# Synchronized Dual-arm Rearrangement via Cooperative mTSP

arXiv: https://arxiv.org/abs/2403.08191 · HTML: https://mlsanity.com/paper/2403.08191/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2024.

## Built on by (most cited first, 3 of 3)

- [A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning](https://mlsanity.com/paper/2506.01628/) (2025)
- [CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem](https://mlsanity.com/paper/2603.19074/) (2026)
- [One-to-Two Acting: A Novel Framework for Single-arm Agent Action Expansion to Dual Arms](https://mlsanity.com/paper/2606.19897/) (2026)

## Abstract (arXiv)

Synchronized dual-arm rearrangement is widely studied as a common scenario in industrial applications. It often faces scalability challenges due to the computational complexity of robotic arm rearrangement and the high-dimensional nature of dual-arm planning. To address these challenges, we formulated the problem as cooperative mTSP, a variant of mTSP where agents share cooperative costs, and utilized reinforcement learning for its solution. Our approach involved representing rearrangement tasks using a task state graph that captured spatial relationships and a cooperative cost matrix that provided details about action costs. Taking these representations as observations, we designed an attention-based network to effectively combine them and provide rational task scheduling. Furthermore, a cost predictor is also introduced to directly evaluate actions during both training and planning, significantly expediting the planning process. Our experimental results demonstrate that our approach outperforms existing methods in terms of both performance and planning efficiency.