Core Insights - The article discusses the evolution of AI towards "world models," which enable AI to simulate and understand the world rather than just generate content. This shift is seen as a critical leap towards "general intelligence" [4][5][9]. Group 1: Definition and Importance of World Models - World models are defined as generative models that can simulate all scenarios, allowing AI to predict and make better decisions through internal simulations rather than relying solely on experience-based learning [15][18]. - The need for world models arises from their ability to construct agent models for better decision-making and to serve as environment models for offline reinforcement learning, enhancing generalization capabilities [18][22]. Group 2: Development and Applications - The development of world models has been rapid, with significant advancements since the 2018 paper "World Models," leading to the emergence of structured models capable of video generation [24][52]. - Key applications of world models include their use in autonomous driving, robotics, and drone technology, where they provide a foundational layer for general intelligence [9][75]. Group 3: Technical Approaches - Various technical approaches to world models are discussed, including explicit physical modeling and the use of generative models that focus on creating environments for reinforcement learning [29][40]. - The article highlights the importance of data collection, representation learning, and architecture improvements to enhance the capabilities of world models [69][71]. Group 4: Future Directions - Future improvements in world models are expected to focus on richer multimodal data collection, stronger representation learning, and the ability to adapt to various tasks and environments [69][70][73]. - The company claims to be the only team globally to have developed a "universal world model" that can be applied across different domains, including ground and aerial intelligent agents [75][81].

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Baining Zhao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。 人类空间感的重要组成部分之一，是对自身移动会产生的视觉观测变化的预期。这对于空间移动下的任务/动作决策至关重要。 因此，推演和想象是具身智能领域的基础问题之一，表现为预测：如果本体执行移动意图，那么具身观测将会如何变化。 现有世界模型的研究主要聚焦于人形机器人和自动驾驶应用，它们大多在二维平面上操作，动作空间有限。 具体而言，关键挑战包括： 为此，清华大学团队提出 AirScape ，专为六自由度（6DoF）空中具身智能体设计的生成式世界模型。 利用提出的 11k 视频-意图对数据集 ，对视频生成基础模型进行监督微调。这一阶段使模型获得对低空动作意图的基本理解和生成能力。 AirScape 能基于当前的低空视觉观测和动作意图，推演未来的序列观测。 项目的数据集和代码已全面开源。 低空世界模型数据集 为支撑低空世界 ...