用大模型提升双臂机器人任务并行效率
RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks
- 构建任务依赖图，消除冗余并行规划
- 跨场景数据集验证，复杂任务效率显著提升
- 适合机器人协作与智能调度研究者
双臂机器人在复杂多任务场景中对提升效率和灵活性至关重要。现有方法虽在任务规划上取得进展，但未能充分优化任务并行性，限制了双臂协作潜力。为此，我们提出RoboPARA——一种基于大语言模型的双臂任务并行规划框架。该框架采用两阶段设计：(1) 基于依赖图的任务候选生成，构建有向无环图（DAG）以建模任务依赖并消除冗余；(2) 基于图重遍历的双臂并行规划，优化DAG遍历路径以最大化并行性并保持任务连贯性。此外，我们构建了首个面向跨场景双臂并行任务评估的数据集X-DAPT，涵盖多种场景与难度层级。大量实验表明，RoboPARA显著优于现有规划方法，在复杂任务组合下展现出更高效率与可靠性。代码已公开于https://github.com/AiDuanshiying/RoboPARA。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。