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The inherent probabilistic nature of Large Language Models (LLMs) introducesan element of unpredictability, raising concerns about potential discrepanciesin their output. This paper introduces an innovative approach aims to generatecorrect and optimal robotic task plans for diverse real-world demands andscenarios. LLMs have been used to generate task plans, but they are unreliableand may contain wrong, questionable, or high-cost steps. The proposed approachuses LLM to generate a number of task plans as trees and amalgamates them intoa graph by removing questionable paths. Then an optimal task tree can beretrieved to circumvent questionable and high-cost nodes, thereby improvingplanning accuracy and execution efficiency. The approach is further improved byincorporating a large knowledge network. Leveraging GPT-4 further, thehigh-level task plan is converted into a low-level Planning Domain DefinitionLanguage (PDDL) plan executable by a robot. Evaluation results highlight thesuperior accuracy and efficiency of our approach compared to previousmethodologies in the field of task planning.

Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability