Motion Planning with Uncertain Information in Robotic Tasks
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Motion Planning with Uncertain Information in Robotic Tasks
منابع مشابه
Motion Planning under Uncertainty for Robotic Tasks with Long Time Horizons
Motion planning with imperfect state information is a crucial capability for autonomous robots to operate reliably in uncertain and dynamic environments. Partially observable Markov decision processes (POMDPs) provide a principled general framework for planning under uncertainty. Using probabilistic sampling, point-based POMDP solvers have drastically improved the speed of POMDP planning, enabl...
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Motion planning in uncertain and dynamic environments is critical for reliable operation of autonomous robots. Partially observable Markov decision processes (POMDPs) provide a principled general framework for such planning tasks and have been successfully applied to several moderately complex robotic tasks, including navigation, manipulation, and target tracking. The challenge now is to scale ...
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Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been applied to various robotic tasks. However, solving POMDPs exactly is computationally intractable. A major challenge is to scale up POMDP algorithms for complex robotic tasks. Robotic systems often have mixed observability...
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As robots become more physically robust and capable of sophisticated sensing, navigation, and manipulation, we want them to carry out increasingly complex tasks. A robot that helps in a household must plan over the scale of hours or days, considering abstract features such as the desires of the occupants of the house, as well as detailed models that support locating and getting objects, whether...
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تاریخ انتشار 2014