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Partially observable Markov decision process
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161	Simultaneous Localization and Grasping Using Belief Space Planning Robert Platt, Leslie Kaelbling, Tomas Lozano-Perez, and Russ Tedrake Abstract— Most approaches to grasp planning assume that the configurations of the Add to Reading List Source URL: groups.csail.mit.edu Language: English - Date: 2011-05-03 00:10:43 Robot control Estimation theory Monte Carlo method Numerical analysis Probabilistic complexity theory Algorithm Particle filter Partially observable Markov decision process Computational complexity theory Statistics Applied mathematics Probability and statistics
162	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
163	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
164	Oliehoek-et-al-camready-version.dvi Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2012-04-11 06:48:59 Stochastic control Control theory Partially observable Markov decision process Markov decision process Trust Advice Statistics Dynamic programming Markov processes
165	JOIST (Joint Operational Informational Strike Tool) Add to Reading List Source URL: edocs.nps.edu Language: English - Date: 2004-12-06 17:08:04 Stochastic control Control theory Structural engineering Partially observable Markov decision process Joist Markov decision process Statistics Dynamic programming Markov processes
166	Apprenticeship Learning for Model Parameters of Partially Observable Environments Takaki Makino [removed] Institute of Industrial Science, the University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo[removed]Ja Add to Reading List Source URL: icml.cc Language: English - Date: 2012-06-07 13:20:26 Dynamic programming Stochastic control Estimation theory Partially observable Markov decision process Markov decision process Apprenticeship learning Reinforcement learning Conjugate prior Automated planning and scheduling Statistics Markov processes Markov models
167	Monte Carlo Bayesian Reinforcement Learning Yi Wang WANGY @ COMP. NUS . EDU . SG Kok Sung Won KOKSUNG @ COMP. NUS . EDU . SG Add to Reading List Source URL: icml.cc Language: English - Date: 2012-06-07 13:20:16 Stochastic control Markov models Partially observable Markov decision process Bayesian statistics Reinforcement learning Markov decision process Conjugate prior Markov chain Q-learning Statistics Markov processes Dynamic programming
168	Bounded Planning in Passive POMDPs Roy Fox Naftali Tishby School of Computer Science and Engineering The Hebrew University Add to Reading List Source URL: icml.cc Language: English - Date: 2012-06-07 13:20:56 Markov processes Stochastic control Partially observable Markov decision process Rate–distortion theory Entropy Markov decision process Statistics Information theory Dynamic programming
169	pre-print: The 43rd Intl. Symp. on Robotics (ISR2012), Taipei, Taiwan, 29 – 31 August 2012, pp. 1106 – [removed]ML, MAP and greedy POMDP shared control: comparison of wheelchair navigation assistance for switch interfa Add to Reading List Source URL: lirias.kuleuven.be Language: English Stochastic control Control theory Partially observable Markov decision process User interface Robotics Markov decision process Autonomous robot Statistics Dynamic programming Markov processes
170	Automatic Derivation of Memoryless Policies and Finite-State Controllers Using Classical Planners Blai Bonet H´ector Palacios Add to Reading List Source URL: www.dtic.upf.edu Language: English - Date: 2009-07-01 11:55:19 Control theory Stochastic control Automated planning and scheduling Markov processes Planning Domain Definition Language Finite-state machine Partially observable Markov decision process Fluent XTR Cybernetics Applied mathematics Systems theory

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