Embodied Agent

Embodied agents are artificial intelligence systems situated in physical or simulated environments, aiming to bridge the gap between perception, reasoning, and action. Current research focuses on improving their ability to learn from experience, navigate complex scenarios, and follow natural language instructions, often employing large language models (LLMs) and reinforcement learning (RL) techniques, including methods like in-context learning and retrieval-augmented approaches. This field is significant for advancing robotics, human-robot interaction, and AI safety, as robust and adaptable embodied agents are crucial for deploying AI in real-world applications.