adams Oliver Stegle Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2008-05-13 10:11:12 Linear filters Exponentials Regression analysis Gaussian process Normal distribution Kalman filter Linear regression Kriging Mixture model Statistics Estimation theory Econometrics
120	Multi-Task Learning of Gaussian Graphical Models Jean Honorio Dimitris Samaras Stony Brook University, Stony Brook, NY 11794, USA Add to Reading List Source URL: www.icml2010.org Language: English - Date: 2010-06-13 09:06:44 Multivariate normal distribution Tikhonov regularization Support vector machine Control theory Kalman filter Statistics Estimation theory Normal distribution

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