# Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement

arXiv: https://arxiv.org/abs/2207.08078 · HTML: https://mlsanity.com/paper/2207.08078/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 12 of 12)

- [INVIGORATE: Interactive Visual Grounding and Grasping in Clutter](https://mlsanity.com/paper/2108.11092/) (2021)
- [Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation](https://mlsanity.com/paper/2003.10863/) (2020)
- [Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps](https://mlsanity.com/paper/1711.07369/) (2017)
- [Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search](https://mlsanity.com/paper/2101.12241/) (2021)
- [Fast High-Quality Tabletop Rearrangement in Bounded Workspace](https://mlsanity.com/paper/2110.12325/) (2021)
- [Persistent Homology for Effective Non-Prehensile Manipulation](https://mlsanity.com/paper/2202.02937/) (2022)
- [Synchronized Multi-Arm Rearrangement Guided by Mode Graphs with Capacity Constraints](https://mlsanity.com/paper/2005.09127/) (2020)
- [DIPN: Deep Interaction Prediction Network with Application to Clutter Removal](https://mlsanity.com/paper/2011.04692/) (2020)
- [Fast, High-Quality Dual-Arm Rearrangement in Synchronous, Monotone Tabletop Setups](https://mlsanity.com/paper/1810.12202/) (2018)
- [Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces](https://mlsanity.com/paper/2110.02814/) (2021)
- [Planning for target retrieval using a robotic manipulator in cluttered and occluded environments](https://mlsanity.com/paper/1907.03956/) (2019)
- [Coordination of two robotic manipulators for object retrieval in clutter](https://mlsanity.com/paper/2109.15220/) (2021)

## Built on by (most cited first, 5 of 5)

- [HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions](https://mlsanity.com/paper/2308.01477/) (2023)
- [ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A Star](https://mlsanity.com/paper/2309.13707/) (2023)
- [RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks](https://mlsanity.com/paper/2506.06683/) (2025)
- [Learning Dual-arm Object Rearrangement for Cartesian Robots](https://mlsanity.com/paper/2402.13634/) (2024)
- [MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments](https://mlsanity.com/paper/2306.06543/) (2023)

## Abstract (arXiv)

We investigate the problem of coordinating two robot arms to solve non-monotone tabletop multi-object rearrangement tasks. In a non-monotone rearrangement task, complex object-object dependencies exist that require moving some objects multiple times to solve an instance. In working with two arms in a large workspace, some objects must be handed off between the robots, which further complicates the planning process. For the challenging dual-arm tabletop rearrangement problem, we develop effective task planning algorithms for scheduling the pick-n-place sequence that can be properly distributed between the two arms. We show that, even without using a sophisticated motion planner, our method achieves significant time savings in comparison to greedy approaches and naive parallelization of single-robot plans.