The article discusses the development of HiCRISP, a Hierarchical Closed-loop Robotic Intelligent Self-correction Planner. This innovative framework allows robots to self-correct errors during task execution, improving their adaptability in dynamic environments. It integrates Large Language Models (LLMs) into robotics, which has significantly improved human-robot interactions and autonomous task planning. However, current LLM-based robotic systems lack the ability to address deviations from task plans, which HiCRISP addresses. This development has the potential to push smart robot systems into new frontiers.
Publication date: 22 Sep 2023
Project Page: not provided
Paper: https://arxiv.org/pdf/2309.12089
