Optimal Grasps and Placements for Task and Motion Planning in Clutter

Carlos Quintero-Peña, Zachary K. Kingston, Tianyang Pan, Rahul Shome, Anastasios Kyrillidis, Lydia E. Kavraki

Abstract: Many methods that solve robot planning problems, such as task and motion planners, employ discrete symbolic search to find sequences of valid symbolic actions that are grounded with motion planning. Much of the efficacy of these planners lies in this grounding-bad placement and grasp choices can lead to inefficient planning when a problem has many geometric constraints. Moreover, grounding methods such as naïve sampling often fail to find appropriate values for these choices in the presence of clutter. Towards efficient task and motion planning, we present a novel optimization-based approach for grounding to solve cluttered problems that have many constraints that arise from geometry. Our approach finds an optimal grounding and can provide feedback to discrete search for more effective planning. We demonstrate our method against baseline methods in complex simulated environments.