Backward-Forward Search for Manipulation Planning
  In this paper we address planning problems in high-dimensional hybrid configuration spaces, with a particular focus on manipulation planning problems involving many objects. We present the hybrid backward-forward (HBF) planning algorithm that uses a backward identification of constraints to direct the sampling of the infinite action space in a forward search from the initial state towards a goal configuration. The resulting planner is probabilistically complete and can effectively construct long manipulation plans requiring both prehensile and nonprehensile actions in cluttered environments. 
Caelan Reed Garrett, Tomas Lozano-Perez, and Leslie Pack Kaelbling
cs.RO cs.AI