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Traditional robot task planning methods face challenges when dealing withhighly unstructured environments and complex tasks. We propose a task planningmethod that combines human expertise with an LLM and have designed an LLMprompt template, Think_Net_Prompt, with stronger expressive power to representstructured professional knowledge. We further propose a method to progressivelydecompose tasks and generate a task tree to reduce the planning volume for eachtask, and we have designed a strategy to decouple robot task planning. Bydividing different planning entities and separating the task from the actualmachine binding process, the task planning process becomes more flexible.Research results show that our method performs well in handling specified codeformats, understanding the relationship between tasks and subtasks, andextracting parameters from text descriptions. However, there are also problemssuch as limited complexity of task logic handling, ambiguity in the quantity ofparts and the precise location of assembly. Improving the precision of taskdescription and cognitive structure can bring certain improvements.https://github.com/NOMIzy/Think_Net_Prompt

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