In the realm of data-driven AI technology, the application of open-source large language models (LLMs) in robotic task planning represents a significant milestone. Recent robotic task planning methods based on open-source LLMs typically leverage vast task planning datasets to enhance models' planning abilities. While these methods show promise, they struggle with complex long-horizon tasks, which require comprehending more context and generating longer action sequences. This paper addresses this limitation by proposing MLDT, theMulti-Level Decomposition Task planning method. This method innovatively decomposes tasks at the goal-level, task-level, and action-level to mitigate the challenge of complex long-horizon tasks. In order to enhance open-source LLMs' planning abilities, we introduce a goal-sensitive corpus generation method to create high-quality training data and conduct instruction tuning on the generated corpus. Since the complexity of the existing datasets is not high enough, we construct a more challenging dataset, LongTasks, to specifically evaluate planning ability on complex long-horizon tasks. We evaluate our method using various LLMs on four datasets in VirtualHome. Our results demonstrate a significant performance enhancement in robotic task planning, showcasing MLDT's effectiveness in overcoming the limitations of existing methods based on open-source LLMs as well as its practicality in complex, real-world scenarios.

翻译：在数据驱动的人工智能技术领域，基于开源大语言模型（LLMs）的机器人任务规划应用标志着重要里程碑。现有基于开源LLMs的机器人任务规划方法通常利用大规模任务规划数据集来增强模型的规划能力。尽管这些方法展现出潜力，但在处理需要理解更复杂上下文并生成更长动作序列的复杂长时域任务时仍存在局限。本文通过提出MLDT——多层级分解任务规划方法来解决这一局限性。该方法创新性地在目标层面、任务层面和动作层面进行任务分解，以缓解复杂长时域任务的挑战。为增强开源LLMs的规划能力，我们引入了一种目标敏感的语料生成方法以创建高质量训练数据，并对生成的语料进行指令微调。鉴于现有数据集复杂度不足，我们构建了更具挑战性的LongTasks数据集，专门评估复杂长时域任务的规划能力。我们在VirtualHome的四个数据集上使用多种LLMs评估了该方法，结果表明机器人任务规划性能显著提升，展现了MLDT在克服基于开源LLMs现有方法局限性方面的有效性，以及在复杂真实场景中的实用性。