Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures

Understand

Traditional robot task planning methods face challenges when dealing with highly unstructured environments and complex tasks.

• We propose a task planning method that combines human expertise with an LLM and have designed an LLM prompt template, Think_Net_Prompt, with stronger expressive power to represent structured professional knowledge.
• We further propose a method to progressively decompose tasks and generate a task tree to reduce the planning volume for each task, and we have designed a strategy to decouple robot task planning.
• By dividing different planning entities and separating the task from the actual machine binding process, the task planning process becomes more flexible.