Grasping POMDPs
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...
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| creator | Kaijen Hsiao Kaelbling, L.P. Lozano-Perez, T. |
| description | 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 |
| doi_str_mv | 10.1109/ROBOT.2007.364201 |
| format | Conference Proceeding |
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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</abstract><pub>IEEE</pub><doi>10.1109/ROBOT.2007.364201</doi><tpages>8</tpages></addata></record> |
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| identifier | ISSN: 1050-4729 |
| ispartof | Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007, p.4685-4692 |
| issn | 1050-4729 2577-087X |
| language | eng |
| recordid | cdi_ieee_primary_4209819 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Feedback Motion planning Optimal control Orbital robotics Robot sensing systems Robot vision systems Robotics and automation Robustness Shape Uncertainty |
| title | Grasping POMDPs |
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