Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking

Abstract

Rearranging objects in cluttered tabletop environments remains a long-standing challenge in robotics. Classical planners often generate inefficient, high-cost plans by moving objects individually and using fixed buffers, temporary spaces such as unoccupied tabletop regions or static stacks, to resolve conflicts. When only free tabletop locations are used as buffers, planning in dense scenes becomes difficult, since placing an object can prevent others from reaching their goals and complicate subsequent planning. Stacking provides additional buffer capacity, but conventional stacking is static: the base object cannot be relocated while holding another, which limits efficiency. To address these limitations, a novel planning primitive called the Dynamic Buffer is introduced. Inspired by human grouping strategies, it enables robots to form temporary, movable stacks that can be transported as a unit. Dynamic buffers improve feasibility and efficiency in dense layouts and reduce travel in large-scale scenarios where buffer space is ample. Compared with a state-of-the-art rearrangement planner, the proposed approach reduces total manipulator travel cost by 13.48% in dense stationary-manipulator scenarios and by 4.34% in large-scale, low-density scenarios involving mobile manipulators. Practicality is validated through execution on a Delta robot with a parallel-jaw gripper. These results establish dynamic buffering as a key primitive for cost-efficient and robust rearrangement planning.