Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · 2007 · 157 citations · 22 references
EngineeringComputer ScienceRobotics EngineeringGraspingMarkov Decision ProcessesRoboticsCompliant GraspingGrasp PlanningOptimal ControlPartially Observable Markov Decision ProcessesRobot ManipulationRobot Motion PlanningConfiguration Space Planning Under UncertaintyGrasp Planning Under UncertaintyManipulation Under UncertaintyMarkov Decision ModelsMotion Planning Under UncertaintyOptimal PoliciesRobotic Manipulation PlanningRobust Grasp Planning
We provide a method for planning under uncertainty for robotic manipulation by partitioning the configuration space into a set of regions that are closed under compliant motions. These regions can be treated as states in a partially observable Markov decision process (POMDP), which can be solved to yield optimal control policies under uncertainty. We demonstrate the approach on simple grasping problems, showing that it can construct highly robust, efficiently executable solutions
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