Consolidating Trees of Robotic Plans Generated Using Large Language
Models to Improve Reliability
Abstract
The inherent probabilistic nature of Large Language Models (LLMs) introduces an
element of unpredictability, raising concerns about potential discrepancies in their out-
put. 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.
