  Large language models (LLMs) have undergone significant expansion and have
been increasingly integrated across various domains. Notably, in the realm of
robot task planning, LLMs harness their advanced reasoning and language
comprehension capabilities to formulate precise and efficient action plans
based on natural language instructions. However, for embodied tasks, where
robots interact with complex environments, text-only LLMs often face challenges
due to a lack of compatibility with robotic visual perception. This study
provides a comprehensive overview of the emerging integration of LLMs and
multimodal LLMs into various robotic tasks. Additionally, we propose a
framework that utilizes multimodal GPT-4V to enhance embodied task planning
through the combination of natural language instructions and robot visual
perceptions. Our results, based on diverse datasets, indicate that GPT-4V
effectively enhances robot performance in embodied tasks. This extensive survey
and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks
enriches the understanding of LLM-centric embodied intelligence and provides
forward-looking insights toward bridging the gap in Human-Robot-Environment
interaction.
