As robots become increasingly integrated into human environments, the ability to interact intuitively through natural language has become a crucial goal. Traditional robotic control systems require structured inputs and predefined behaviors, limiting their adaptability in dynamic, real world environments. Recent advances in Large Language Models (LLM) offer a new paradigm: harnessing language as a general interface for reasoning, perception, and decision making. However, integrating LLMs with embodied agents presents fundamental challenges, including grounding instructions in physical space, ensuring safety, and linking symbolic language and low-level robotic actions. This thesis explores the integration of Large Language Models (LLMs) into robotic systems for natural language-driven control and perception.