In this work, we introduce and formalize the Zero-Knowledge Task Planning (ZKTP) problem, i.e., formulating a sequence of actions to achieve some goal without task-specific knowledge. Additionally, we present a first investigation and approach for ZKTP that leverages a large language model (LLM) to decompose natural language instructions into subtasks and generate behavior trees (BTs) for execution. If errors arise during task execution, the approach also uses an LLM to adjust the BTs on-the-fly in a refinement loop. Experimental validation in the AI2-THOR simulator demonstrate our approach's effectiveness in improving overall task performance compared to alternative approaches that leverage task-specific knowledge. Our work demonstrates the potential of LLMs to effectively address several aspects of the ZKTP problem, providing a robust framework for automated behavior generation with no task-specific setup.

翻译：本文提出并形式化了零知识任务规划问题，即在缺乏任务特定知识的情况下，为达成目标制定动作序列。此外，我们针对该问题开展了首次探索性研究，提出一种利用大语言模型将自然语言指令分解为子任务、并生成行为树以供执行的方法。若任务执行过程中出现错误，该方法还可通过大语言模型在优化循环中实时调整行为树。在AI2-THOR模拟器中的实验验证表明，相较于依赖任务特定知识的替代方案，本方法能有效提升整体任务性能。本研究证明了大语言模型在解决零知识任务规划多个维度的潜力，为无需任务特定设置的自动化行为生成提供了稳健框架。