Core Insights - Embodied intelligence is seen as a key pathway to achieving Artificial General Intelligence (AGI), enabling agents to develop a closed-loop system of "perception-decision-action" in real-world scenarios [1][2] - The article provides a comprehensive overview of the latest advancements in embodied intelligence powered by large models, focusing on how these models enhance autonomous decision-making and embodied learning [1][2] Group 1: Components and Operation of Embodied AI Systems - An Embodied AI system consists of two main parts: physical entities (like humanoid robots and smart vehicles) and agents that perform cognitive functions [4] - These systems interpret human intentions from language instructions, explore environments, perceive multimodal elements, and execute actions, mimicking human learning and problem-solving paradigms [4] - Agents utilize imitation learning from human demonstrations and reinforcement learning to optimize strategies based on feedback from their actions [4][6] Group 2: Decision-Making and Learning in Embodied Intelligence - The core of embodied intelligence is enabling agents to make autonomous decisions and learn new knowledge in dynamic environments [6] - Autonomous decision-making can be achieved through hierarchical paradigms that separate perception, planning, and execution, or through end-to-end paradigms that integrate these functions [6] - World models play a crucial role by simulating real-world reasoning spaces, allowing agents to experiment and accumulate experience [6] Group 3: Overview of Large Models - Large models, including large language models (LLMs), large vision models (LVMs), and vision-language-action (VLA) models, have made significant breakthroughs in architecture, data scale, and task complexity [7] - These models exhibit strong capabilities in perception, reasoning, and interaction, enhancing the overall performance of embodied intelligence systems [7] Group 4: Hierarchical Autonomous Decision-Making - Hierarchical decision-making structures involve perception, high-level planning, low-level execution, and feedback mechanisms [30] - Traditional methods face challenges in dynamic environments, but large models provide new paradigms for handling complex tasks by combining reasoning capabilities with physical execution [30] Group 5: End-to-End Autonomous Decision-Making - End-to-end decision-making has gained attention for directly mapping multimodal inputs to actions, often implemented through VLA models [55][56] - VLA models integrate perception, language understanding, planning, action execution, and feedback optimization into a unified framework, representing a breakthrough in embodied AI [58] Group 6: Enhancements and Challenges of VLA Models - VLA models face limitations such as sensitivity to visual and language input disturbances, reliance on 2D perception, and high computational costs [64] - Researchers propose enhancements in perception capabilities, trajectory action optimization, and training cost reduction to improve VLA performance in complex tasks [69][70][71]