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

翻译：传统机器人任务规划方法在高度非结构化环境和复杂任务场景中面临挑战。我们提出了一种融合人类专业知识与大语言模型（LLM）的任务规划方法，并设计了一种具有更强表达能力的LLM提示模板Think_Net_Prompt，用于表示结构化专业知识。进一步地，我们提出了一种逐步分解任务并生成任务树的方法，以降低每个任务的规划复杂度，同时设计了机器人任务规划的解耦策略。通过划分不同的规划实体，并将任务与实际机器绑定过程相分离，使得任务规划过程更加灵活。研究结果表明，本方法在处理指定代码格式、理解任务与子任务之间的关系以及从文本描述中提取参数方面表现良好，但也存在任务逻辑处理复杂度有限、零件数量与装配精确定位模糊等问题。提升任务描述的精度与认知结构可带来一定改进。https://github.com/NOMIzy/Think_Net_Prompt