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The integration of Large Language Models (LLMs) into robotics hasrevolutionized human-robot interactions and autonomous task planning. However,these systems are often unable to self-correct during the task execution, whichhinders their adaptability in dynamic real-world environments. To address thisissue, we present a Hierarchical Closed-loop Robotic IntelligentSelf-correction Planner (HiCRISP), an innovative framework that enables robotsto correct errors within individual steps during the task execution. HiCRISPactively monitors and adapts the task execution process, addressing bothhigh-level planning and low-level action errors. Extensive benchmarkexperiments, encompassing virtual and real-world scenarios, showcase HiCRISP'sexceptional performance, positioning it as a promising solution for robotictask planning with LLMs.

HiCRISP: A Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner