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

Understand

Dual-arm robots promise greater efficiency but require planning for complex tasks with nonlinear sub-task dependencies.

• Current methods using Large Language Models (LLMs) suffer from a fundamental trade-off: generating linear sequences is efficient but fails to model parallelism and adapt to changes, while iterative querying is adaptive but too slow and costly.
• To bridge this gap, we introduce DAG-Plan, a novel task planning framework that for the first time employs a Directed Acyclic Graph (DAG) as the central representation for dual-arm coordination.
• The key insight is that a DAG natively captures complex sub-task dependencies and explicitly reveals opportunities for parallel execution.