Dual-arm robots play a crucial role in improving efficiency and flexibility in complex multitasking scenarios.While existing methods have achieved promising results in task planning, they often fail to fully optimize task parallelism, limiting the potential of dual-arm collaboration.To address this issue, we propose RoboPARA, a novel large language model (LLM)-driven framework for dual-arm task parallelism planning.RoboPARA employs a two-stage process: (1) Dependency Graph-based Planning Candidates Generation, which constructs directed acyclic graphs (DAGs) to model task dependencies and eliminate redundancy, and (2) Graph Re-Traversal-based Dual-Arm Parallel Planning, which optimizes DAG traversal to maximize parallelism while maintaining task coherence.In addition, we introduce the Cross-Scenario Dual-Arm Parallel Task dataset (X-DAPT dataset), the first dataset specifically designed to evaluate dual-arm task parallelism across diverse scenarios and difficulty levels.Extensive experiments demonstrate that RoboPARA significantly outperforms existing planning methods, achieving higher efficiency and reliability, particularly in complex task combinations.Our code is publicly available at https://github.com/AiDuanshiying/RoboPARA.

翻译：双臂机器人在复杂多任务场景中对于提升效率与灵活性具有关键作用。尽管现有方法在任务规划方面已取得显著成果，但往往未能充分优化任务并行性，从而限制了双臂协作的潜力。为解决这一问题，我们提出RoboPARA——一种基于大语言模型（LLM）驱动的新型双臂任务并行规划框架。RoboPARA采用两阶段流程：（1）基于依赖图的规划候选生成，通过构建有向无环图（DAG）建模任务依赖关系并消除冗余；（2）基于图重遍历的双臂并行规划，通过优化DAG遍历在保持任务连贯性的同时最大化并行度。此外，我们提出了跨场景双臂并行任务数据集（X-DAPT数据集），这是首个专门用于评估不同场景与难度级别下双臂任务并行性能的数据集。大量实验表明，RoboPARA在效率与可靠性方面显著优于现有规划方法，尤其在复杂任务组合中表现突出。我们的代码已公开于https://github.com/AiDuanshiying/RoboPARA。