DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning

Dual-arm robots offer enhanced versatility and efficiency over single-arm counterparts by enabling concurrent manipulation of multiple objects or cooperative execution of tasks using both arms. However, the coordination of dual-arm systems for long-horizon tasks continues to pose significant challenges, stemming from the intricate temporal and spatial dependencies among sub-tasks, necessitating intelligent decisions regarding the allocation of actions between arms and their optimal execution order. Existing task planning methods predominantly focus on single-arm robots or rely on predefined bimanual operations to use large language models (LLMs) generate task sequence with linear temporal dependency, failing to fully leverage the capabilities of dual-arm systems. To address this limitation, we introduce DAG-Plan, a structured task planning framework tailored for dual-arm robots. DAG-Plan harnesses LLMs to decompose intricate tasks into actionable sub-tasks represented as nodes within a directed acyclic graph (DAG). Critically, DAG-Plan dynamically assigns these sub-tasks to the appropriate arm based on real-time environmental observations, enabling parallel and adaptive execution. We evaluate DAG-Plan on the Dual-Arm Kitchen Benchmark, comprising 5 sequential tasks with 44 sub-tasks. Extensive experiments demonstrate the superiority of DAG-Plan over directly using LLM to generate linear task sequence, achieving 52.8% higher efficiency compared to the single-arm task planning and 48% higher success rate of the dual-arm task planning. Compared to iterative methods, DAG-Plan improving execution efficiency 84.1% due to its fewer query time. More demos and information are available onthis https URL.

@article{gao2025_2406.09953,
  title={ DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning },
  author={ Zeyu Gao and Yao Mu and Jinye Qu and Mengkang Hu and Shijia Peng and Chengkai Hou and Lingyue Guo and Ping Luo and Shanghang Zhang and Yanfeng Lu },
  journal={arXiv preprint arXiv:2406.09953},
  year={ 2025 }
}