In the rapidly evolving field of robotics, Large Language Models (LLMs) like GPT-4V are making strides in improving robot task planning through advanced reasoning and language comprehension (Wang et al., 2024). While pure text-based LLMs face challenges in environments requiring embodied intelligence, the integration with multimodal systems opens new horizons for efficient robot performance in complex tasks.

• LLMs demonstrate exceptional natural language-based action planning.
• GPT-4V’s multimodal capabilities enhance robotic perception.
• Comprehensive overview of LLM use across robotic tasks.
• Introduction of a framework for LLM-centric embodied intelligence.
• Positive results from diverse datasets promise a new era in Human-Robot-Environment interaction.

The integration of LLMs in robotics is a pioneering step towards creating intelligent agents capable of nuanced interactions and complex problem-solving in real-world scenarios. It underscores the promise and potential of AI in enhancing human-robot collaboration.