HiCRISP: LLMを用いた階層的閉ループロボット自己修正プランナ
HiCRISP: An LLM-based Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner
LLMによるロボットタスク実行中に、高レベル計画と低レベル行動の誤りを階層的に監視・自己修正する閉ループ型プランニングフレームワークを提案し、仮想・実環境で有効性を示した。
著者: Chenlin Ming, Jiacheng Lin, Pangkit Fong, Han Wang, Xiaoming Duan, Jianping He
分類: cs.RO
原文アブストラクト
The integration of Large Language Models (LLMs) into robotics has revolutionized human-robot interactions and autonomous task planning. However, these systems are often unable to self-correct during the task execution, which hinders their adaptability in dynamic real-world environments. To address this issue, we present a Hierarchical Closed-loop Robotic Intelligent Self-correction Planner (HiCRISP), an innovative framework that enables robots to correct errors within individual steps during the task execution. HiCRISP actively monitors and adapts the task execution process, addressing both high-level planning and low-level action errors. Extensive benchmark experiments, encompassing virtual and real-world scenarios, showcase HiCRISP's exceptional performance, positioning it as a promising solution for robotic task planning with LLMs.
