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

Researchers introduce DAG-Plan, a novel task planning framework for dual-arm robots that uses Directed Acyclic Graphs to represent complex task dependencies and enable parallel execution. By leveraging LLMs as a single semantic parser rather than iterative query system, the approach achieves 48% higher success rates and 84% better efficiency than existing methods on benchmark testing.

DAG-Plan addresses a critical bottleneck in robotic task planning by reconceptualizing how AI systems coordinate dual-arm operations. The framework represents a meaningful shift from linear sequence generation toward graph-based representations that explicitly model task parallelism and dependencies. This architectural choice matters because it eliminates the false choice between efficiency and adaptability that plagued previous approaches—linear sequences execute quickly but fail to capture complex workflows, while iterative LLM querying adapts well but incurs prohibitive computational costs.

The technical innovation lies in using LLMs more strategically. Rather than querying language models repeatedly during execution, the system leverages their semantic understanding once to parse natural language into a structured DAG, then executes dynamically based on real-time observations. This hybrid approach preserves LLM strengths while mitigating their latency and cost limitations. The benchmark results—48% improvement over dual-arm linear methods and 84.1% efficiency gains over iterative approaches—suggest substantial practical advantages.

For robotics developers and AI researchers, DAG-Plan demonstrates that specialized data structures can unlock more efficient AI integration in embodied systems. The work validates that problem-specific representations matter more than raw model capability. As robots increasingly handle complex household and industrial tasks requiring coordination, frameworks that enable parallel execution without constant model querying become economically and technically essential. The open-sourced code and demonstrations position this as a potential standard for planning complex robotic workflows.

• →DAG-Plan uses Directed Acyclic Graphs to represent task dependencies, enabling parallel execution for dual-arm robots.
• →LLMs serve as single semantic parsers rather than iterative query systems, reducing latency and cost by 84% versus previous methods.
• →The framework achieves 48% higher success rates than linear sequence methods by robustly managing complex task dependencies.
• →Real-time environmental observations enable dynamic task assignment to suitable arms during execution.
• →Graph-based planning representations prove more effective than sequential or iterative approaches for complex robotic coordination.