We propose Synchronous Dual-Arm Rearrangement Planner (SDAR), a task and motion planning (TAMP) framework for tabletop rearrangement, where two robot arms equipped with 2-finger grippers must work together in close proximity to rearrange objects whose start and goal configurations are strongly entangled. To tackle such challenges, SDAR tightly knit together its dependency-driven task planner (SDAR-T) and synchronous dual-arm motion planner (SDAR-M), to intelligently sift through a large number of possible task and motion plans. Specifically, SDAR-T applies a simple yet effective strategy to decompose the global object dependency graph induced by the rearrangement task, to produce more optimal dual-arm task plans than solutions derived from optimal task plans for a single arm. Leveraging state-of-the-art GPU SIMD-based motion planning tools, SDAR-M employs a layered motion planning strategy to sift through many task plans for the best synchronous dual-arm motion plan while ensuring high levels of success rate. Comprehensive evaluation demonstrates that SDAR delivers a 100% success rate in solving complex, non-monotone, long-horizon tabletop rearrangement tasks with solution quality far exceeding the previous state-of-the-art. Experiments on two UR-5e arms further confirm SDAR directly and reliably transfers to robot hardware.

翻译：我们提出同步双臂重排规划器（SDAR），一种用于桌面重排的任务与运动规划（TAMP）框架。该框架针对两个配备二指夹爪的机械臂需在近距离协作、重排起始与目标位姿深度纠缠物体的场景。为应对此类挑战，SDAR将其依赖驱动的任务规划器（SDAR-T）与同步双臂运动规划器（SDAR-M）紧密集成，以智能筛选大量可能的任务与运动规划方案。具体而言，SDAR-T采用简洁高效的策略分解重排任务引发的全局物体依赖图，相比单臂最优任务规划方案，能生成更优的双臂任务规划。基于先进的GPU SIMD运动规划工具，SDAR-M采用分层运动规划策略，在确保高成功率的同时从众多任务规划中筛选最优同步双臂运动规划。综合评估表明，SDAR在求解复杂、非单调、长时域的桌面重排任务中实现100%成功率，其解质量远超现有最优方法。在两个UR-5e机械臂上的实验进一步证实SDAR可直接可靠地迁移至机器人硬件平台。