大規模言語モデルが生成するロボット計画の木を統合して信頼性を向上させる手法
Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability
LLMが生成した複数のタスク計画を木構造として統合し、疑わしい経路を除去して最適な計画を抽出することで、ロボットタスク計画の精度と実行効率を高める手法を提案した。
著者: Md Sadman Sakib, Yu Sun
分類: cs.RO, cs.AI, cs.CL
原文アブストラクト
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.
