  In recent years, the integration of large language models (LLMs) has
revolutionized the field of robotics, enabling robots to communicate,
understand, and reason with human-like proficiency. This paper explores the
multifaceted impact of LLMs on robotics, addressing key challenges and
opportunities for leveraging these models across various domains. By
categorizing and analyzing LLM applications within core robotics elements --
communication, perception, planning, and control -- we aim to provide
actionable insights for researchers seeking to integrate LLMs into their
robotic systems. Our investigation focuses on LLMs developed post-GPT-3.5,
primarily in text-based modalities while also considering multimodal approaches
for perception and control. We offer comprehensive guidelines and examples for
prompt engineering, facilitating beginners' access to LLM-based robotics
solutions. Through tutorial-level examples and structured prompt construction,
we illustrate how LLM-guided enhancements can be seamlessly integrated into
robotics applications. This survey serves as a roadmap for researchers
navigating the evolving landscape of LLM-driven robotics, offering a
comprehensive overview and practical guidance for harnessing the power of
language models in robotics development.
