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.

提出了一种名为 DELTA 的新型 LLM 驱动的任务规划方法，通过将环境拓扑图作为 LLM 中的环境表示，以快速生成精确的规划问题描述；同时利用 LLM 将长期任务目标分解为自回归的子目标序列，以供自动化任务规划器解决；该方法提供了更高效且完全自动化的任务规划流程，实现了比现有技术更高的规划成功率和显著缩短的规划时间。