最新综述：从物理模拟器和世界模型中学习具身智能

Core Insights - 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 AI systems [4][6]. - It highlights the importance of a unified grading system for intelligent robots, which categorizes their capabilities from basic mechanical execution to advanced social intelligence [6][67]. Group 1: Embodied Intelligence and Robotics - Embodied intelligence is defined as the ability of robots to interact with the physical world, enabling perception, action, and cognition through physical feedback [6]. - The integration of physical simulators provides a controlled environment for training and evaluating robotic agents, while world models enhance the robots' internal representation of their environment for better prediction and decision-making [4][6]. - The article maintains a resource repository of the latest literature and open-source projects to support the development of embodied AI systems [4]. Group 2: Grading System for Intelligent Robots - The proposed grading model includes five progressive levels (IR-L0 to IR-L4), assessing autonomy, task handling, and social interaction capabilities [6][67]. - Each level reflects the robot's ability to perform tasks, from complete reliance on human control (IR-L0) to fully autonomous social intelligence (IR-L4) [6][67]. - The grading system aims to provide a unified framework for evaluating and guiding the development of intelligent robots [6][67]. Group 3: Physical Simulators and World Models - Physical simulators like Isaac Sim utilize GPU acceleration for high-fidelity simulations, addressing data collection costs and safety issues [67]. - World models, such as diffusion models, enable internal representation for predictive planning, bridging the gap between simulation and real-world deployment [67]. - The article discusses the complementary roles of simulators and world models in enhancing robotic capabilities and operational safety [67]. Group 4: Future Directions and Challenges - The future of embodied intelligence involves developing structured world models that integrate machine learning and AI to improve adaptability and generalization [68]. - Key challenges include high-dimensional perception, causal reasoning, and real-time processing, which need to be addressed for effective deployment in complex environments [68]. - The article suggests that advancements in 3D structured modeling and multimodal integration will be critical for the next generation of intelligent agents [68].