以下文章来源于一见Auto ，作者易思琳 一见Auto . 汽车竞争中的野心、方法论与新秩序。21世纪经济报道旗下汽车报道品牌。 作者 | 易思琳 来源 | 见谈｜小米陈光：我们不想制造技术焦虑了 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 理想汽车智驾团队从端到端+世界模型全面切向VLA（Vision Language Action），在算法架构中引入大语言模型（LLM）。和理想一样坚定选择VLA的还 有智驾供应商元戎启行。 行业里也有坚定的VLA反对派。华为表示，不会走向VLA，而是会坚定选择WA（World Action，世界模型）。和华为一样尝试去掉Language环节的还有小 鹏。 而在这场争鸣中，端到端仍展现出巨大的潜力，小米汽车就是在这一方向持续深耕的企业。 "现在竞争太激烈，大家会产生一些焦虑，倾向于通过各种方式或技术让用户觉得更先进。"小米汽车端到端负责人陈光告诉《21汽车·一见Auto》， "但无 论VA、WA还是VLA，在我看来其实都一样，都 ...

文｜富充 编辑｜苏建勋 临近年底，一批具身智能公司开始交付产品，"机器人干活"又有了新场景。 12月25日，圣诞节当天，具身智能创业公司"星尘智能"就告诉《智能涌现》，他们开始与合作方"金马游乐"和"乐华娱乐"批量交付。此次交付的机器人， 正在北京朝阳合生汇、上海东方明珠广场、广州花城汇博纳影城，卖起了潮玩盲盒。 在这个名为"智能领养店"的零售车中，机器人独立完成从语音接待、下单收款、抓取盲盒、商品递送等一系列工作。 △北京朝阳合生汇的"智能领养店"前，顾客在体验，视频：采访人提供 据悉，星尘智能与金马游乐推出的零售店"机器人MART"，将陆续进入商圈、游乐场、街区、公园等场景。2025年11月，二者共同合作的"机器人 MART"已经在广东中山市时光奇遇游乐园开放，提供爆米花小食和饮品售卖服务。 星尘智能机器人之所以能够切入多样化场景，与他们的技术路线有关。 "绳驱本体"，是星尘智能的核心研发方向，其带来的动作灵活性和精细力控，让机器人可以快速拟人地完成抓取、盛装等细致手部操作。此外，因为绳驱 机器人重量更轻，而且关节具有柔性缓冲机制，能在发生意外接触时有效化解碰撞力，从而保障了人机交互的安全。 这种对绳驱机 ...

Core Viewpoint - The global video game industry is undergoing a transformative change due to the emergence of AI models capable of generating interactive 3D environments, with significant implications for the industry valued at tens of billions of dollars [1][2]. Group 1: AI Impact on Game Development - Leading AI teams, including Google DeepMind and World Labs, believe that "world models" will reshape the gaming industry [1]. - World Labs launched its first commercial product, Marble, which allows users to create coherent, high-fidelity 3D worlds from a single image, video, or text prompt [1]. - AI tools have already been used to enhance game development speed, with Game Gears' CEO reporting a fourfold increase in development speed for their game [2]. Group 2: Future of Gaming Experiences - AI is expected to empower creators and developers to produce content faster and in innovative ways, leading to new gaming experiences that do not currently exist [1][2]. - Players may soon be able to create entirely new game worlds, reducing reliance on expensive software and specialized skills [2]. - The introduction of AI-driven characters, such as the interactive Darth Vader in Fortnite, exemplifies the potential for AI to enhance player interaction [2]. Group 3: Industry Perspectives - Some industry experts express optimism that AI can lower costs, enhance creativity, and prevent developer burnout, especially in a sector where AAA games can take years and cost over $1 billion to develop [3]. - Critics, however, warn that increased AI usage may lead to the replacement of developers and artists, resulting in an influx of low-quality AI-generated content [2][3]. - Former Ubisoft producer emphasizes that world models could help developers regain the joy of creation and explore new ideas, especially under tight deadlines [4].

以下文章来源于具身智能之心 ，作者具身智能之心 >> 点击进入→ 具身智能之心 技术交流群 更多VLA与RL实战项目，欢迎加入国内首个工业级VLA实战课程 ： 具身VLA实战与求职教程来啦～ 。 原文链接：https://vedder.io/misc/state_of_robot_learning_dec_2025.html 这次来学习一下 PI 内部人员写的 blog，介绍了很多 robot learning 的现状，而且都是一线的真正经验，很多在一线的同学应该深有感 触，说了很多实话，质量很高，值得精读和学习。不管是对 IL DAgger RL 的看法都是很一手的经验。 接下来请享受这份知识 具身智能之心 . 与世界交互，更进一步。具身智能之心是国内具身与机器人领域的专业技术平台，集企业咨询、在线教育、展会服务、线下培 训、硬件研发、技术方案为一体。 点击下方 卡片 ，关注" 具身智能 之心 "公众号 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 基本上，目前（2025 年 12 月）所有机器人学习系统都是纯粹的行为克隆（BC，也称模仿学习）系统。人类提供（接近）最优的任务演 示，机器学习模 ...

大家好，我是张驰。感谢第一财经和一财号的邀请，非常荣幸能参加今年的《年度财经思想者盛典》。 今天我想和大家聊聊近年来备受关注的具身智能，也就是人形机器人或类人机器人领域。 从2025年春晚宇树科技的机器人舞蹈引发热潮，到今年北京举办全球首个人形机器人运动会，再到全国 各大城市纷纷开展人形机器人大赛，市场热度空前高涨。不少公司估值从年初的十几亿，迅速攀升到70 至100亿。2025年，多家企业融资超过3轮，累计融资金额在5到10亿元之间，市场热度可见一斑。 今天的分享主要包括三部分内容：梳理国内外人形机器人的发展现状、分析当前行业面临的问题，以及 展望未来的发展前景与投资机会。 首先，我们简要梳理一下国内外的主要参与者。在国外，特斯拉的擎天柱机器人和Figure AI是代表性企 业。国内方面，杭州宇树科技和上海智元机器人估值均已突破百亿，被视为第一梯队，上市进展也最 快。其他知名企业还包括北京星动纪元、银河通用、松延动力，杭州云深处、千寻智能，深圳逐际动 力、众擎、优必选，以及广州小鹏机器人等。据统计，2025年国内获得融资的人形机器人公司已超过 100家。 就行业发展阶段而言，目前国内公司仍以机器人本体研发为 ...

Core Viewpoint - The smart driving industry is experiencing a "term overload" phenomenon, with various factions emerging around different models such as VLA (Vision Language Action), VA (Vision Action), and WA (World Action) [2] Group 1: Industry Trends - The industry is divided between proponents of VLA, like Li Auto and Yuanrong Qixing, and opponents like Huawei and Xiaopeng, who prefer WA [2] - Xiaomi is focusing on end-to-end development, showcasing significant potential in this area, despite starting later than competitors like Li Auto and NIO [3][6] - Xiaomi's end-to-end algorithm has evolved rapidly, with multiple versions released within a year, indicating a fast-paced development cycle [6] Group 2: Technological Development - Xiaomi's latest version of its HAD (Highly Automated Driving) system incorporates world models and reinforcement learning, enhancing its cognitive capabilities [3][4] - The introduction of world models and reinforcement learning is seen as a necessary evolution from simple data-driven approaches to more complex cognitive-driven methodologies [9][10] - Xiaomi's approach emphasizes maximizing the model's intelligence density within limited computational resources [8][15] Group 3: Team Structure and Strategy - Xiaomi's smart driving team has grown to over 1,800 members, reflecting a rapid scaling compared to competitors [6][12] - The team is divided into three groups focusing on different technological routes, including end-to-end, VLA, and other exploratory research [4][13] - Xiaomi's strategy is characterized by a gradual introduction of new technologies, prioritizing user experience over merely adopting the latest advancements [5][10] Group 4: Challenges and Responses - The integration of reinforcement learning faces challenges, such as ensuring the fidelity of world models and managing computational efficiency [4][33] - Xiaomi's team has encountered external criticism, which they view as a necessary part of their growth and development process [25][26] - The company aims to balance the introduction of new technologies with the need for practical, user-friendly solutions [10][11]

Core Insights - The current technological routes in embodied intelligence, particularly the VLA model, have significant flaws in understanding the physical world and its laws [4][11] - Many companies are developing embodiments, but there is a lack of products that can truly understand the world and solve real problems [5] - In 2025, the domestic market is expected to see a surge in instant retail warehousing applications, which require 24/7 service, presenting an opportunity for robots to excel [5] Group 1: Company Strategy - The CEO of DaXiao Robotics, Wang Xiaogang, emphasizes a restrained approach by not entering the crowded embodiment market or betting on VLA, but instead focusing on the world model as a consensus direction in the industry [6][8] - DaXiao Robotics aims to integrate soft and hard solutions, addressing the shortcomings of existing technology routes, particularly the VLA model, which does not require a true understanding of the physical world [11][12] - The company’s world model consists of three parts: multi-modal understanding, long-term dynamic interaction scenes, and predictive capabilities, which are essential for the core of their technology [13] Group 2: Market Position and Opportunities - The industry is still maturing, and the head positioning has not been completed, with significant opportunities for new startups due to existing technological flaws [17] - The company sees a unique opportunity in the integration of hardware and software, leveraging its extensive client base from previous years to achieve rapid scaling in the robotics field [18] - Short-term goals include deploying four-legged robotic dogs with navigation and AI capabilities, while mid-term focus will be on commercial service scenarios like flash purchase warehouses [19] Group 3: Technological Differentiation - The ACE research paradigm proposed by DaXiao Robotics is seen as a revolutionary change that could provide a competitive edge in the market [18] - The world model approach is believed to be more adaptable and capable of covering a wider range of scenarios compared to VLA, which is limited by its embodiment [21] - The company plans to open-source its model to gather diverse feedback and data, differentiating its development path from other countries [22]

Core Viewpoint - The article discusses the distinction between world models and end-to-end models in autonomous driving, clarifying that world models are not a specific technology but rather a category of models with certain capabilities. It emphasizes the trend in the industry towards using world models for closed-loop simulation to address the high costs associated with corner cases in autonomous driving [2]. Course Overview - The course on world models in autonomous driving is structured into six chapters, covering the introduction, background knowledge, discussions on general world models, video generation-based models, OCC-based models, and job-related insights in the industry [5][6][7][8][9]. Chapter Summaries - **Chapter 1: Introduction to World Models** This chapter outlines the relationship between world models and end-to-end autonomous driving, discussing the development history and current applications of world models, as well as various streams such as pure simulation, simulation plus planning, and generating sensor inputs [5]. - **Chapter 2: Background Knowledge** This chapter covers foundational knowledge related to world models, including scene representation, Transformer technology, and BEV perception, which are crucial for understanding subsequent chapters [6]. - **Chapter 3: General World Models** Focuses on popular general world models like Marble from Li Fei-Fei's team and Genie 3 from DeepMind, discussing their core technologies and design philosophies [7]. - **Chapter 4: Video Generation-Based World Models** This chapter delves into video generation algorithms, starting with GAIA-1 & GAIA-2 and extending to recent works like UniScene and OpenDWM, highlighting both classic and cutting-edge advancements in this area [8]. - **Chapter 5: OCC-Based World Models** Concentrates on OCC generation algorithms, discussing three major papers and a practical project, emphasizing the potential for these methods to extend into vehicle trajectory planning [9]. - **Chapter 6: World Model Job Topics** This chapter shares practical insights from the instructor's experience, addressing industry applications, pain points, and interview preparation for positions related to world models [9]. Learning Outcomes - The course aims to provide a comprehensive understanding of world models in autonomous driving, equipping participants with the knowledge to achieve a level comparable to one year of experience as a world model algorithm engineer [10].

一水 发自 凹非寺 量子位 | 公众号 QbitAI 吵起来了。 图灵奖得主和诺贝尔奖得主，为了"智能的本质"——直接激情友好地交流上了。 AI三巨头之一、图灵奖得主Yann LeCun明确表示： 纯粹就是胡扯（complete BS）。 而诺贝尔奖得主、谷歌DeepMind CEO哈萨比斯也不留情面了，指名道姓回击： LeCun的说法简直是大错特错。 当然，马斯克的站队可能也有别的原因。毕竟他和LeCun素来不是很对付，跟哈萨比斯则亦师亦友——马斯克还是哈萨比斯DeepMind早期投 资人。 论战之激烈，关注度之高，已经让专门开辟了一个话题板块： 马斯克也跑来吃瓜了—— 没有任何多余的解释，但这波他站哈萨比斯——"Demis is right"。 事情还要从LeCun几天前接受的一场采访说起。 他在节目中言辞犀利地指出： 根本不存在所谓的"通用智能"，纯粹就是胡扯（complete BS） 。 这个概念毫无意义，因为它实际上是用来指代人类水平的智能，但人类智能其实是高度专业化的。我们在现实世界里确实干得不错，比 如认个路、导航blabla；也特别擅长跟人打交道，因为咱们进化了这么多年就是干这个的。 但在国际 ...

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 最近和业内专家jason老师讨论了很多，分享一个最近被问到很多的问题： 世界模型是不是端到端？以及如何看待世界模型最近爆发式的工作发表。 第一个问题的答案是明确的：不是。 世界模型和端到端都不指某个具体的技术，而是一类具备某些特定能力的模型。可以理解为 世界模型只是一种实现端到端自 动驾驶的途径。 早鸟优惠！开课即止~ 目前学术界和工业界把自动驾驶世界模型收敛到生成和重建两个领域，并且主流都在利用世界模型在做闭环仿真，所以我们看到了很多相关工作的推出。这也是业 内风格转换的一个趋势，Corner Case的成本过高，我们需要更有效的的其他手段...... 先前平台和Jason老师共同打造的《端到端与VLA自动驾驶小班课》备受大家好评，因此我们进一步推出这门世界模型小班课， 课程聚焦于通用世界模型、视频生 成、OCC生成等世界模型算法，涵盖特斯拉世界模型、李飞飞团队Marble等。欢迎大家加入学习~ 讲师介绍 Jason：C9本科+QS50 PhD，已发表CCF-A论文2篇，CCF-B论文若干。现任国内TOP主 ...