Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking
🤖 AI Summary
Traditional static buffering mechanisms—such as reserved empty slots or fixed stacking—lead to infeasible motion planning and high manipulation costs in dense robotic tabletop rearrangement tasks.
Method: We propose a dynamic buffering mechanism that models temporary stacks as mobile buffer primitives, enabling object grouping and coordinated transport—inspired by human rearrangement strategies. Our approach unifies path planning and action generation by integrating classical planning algorithms with adaptive stacking policies, supporting both stationary and mobile robot platforms.
Contribution/Results: Experiments demonstrate an 11.89% reduction in total arm movement cost in dense scenarios and a 5.69% reduction in large-scale, low-density settings. Feasibility is further validated on a Delta parallel robot. To our knowledge, this is the first work to model buffer space as mobile units, significantly expanding the feasible planning domain and execution efficiency for high-density rearrangement tasks.
Application Category
📝 Abstract
Rearranging objects in cluttered tabletop environments remains a long-standing challenge in robotics. Classical planners often generate inefficient, high-cost plans by shuffling objects individually and using fixed buffers--temporary spaces such as empty table regions or static stacks--to resolve conflicts. When only free table locations are used as buffers, dense scenes become inefficient, since placing an object can restrict others from reaching their goals and complicate planning. Allowing stacking provides extra buffer capacity, but conventional stacking is static: once an object supports another, the base cannot be moved, which limits efficiency. To overcome these issues, 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. This improves both feasibility and efficiency in dense layouts, and it also reduces travel in large-scale settings where space is abundant. Compared with a state-of-the-art rearrangement planner, the approach reduces manipulator travel cost by 11.89% in dense scenarios with a stationary robot and by 5.69% in large, low-density settings with a mobile manipulator. Practicality is validated through experiments on a Delta parallel robot with a two-finger gripper. These findings establish dynamic buffering as a key primitive for cost-efficient and robust rearrangement planning.
Problem
Research questions and friction points this paper is trying to address.
Dynamic Buffers enable movable temporary stacks for rearrangement
Reduces manipulator travel cost in dense and large-scale settings
Overcomes inefficiency of fixed buffers and static stacking methods
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic Buffers enable movable temporary stacks
Robots transport grouped objects as single units
Reduces manipulator travel cost in dense layouts
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