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

Authors :
Gao, Zeyu
Mu, Yao
Qu, Jinye
Hu, Mengkang
Guo, Lingyue
Luo, Ping
Lu, Yanfeng
Publication Year :
2024

Abstract

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, effectively coordinating the two arms for complex long-horizon tasks remains a significant challenge. Existing task planning methods predominantly focus on single-arm robots or rely on predefined bimanual operations, 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 large language models (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 novel Dual-Arm Kitchen Benchmark, comprising 9 sequential tasks with 78 sub-tasks and 26 objects. Extensive experiments demonstrate the superiority of DAG-Plan over directly using LLM to generate plans, achieving nearly 50% higher efficiency compared to the single-arm task planning baseline and nearly double the success rate of the dual-arm task planning baseline.<br />Comment: 46 pages, 13 figures

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2406.09953
Document Type :
Working Paper