Author :
Hsiao, Kaijen ; Kaelbling, Leslie Pack ; Lozano-Pérez, Tomás
Author_Institution :
Comput. Sci. & Artificial Intelligence Lab., Massachusetts Inst. of Technol., Cambridge, MA
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;
Conference_Titel :
Robotics and Automation, 2007 IEEE International Conference on
Conference_Location :
Roma
Print_ISBN :
1-4244-0601-3
Electronic_ISBN :
1050-4729
DOI :
10.1109/ROBOT.2007.364201
