最新综述：从物理仿真和世界模型中学习具身智能

Core Viewpoint - The article focuses on the advancements in embodied intelligence within robotics, emphasizing the integration of physical simulators and world models as crucial for developing robust embodied intelligence [3][5]. Group 1: Embodied Intelligence and Robotics - Embodied intelligence is highlighted as a key area of research, emphasizing the importance of physical interaction with the environment for perception, action, and cognition [5]. - The article discusses the necessity for a scientific and reasonable grading system for robotic intelligence, especially in dynamic and uncertain environments [5][6]. - A proposed grading model for intelligent robots includes five progressive levels (IR-L0 to IR-L4), covering autonomy and task handling capabilities [6][10]. Group 2: Grading System for Intelligent Robots - The grading system categorizes robots based on their task execution capabilities, decision-making depth, interaction complexity, and ethical cognition [7][10]. - Key dimensions for grading include autonomy, task processing ability, environmental adaptability, and social cognition [11]. Group 3: Physical Simulators and World Models - The article reviews the complementary roles of physical simulators and world models in enhancing robot autonomy, adaptability, and generalization capabilities [3][72]. - A resource repository is maintained to provide comprehensive insights into the development of embodied AI systems and future challenges [3]. Group 4: Key Technologies and Trends - The advancements in robotics include the integration of various technologies such as model predictive control, reinforcement learning, and imitation learning to enhance robot capabilities [24][25]. - The article discusses the evolution of world models, which simulate real-world dynamics and improve the robustness of robotic systems [45][60]. Group 5: Future Directions and Challenges - Future directions include the development of structured world models, multi-modal integration, and lightweight models for efficient inference [73][72]. - The challenges faced by the industry include high-dimensional perception, causal reasoning, and real-time processing requirements [71][73].