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.

翻译：同步双臂重排作为工业应用中的常见场景被广泛研究。由于机械臂重排的计算复杂性和双臂规划的高维特性，该方法常面临可扩展性挑战。为应对这些挑战，我们将该问题建模为合作mTSP（一种智能体共享协作成本的mTSP变体），并利用强化学习进行求解。我们的方法包括：采用捕获空间关系的任务状态图表示重排任务，以及提供动作成本细节的协作成本矩阵。以这些表示为观测，我们设计了一种基于注意力机制的网络，有效整合它们以提供合理的任务调度。此外，还引入了成本预测器，在训练和规划阶段直接评估动作，显著加速了规划进程。实验结果表明，我们的方法在性能和规划效率方面均优于现有方法。