Grasping POMDPs Kaijen Hsiao and Leslie Pack Kaelbling and Tom´as Lozano-P´erez Abstract— We provide a method for planning under uncertainty for robotic manipulation by partitioning the configuration space into a set

First Page		Document Content
Date: 2012-06-11 20:15:52 Stochastic control Partially observable Markov decision process Procedural programming languages Lisp programming language Markov decision process ALGOL 68 Automated planning and scheduling Motion planning Cons Statistics Dynamic programming Markov processes		Grasping POMDPs Kaijen Hsiao and Leslie Pack Kaelbling and Tom´as Lozano-P´erez Abstract— We provide a method for planning under uncertainty for robotic manipulation by partitioning the configuration space into a set Add to Reading List Source URL: people.csail.mit.edu Download Document from Source Website File Size: 2,05 MB Share Document on Facebook

	Towards a high level programming paradigm to deploy e-science applications with dynamic workflows on large scale distributed systems Mohamed Ben Belgacem Nabil Abdennadher DocID: 1xTOs - View Document
	Minimax Differential Dynamic Programming: An Application to Robust Biped Walking Jun Morimoto Human Information Science Labs, Department 3, ATR International DocID: 1vqMk - View Document
	Empirical Dynamic Programming William B. Haskell ISE Department, National University of Singapore Rahul Jain* DocID: 1vouJ - View Document
	MarchRevised MayReport LIDS-P-3506 Stable Optimal Control and Semicontractive Dynamic Programming DocID: 1vhRF - View Document
	EE365: Deterministic Finite State Control Deterministic optimal control Shortest path problem Dynamic programming Examples DocID: 1vg0M - View Document