Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How

• URL: http://arxiv.org/abs/2303.13385v1
• Date: Thu, 23 Mar 2023 15:55:19 GMT
• Title: Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How
• Authors: Dhruv Saxena and Maxim Likhachev
• Abstract summary: A recently proposed algorithm, M4M, determines which objects need to be moved and where by solving a Multi-Agent Pathfinding MAPF abstraction of this problem. We extend M4M and present Enhanced-M4M -- a systematic graph search-based solver that searches over orderings of pushes for movable objects.
• Score: 17.498416513944886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide, tilt, lean or topple. A recently proposed algorithm, M4M, determines which objects need to be moved and where by solving a Multi-Agent Pathfinding MAPF abstraction of this problem. It then utilises a nonprehensile push planner to compute actions for how the robot might realise these rearrangements and a rigid body physics simulator to check whether the actions satisfy physics constraints encoded in the problem. However, M4M greedily commits to valid pushes found during planning, and does not reason about orderings over pushes if multiple objects need to be rearranged. Furthermore, M4M does not reason about other possible MAPF solutions that lead to different rearrangements and pushes. In this paper, we extend M4M and present Enhanced-M4M (E-M4M) -- a systematic graph search-based solver that searches over orderings of pushes for movable objects that need to be rearranged and different possible rearrangements of the scene. We introduce several algorithmic optimisations to circumvent the increased computational complexity, discuss the space of problems solvable by E-M4M and show that experimentally, both on the real robot and in simulation, it significantly outperforms the original M4M algorithm, as well as other state-of-the-art alternatives when dealing with complex scenes.

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