作者 | 周智宇 编辑 | 张晓玲 2025年，全球智驾行业正经历一场范式转移。过去十年，自动驾驶是实验室里的代码游戏，是靠Demo 和PPT堆砌的幻梦；而现在，这门生意正式从虚空坠入实地，开始在财务报表上硬碰硬。 当曾经光环满身的L4独角兽因无法跨越规模生死线而陷入停摆，先行者们已经悄然扣响了盈利的大 门。2025年二季度，百度萝卜快跑在武汉实现收支平衡；11月，小马智行宣布其第七代Robotaxi在广州 实现单位经济模型（UE）转正。 小马智行联合创始人、CFO王皓俊在近期的采访中对华尔街见闻表示，能够在广州实现UE转正，意味 着小马智行在规模上量的过程中，逐渐打磨出一个标准的运营流程，能够赋能给小马智行的合作伙伴。 王皓俊认为，前几年Robotaxi的商业化还更多处于0到1的阶段，现在已经逐渐进入到了一个1到100、1 到1000的阶段。 一张清晰的商业化时间表已经浮出水面：从2025年底冲击千辆级车队，2026年提升至3000辆，到2030年 迈向10万辆规模，Robotaxi将成为人们日常生活的一部分。 商业闭环 这意味着，Robotaxi的竞争主战场已经转移。当单车硬件成本下探至25万人民币的生死 ...

Core Viewpoint - The article discusses the current state and future potential of embodied intelligence, focusing on the challenges and opportunities presented by world models and spatial intelligence in the field of robotics and AI [2][4][10]. Group 1: Development of Embodied Intelligence - The technology route for embodied intelligence is still in an exploratory phase, with no convergence yet, which is seen as a positive sign for innovation [4][3]. - There is a consensus among experts that the core issues of embodied intelligence, such as interaction and human-machine collaboration, should be addressed by academic institutions, while industries focus on practical applications [4][5]. - The integration of AI with physical entities is expected to lead to significant advancements in intelligence, but the field must avoid reverting to industrial automation without achieving generalized intelligence [4][5][30]. Group 2: World Models in Autonomous Driving - World models are currently being utilized by leading companies like Tesla to enhance data generation and improve decision-making processes through closed-loop testing [11][12]. - The concept of world models has gained traction in autonomous driving due to the simplicity of generating scenarios compared to robotics, with advancements in generative AI enabling the creation of realistic training samples [12][13]. - There is ongoing debate regarding the definition and application of world models in both autonomous driving and robotics, with differing opinions on the necessity of pixel-level reconstruction versus latent state representation [12][13][14]. Group 3: Spatial Intelligence in Robotics - Spatial intelligence is a critical aspect of robotics, with a focus on perception and understanding spatial relationships, which has evolved from traditional SLAM techniques to more learning-based approaches [20][21]. - The current challenges in spatial intelligence include the need for better data representation and understanding of complex spatial relationships, which are still underdeveloped in robotic systems [22][23]. - The integration of visual and semantic information is essential for enhancing robots' spatial capabilities, but the field is still in its early stages [22][23][24]. Group 4: Commercialization and Future Applications - The future of drone applications is expected to expand significantly, with potential uses in various sectors, but the timeline for widespread adoption remains uncertain [26][27]. - The gap between technological capabilities and market needs poses challenges for entrepreneurs, as there is often a mismatch between innovative ideas and practical industrial requirements [30][31]. - The shift towards learning-based control paradigms is anticipated to increase the applicability of drones and robots in real-world scenarios, moving beyond traditional automation [28][29].

Core Insights - The article discusses various advanced directions in autonomous driving research, emphasizing the importance of deep learning and traditional methods for different academic backgrounds [2][3]. Group 1: Research Directions - Key areas of focus include VLA, end-to-end learning, reinforcement learning, 3DGS, and world models, which are recommended for students in computer science and automation [2]. - For mechanical and vehicle engineering students, traditional methods like PnC and 3DGS are suggested due to their lower computational requirements and ease of entry [2]. Group 2: Paper Guidance Services - The article announces the launch of a paper guidance service that covers various topics such as end-to-end learning, multi-sensor fusion, and trajectory prediction [3][6]. - The service includes support for topic selection, full process guidance, and experimental assistance [6]. Group 3: Publication Success - The guidance service has a high acceptance rate for papers submitted to top conferences and journals, including CVPR, AAAI, and ICLR [7]. - The article highlights the range of publication venues, including CCF-A, CCF-B, and various SCI categories [10].

3DGenR1团队 投稿 量子位 | 公众号 QbitAI 在大语言模型和文生图领域，强化学习 （RL） 已成为提升模型思维链与生成质量的关键方法。 但当我们将目光转向更为复杂的文本到3D生成时，这套方法还会还管用吗？ 近期，一项由 西北工业大学、北京大学、香港中文大学、上海人工智能实验室、香港科技大学合作 开展 的研究系统性探索了这一重要问 题。 论文链接： https://arxiv.org/pdf/2512.10949 代码链接： https://github.com/Ivan-Tang-3D/3DGen-R1 强化学习是否能够用于Text-to-3D生成，以加强3D自回归模型的逐步推理与生成过程？ 在LLM推理和2D文生图中，RL已经证明可以显著提升CoT推理能力和生成质量。但 3D物体更长、更稠密、更具几何约束 。 因此相关方向研究常面临这几个问题： Progressive Investigation：四个层次拆解Text-to-3D+RL 1. Reward设计层 1. 奖励如何同时刻画语义对齐、几何一致性和视觉质量？ 2. 现有RL算法是否适合自回归式3D生成？ 3. 缺乏专门考察"3D推理能力 ...

Core Viewpoint - Rohit Prasad, the Senior Vice President and Chief Scientist of Amazon's AGI team, has announced his departure, marking a significant leadership change in Amazon's AI initiatives [1][3][4]. Group 1: Leadership Changes - Rohit Prasad joined Amazon in 2013 and played a crucial role in developing Alexa and leading the Nova foundational model project [3][4]. - Following Prasad's exit, Amazon will centralize AI research under the cloud computing division (AWS), with Peter DeSantis appointed to lead a new organization that will report directly to CEO Andy Jassy [5][6]. Group 2: AI Development Focus - Amazon aims to enhance its AI product development to compete with OpenAI, Google, and Anthropic, having launched its own foundational model series, Nova, and developed custom AI chips, Trainium, to rival Nvidia [5]. - The new department led by Peter DeSantis will oversee the development of core models, support for self-developed chip initiatives, and exploration of quantum computing technologies [10][12]. Group 3: New Appointments - Pieter Abbeel, a leading AI researcher and co-founder of Covariant, will take over the leadership of the foundational model research team, focusing on advancing Amazon's AI research [12][17]. - Abbeel's extensive background in AI and robotics positions him well to drive innovation and collaboration within Amazon's AI initiatives [12][15]. Group 4: Employment Perspectives - AWS CEO Matt Garman expressed confidence that AI will create more jobs than it displaces, emphasizing the importance of nurturing new talent to fill high-value roles in the future [19][20]. - Garman highlighted that junior developers, who are more adept at using AI tools, will play a crucial role in the evolving tech landscape, countering the notion that AI will replace entry-level positions [20].

Core Viewpoint - The article emphasizes the importance of end-to-end production in autonomous driving technology, highlighting the need for practical experience in various algorithms and applications to address real-world challenges in the industry [2][7]. Course Overview - The course is designed to provide in-depth knowledge on end-to-end production techniques, focusing on key algorithms such as one-stage and two-stage frameworks, reinforcement learning, and trajectory optimization [2][4]. - It includes practical projects that cover the entire process from theory to application, ensuring participants gain hands-on experience [2][12]. Instructor Background - The instructor, Wang Lu, is a top-tier algorithm expert with a strong academic background and extensive experience in developing and implementing advanced algorithms for autonomous driving [3]. Course Structure - The course consists of eight chapters, each focusing on different aspects of end-to-end algorithms, including: 1. Overview of end-to-end tasks and integration of perception and control systems [7]. 2. Two-stage end-to-end algorithm frameworks and their advantages [8]. 3. One-stage end-to-end algorithms with a focus on performance [9]. 4. Application of navigation information in autonomous driving [10]. 5. Introduction to reinforcement learning algorithms and training strategies [11]. 6. Optimization of trajectory outputs using various algorithms [12]. 7. Post-processing strategies for ensuring reliable outputs [13]. 8. Sharing of production experiences and strategies for real-world applications [14]. Target Audience - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, including familiarity with reinforcement learning and diffusion models [15][17].

DeepSeek-V3.2 与其同类模型的基准测试结果。 开源模型的三大痛点 要理解DeepSeek-V3.2的突破性，首先需要正视当前开源模型普遍面临的三大核心困境。 从 架构层面 看，传统开源模型大多依赖 标准注意力机制（vanilla attention） ，这种机制在处理长序列文本时，计算复杂度会随序列长度的平方增长 （O(L²)），不仅导致推理速度缓慢，更限制了模型在长上下文场景中的部署与后续训练优化。 点击下方 卡片 ，关注" 大模型之心Tech "公众号 戳我-> 领取大模型巨卷干货 在 大语言模型 （LLM）的发展赛道上，闭源与开源阵营的实力差距曾一度呈现扩大态势。随着OpenAI等巨头持续加码算力与数据投入，其闭源模型在 复杂推 理、工具使用 等核心能力上不断突破；而开源社区虽不乏创新尝试，但受限于架构效率、训练资源等多重因素，在高端任务场景中始终难以望其项背。这种不 平衡的发展格局，让业界对开源模型的上限充满疑虑——开源LLM是否注定只能成为闭源模型的"简化版替代品"？ 面对这一趋势，DeepSeek团队并未止步，而是通过系统性技术创新，推出了 DeepSeek-V3.2 。这款兼顾计算效 ...

Industry Insights - The Robo-X initiative is expected to reach a milestone in 2026, driven by supportive policies, technological advancements, and cost reductions in L4 autonomous driving [3][4] - The global L4 market is projected to exceed trillions by 2030, with the domestic Robotaxi market estimated at 236 billion yuan annually, and Robovan and Robotruck markets also showing significant potential [4][12] - The competitive landscape includes key players such as Pony.ai and WeRide in the Robotaxi sector, with various companies emerging in Robovan, Robotruck, Robobus, and Robosweeper markets [4] Company Analysis - Pony.ai reported a 72% year-on-year revenue growth in Q3, with ongoing progress in the commercialization of Robotaxi services [1][2] - WeRide achieved a remarkable 144% year-on-year revenue growth in Q3, indicating accelerated commercialization of its L4 products [2][1] Policy Developments - Global policies are increasingly supportive of autonomous driving, with countries like the UAE and Singapore implementing frameworks to facilitate the testing and deployment of autonomous vehicles [12][14] - In China, the Ministry of Industry and Information Technology has initiated pilot programs for smart connected vehicles, involving major automotive companies [14][15] Investment Trends - In 2025, the L4 sector is expected to attract significant investment, with over 49 financing events reported, totaling nearly 21.8 billion yuan in funding [16]

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 >> 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。 现有方法的不足 人形机器人需要精确的移动能力和灵巧的操作技能来完成具有挑战性的移动-操作任务。然而，现有的模块化或端到端方法在"操作感知型移动"方面存在不足。无法 通过规划和执行移动来主动创造操作所需的前提条件（如接近目标、调整姿态、保持稳定），而是将移动和操作视为独立阶段。 ★ 这使得机器人被限制在有限的工作空间内，难以完成大范围移动-操作任务。 ★ 核心挑战在于"操作感知型移动"：规划和执行能够主动创造操作前提条件（接近、定向、稳定）的移动，而非将移动和操作视为独立阶段。 一种朴素的解决方案是通过高层规划器序列化移动和操作，在不同技能间切换（如导航与抓取）。然而，有限的闭环反馈和缺乏端到端联合优化可能导致误差累 积，使机器人处于不利于后续操作的次优状态。另一种有前景的方案是端到端框架，直接执行全身控制以缓解模块化pipeline的切换问题，但通 ...

OpenAI大神姚顺雨，突然入职鹅厂，双重身份曝光，任首席AI科学家，同时兼任AI Infra部、大语言模型负责人。 今天，OpenAI科学家、清华校友姚顺雨入职腾讯，出任首席AI科学家！ 个人主页暂未更新 几个月前，全网一则关于姚顺雨去向的爆料，在AI圈掀起涟漪。 如今，这个被反复讨论却始终未被官方正式的消息，终于迎来了大结局。 有媒体报道，腾讯官方宣布，要对内部大模型研发体系，进行一次力度空前的架构升级，其中包括—— 新成立AI Infra部、AI Data部、数据计算平台部，试图从算力、数据到平台能力。 一切行动，就是为了全面夯实大模型「地基」。 与此同时，一直未正式露面的姚顺雨，也首次以官方身份亮相，担任两大职务—— 任CEO/总裁办公室首席AI科学家，向腾讯总裁刘炽平汇报； 兼任AI Infra部、大语言模型部负责人，向技术工程事业群总裁卢山汇报 这位AI天才的加入，将为中国大语言模型领域带来怎样的变革？ 清华姚班毕业，顶级学霸 姚顺雨本科毕业于清华大学，是姚班出身的典型「学霸」，学业生涯一路闪耀。 初中阶段，他就读于合肥45中，后升入合肥市第一中学。 2014年，他斩获「全国信息学奥林匹克竞赛」（ ...