Abstract
Manipulation problems involving many 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.
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Acknowledgments
This work was supported in part by the NSF under Grant No. 019868, in part by ONR MURI grant N00014-09-1-1051, in part by AFOSR grant AOARD-104135 and in part by Singapore Ministry of Education under a grant to the Singapore-MIT International Design Center.
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Garrett, C.R., Lozano-Pérez, T., Kaelbling, L.P. (2015). FFRob: An Efficient Heuristic for Task and Motion Planning. In: Akin, H., Amato, N., Isler, V., van der Stappen, A. (eds) Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-16595-0_11
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DOI: https://doi.org/10.1007/978-3-319-16595-0_11
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