  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.
