FFRob: An Efficient Heuristic for Task and Motion Planning
Author(s)
Garrett, Caelan Reed; Lozano-Perez, Tomas; Kaelbling, Leslie P
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Manipulation problemsinvolvingmany objects present substantial challenges for motion planning algorithms due to the high dimensionality and multi-modality of the search space. Symbolic task planners can efficiently construct plans involving many entities but cannot incorporate the constraints from geometry and kinematics. In this paper, we show how to extend the heuristic ideas from one of the most successful symbolic planners in recent years, the FastForward (FF) planner, to motion planning, and to compute it efficiently. We use a multi-query roadmap structure that can be conditionalized to model different placements of movable objects. The resulting tightly integrated planner is simple and performs efficiently in a collection of tasks involving manipulation of many objects.
Date issued
2015-04Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Algorithmic Foundations of Robotics XI
Publisher
Springer Cham
Citation
Garrett, Caelan Reed, et al. “FFRob: An Efficient Heuristic for Task and Motion Planning.” Algorithmic Foundations of Robotics XI, edited by H. Levent Akin et al., vol. 107, Springer International Publishing, 2015, pp. 179–95.
Version: Author's final manuscript
ISBN
978-3-319-16594-3
978-3-319-16595-0
ISSN
1610-7438
1610-742X