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

arXiv: https://arxiv.org/abs/2509.22828 · HTML: https://mlsanity.com/paper/2509.22828/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 45 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 28 of 28)

- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [Rearrangement: A Challenge for Embodied AI](https://mlsanity.com/paper/2011.01975/) (2020)
- [Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs](https://mlsanity.com/paper/2012.07277/) (2020)
- [NeRP: Neural Rearrangement Planning for Unknown Objects](https://mlsanity.com/paper/2106.01352/) (2021)
- [Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning](https://mlsanity.com/paper/1904.10348/) (2019)
- [SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs](https://mlsanity.com/paper/2309.12188/) (2023)
- [Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation](https://mlsanity.com/paper/2003.10863/) (2020)
- [Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sorting](https://mlsanity.com/paper/1912.07024/) (2019)
- [LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement](https://mlsanity.com/paper/2309.15821/) (2023)
- [Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps](https://mlsanity.com/paper/1711.07369/) (2017)
- [Fast High-Quality Tabletop Rearrangement in Bounded Workspace](https://mlsanity.com/paper/2110.12325/) (2021)
- [On Minimizing the Number of Running Buffers for Tabletop Rearrangement](https://mlsanity.com/paper/2105.06357/) (2021)
- [Learning to Efficiently Plan Robust Frictional Multi-Object Grasps](https://mlsanity.com/paper/2210.07420/) (2022)
- [Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior](https://mlsanity.com/paper/2402.15402/) (2024)
- [Prioritized Planning for Target-Oriented Manipulation via Hierarchical Stacking Relationship Prediction](https://mlsanity.com/paper/2303.07828/) (2023)
- [Minimizing Running Buffers for Tabletop Object Rearrangement: Complexity, Fast Algorithms, and Applications](https://mlsanity.com/paper/2304.01764/) (2023)
- [The Busboy Problem: Efficient Tableware Decluttering Using Consolidation and Multi-Object Grasps](https://mlsanity.com/paper/2307.03882/) (2023)
- [Toward Optimal Tabletop Rearrangement with Multiple Manipulation Primitives](https://mlsanity.com/paper/2310.00167/) (2023)
- [ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A Star](https://mlsanity.com/paper/2309.13707/) (2023)
- [Adversarial Object Rearrangement in Constrained Environments with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2309.15378/) (2023)
- [Multi-Stage Monte Carlo Tree Search for Non-Monotone Object Rearrangement Planning in Narrow Confined Environments](https://mlsanity.com/paper/2305.17175/) (2023)
- [Toward Holistic Planning and Control Optimization for Dual-Arm Rearrangement](https://mlsanity.com/paper/2404.06758/) (2024)
- [Task Planning for Object Rearrangement in Multi-room Environments](https://mlsanity.com/paper/2406.00451/) (2024)
- [Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes](https://mlsanity.com/paper/2409.17720/) (2024)
- [Planning for Tabletop Object Rearrangement](https://mlsanity.com/paper/2411.10899/) (2024)
- [Neural Rearrangement Planning for Object Retrieval from Confined Spaces Perceivable by Robot's In-hand RGB-D Sensor](https://mlsanity.com/paper/2402.06976/) (2024)
- [Tidiness Score-Guided Monte Carlo Tree Search for Visual Tabletop Rearrangement](https://mlsanity.com/paper/2502.17235/) (2025)

## Abstract (arXiv)

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.