Core Viewpoint - The article discusses the integration of World Model and Reinforcement Learning to enhance closed-loop simulation in autonomous driving, aiming to surpass human driving capabilities and improve safety and reliability [3]. Group 1: Limitations and Solutions - Traditional vehicle architectures hinder end-to-end training, leading to ineffective information transfer in reinforcement learning [5]. - The lack of realistic interactive environments has resulted in models that are prone to biases and inaccuracies due to insufficient scene realism and small-scale construction [5]. - The ideal solution combines real data 3D reconstruction with noise addition to train generative models, enhancing their ability to generate diverse scenes [5]. Group 2: DrivingSphere Framework - DrivingSphere is the first generative closed-loop simulation framework that integrates geometric prior information, creating a 4D world representation that combines static backgrounds and dynamic objects [8]. - The framework addresses issues of open-loop simulation lacking dynamic feedback and traditional closed-loop simulation's visual realism and data compatibility [10]. - DrivingSphere consists of three main modules: Dynamic Environment Composition, Visual Scene Synthesis, and Closed-Loop Feedback Mechanism [12]. Group 3: Dynamic Environment Composition - This module constructs a 4D driving world with static backgrounds and dynamic entities, utilizing the OccDreamer diffusion model and action dynamics management [13]. - The 4D world representation is stored in an occupancy grid format, allowing unified modeling of spatial layouts and dynamic agents [16]. Group 4: Visual Scene Synthesis - This module converts 4D occupancy data into high-fidelity multi-view videos, focusing on dual-path conditional encoding and ID-aware representation [19]. - The use of VQVAE for mapping 3D occupancy data enhances reconstruction accuracy through a combination of loss functions [20]. Group 5: Closed-Loop Feedback Mechanism - The closed-loop feedback mechanism enables real-time interaction between the autonomous driving agent and the simulated environment, facilitating a "agent action - environment response" cycle [23]. - This mechanism supports an iterative process of "simulation - testing - optimization," allowing for the identification and correction of algorithmic flaws [23].

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Xiaoxiao Long等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要 的。 出发点与工作背景 本综述聚焦具身智能在机器人研究中的前沿进展，指出实现强大具身智能的关键在于物理模拟器与世界模 型的整合。物理模拟器提供可控高保真环境用于训练评估机器人智能体，世界模型则赋予机器人环境内部 表征能力以支持预测规划与决策。 文中系统回顾了相关最新进展，分析了两者在增强机器人自主性、适应性和泛化能力上的互补作用，探讨 了外部模拟与内部建模的相互作用以弥合模拟训练与现实部署的差距。此外，还提及维护了一个包含最新 文献和开源项目的资源库，网址为https://github.com/NJU3DV-LoongGroup/Embodied-World-Models-Survey， 旨在为具身 AI 系统的发展提供全面视角并明确未来挑战。 一些介绍 随着人工智能与机器人技术的发展，智能体与物理世界的交互成为研 ...