Consolidating Trees of Robotic Plans Generated Using Large Language
  Models to Improve Reliability
Md Sadman Sakib Yu Sun
The inherent probabilistic nature of Large Language Models (LLMs) introduces
an element of unpredictability, raising concerns about potential discrepancies
in their output. This paper introduces an innovative approach aims to generate
correct and optimal robotic task plans for diverse real-world demands and
scenarios. LLMs have been used to generate task plans, but they are unreliable
and may contain wrong, questionable, or high-cost steps. The proposed approach
uses LLM to generate a number of task plans as trees and amalgamates them into
a graph by removing questionable paths. Then an optimal task tree can be
retrieved to circumvent questionable and high-cost nodes, thereby improving
planning accuracy and execution efficiency. The approach is further improved by
incorporating a large knowledge network. Leveraging GPT-4 further, the
high-level task plan is converted into a low-level Planning Domain Definition
Language (PDDL) plan executable by a robot. Evaluation results highlight the
superior accuracy and efficiency of our approach compared to previous
methodologies in the field of task planning.