DocumentCode
2632949
Title
Grasping POMDPs
Author
Hsiao, Kaijen ; Kaelbling, Leslie Pack ; Lozano-Pérez, Tomás
Author_Institution
Comput. Sci. & Artificial Intelligence Lab., Massachusetts Inst. of Technol., Cambridge, MA
fYear
2007
fDate
10-14 April 2007
Firstpage
4685
Lastpage
4692
Abstract
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
Keywords
Markov processes; grippers; manipulators; motion control; optimal control; compliant motions; optimal control; partially observable Markov decision process; robot grasping; robotic manipulation; Feedback; Motion planning; Optimal control; Orbital robotics; Robot sensing systems; Robot vision systems; Robotics and automation; Robustness; Shape; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics and Automation, 2007 IEEE International Conference on
Conference_Location
Roma
ISSN
1050-4729
Print_ISBN
1-4244-0601-3
Electronic_ISBN
1050-4729
Type
conf
DOI
10.1109/ROBOT.2007.364201
Filename
4209819
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