  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.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Prompt
