  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.
