  Recent advancements in Large Language Models (LLMs) have sparked a revolution
across various research fields. In particular, the integration of common-sense
knowledge from LLMs into robot task and motion planning has been proven to be a
game-changer, elevating performance in terms of explainability and downstream
task efficiency to unprecedented heights. However, managing the vast knowledge
encapsulated within these large models has posed challenges, often resulting in
infeasible plans generated by LLM-based planning systems due to hallucinations
or missing domain information. To overcome these challenges and obtain even
greater planning feasibility and computational efficiency, we propose a novel
LLM-driven task planning approach called DELTA. For achieving better grounding
from environmental topology into actionable knowledge, DELTA leverages the
power of scene graphs as environment representations within LLMs, enabling the
fast generation of precise planning problem descriptions. For obtaining higher
planning performance, we use LLMs to decompose the long-term task goals into an
autoregressive sequence of sub-goals for an automated task planner to solve.
Our contribution enables a more efficient and fully automatic task planning
pipeline, achieving higher planning success rates and significantly shorter
planning times compared to the state of the art.
