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.

翻译：大型语言模型（LLMs）与机器人的集成彻底革新了人机交互与自主任务规划。然而，这些系统在任务执行过程中往往无法进行自我校正，从而限制了其在动态真实环境中的适应性。为解决此问题，我们提出了一种分层闭环机器人智能自我校正规划器（HiCRISP）——一种创新框架，使机器人能在任务执行过程中纠正单个步骤中的错误。HiCRISP主动监控并调整任务执行流程，同时解决高层规划与低层动作错误。涵盖虚拟与真实场景的广泛基准实验展示了HiCRISP的卓越性能，将其定位为基于LLM的机器人任务规划中一种富有前景的解决方案。