  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.
