正文： 梅开四度， 国内领先通用具身智能企业[极佳视界]完成亿元级A1轮融资！ 近日，Physical AI（物理AI）领域头部创企 [极佳视界 ]宣布完成 新一轮亿元级A1轮融资，本轮融资由华为哈 勃、华控基金联合投资 。 值的注意的是，公司于8月28日刚完成Pre-A、Pre-A+两轮数亿元融资，其中 Pre-A轮融资由国中资本领投，紫峰 资本、老股东 PKSHA Algorithm Fund跟投；Pre-A+轮融资由中金资本、广州产投、一村淞灵、华强资本投资； 以及于今年2月份完成由 普超资本、合鼎共资本、上海天使会投资联合投资的 数千万天使++轮融资。 温馨提示 ： 点击下方图片，查看运营团队最新原创报告（共235页） 说明： 欢迎约稿、刊例合作、行业交流 ， 行业交流记得先加入 "机器人头条"知识星球 ，后添加（ 微信号：lietou100w ）微 信； 若有侵权、改稿请联系编辑运营（微信：li_sir_2020）； 有关科技大厂入局具身赛道（大模型赋能、投资和自研）更多详细梳理、解读，已放到知识星 球"机器人头条"（点击后方链接，加入星球查看） ： 【 原创】多家顶尖科技大厂，进军人形机器人整机制 ...

Core Viewpoint - The roundtable forum highlighted the importance of funding and data in advancing embodied intelligence, with participants discussing various strategies for utilizing a hypothetical budget of 10 billion yuan to drive development in the field [1][53]. Group 1: Funding and Investment Strategies - Participants expressed differing opinions on how to allocate 10 billion yuan for the advancement of embodied intelligence, with suggestions including investing in research institutions and building data engines [1][54][56]. - The CEO of Accelerated Evolution emphasized the need for collaboration, suggesting that 10 billion yuan may not be sufficient without partnerships [1][53]. - The focus on creating the largest self-evolving data flywheel was proposed as a key investment area [54]. Group 2: Data Challenges and Solutions - A significant discussion point was the scarcity of data, with varying opinions on the importance of real-world data versus synthetic data [2][29]. - The emphasis was placed on the necessity of high-quality, diverse data collected from real-world scenarios to enhance model training [30][32][36]. - The use of simulation data was also highlighted as a means to accelerate the development of embodied intelligence before sufficient real-world data can be gathered [43][44]. Group 3: World Models and Predictive Capabilities - The forum participants agreed on the critical role of world models in embodied intelligence, particularly in enabling robots to predict and plan actions based on future goals [5][12]. - There was a consensus that training data for these models should primarily come from the robots themselves to ensure relevance and effectiveness [5][12]. - The discussion included the potential for a unified architecture in embodied intelligence models, contrasting with the current fragmented approaches [7][15][27]. Group 4: First Principles and Decision-Making - Participants shared their foundational principles guiding decision-making in the development of embodied intelligence, emphasizing the importance of data scale and quality [48][49][51]. - The need for a physical world foundation model that accurately represents complex physical interactions was highlighted as essential for future advancements [26][27]. - The concept of a closed-loop model for embodied intelligence was proposed, contrasting with the open-loop nature of current language models [10][11].

Core Viewpoint - Xiaomi is making significant investments in intelligent driving technology, focusing on safety, comfort, and efficiency, with safety being the top priority in their development strategy [4][7]. Development Progress - Xiaomi's intelligent driving has progressed through several versions: from high-precision maps for highway NOA (version 24.3) to urban NOA (version 24.5), and moving towards light map and no map versions (version 24.10) [7]. - The company is advancing through three stages of intelligent driving: 1.0 (rule-driven), 2.0 (data-driven), and 3.0 (cognitive-driven), with a focus on VLA (Vision Language Architecture) for the next production phase [7][10]. World Model Features - The world model introduced by Xiaomi has three essential characteristics: diversity in generated scenarios, multimodal input and output, and interactive capabilities that influence vehicle behavior [8][9]. - The world model is designed to enhance model performance through cloud-based data generation, closed-loop simulation, and reinforcement learning, rather than direct action outputs from the vehicle [10]. VLA and Learning Models - VLA is described as an enhancement over end-to-end learning, integrating high-level human knowledge (traffic rules, values) into the driving model [13]. - Xiaomi's development roadmap includes various model training stages, from LLM pre-training to embodied pre-training, with recent advancements in MiMo and MiMo-vl models [13]. Community and Knowledge Sharing - The "Automated Driving Heart Knowledge Sphere" community aims to provide a comprehensive platform for learning and sharing knowledge in the field of autonomous driving, with over 4,000 members and plans to expand [15][26]. - The community offers resources such as technical routes, video tutorials, and Q&A sessions to assist both beginners and advanced learners in the autonomous driving sector [27][30].

直击AI新时代下涌现的产业革命。36氪旗下账号。 以下文章来源于智能涌现 ，作者富充 智能涌现 . 即便在国内顶尖从业者之间，非共识依然存在。不同的回答折射出每位创业者心目中的"第一性原理"与战略重心。 文 ｜ 富充 编辑 ｜ 苏建勋 来源｜ 智能涌现（ID：AIEmergence） 封面来源 ｜ 智源研究院 "如果给你的企业100亿元来推进具身智能的发展，这笔钱你会怎么花？" 在11月20日举行的2025智源具身Open Day圆桌论坛上，主持人抛出了这样一个开放性问题。 面对这个问题的嘉宾，来自8家国内具身行业的顶流企业机构： 智源研究院院长王仲远 智元机器人合伙人、首席科学家罗剑岚 北京大学助理教授、银河通用创始人王鹤 清华大学交叉信息学院助理教授、星海图联合创始人赵行 加速进化创始人兼CEO程昊 自变量创始人兼CEO王潜 招商局集团AI首席科学家张家兴 中国科学院大学教授赵冬斌 "我觉得100亿元不太够。"加速进化创始人兼CEO程昊笑着回应道，观众席也发出一阵默契的笑声，"如果只有100亿，应该会找更多朋友一起推动具身行 业。比如把钱投到智源研究院。" 智元机器人合伙人罗剑岚倾向于用这笔钱解决当前的数 ...

Core Viewpoint - The article discusses the limitations of current AI language models, emphasizing that while they are advanced in processing language, they lack true understanding of the physical world, which is essential for achieving genuine intelligence [5][6][7]. Group 1: Limitations of Current AI Models - Current AI language models, like ChatGPT and Google's Gemini, excel at predicting the next word based on statistical patterns but fail to understand basic physical concepts [6][7]. - The analogy of a scholar in a dark room illustrates that while these models can generate coherent text, they lack real-world experience and understanding [7][13]. - AI's reliance on language statistics rather than physical interactions leads to nonsensical outputs, highlighting the need for a deeper understanding of the world [8][13]. Group 2: The Concept of Spatial Intelligence - To advance AI, it is crucial to develop "spatial intelligence," which involves understanding and interacting with the physical world without relying solely on language [8][14]. - The article posits that true intelligence requires the ability to predict physical interactions and outcomes, akin to how humans learn through experience [14][15]. - Examples from child development and scientific discovery illustrate how spatial interactions lead to a deeper understanding of cause and effect [9][11]. Group 3: Future Directions for AI - The future of AI may shift from predicting the next word to predicting the next frame of the world, integrating physical laws and spatial reasoning [14][17]. - Developing a "world model" that incorporates spatial data and physical interactions could revolutionize AI capabilities, allowing for more accurate simulations and predictions [15][17]. - The article mentions ongoing efforts to extract spatial information from 2D videos to train AI models, indicating a significant area of research [17][18]. Group 4: Practical Applications and Opportunities - The emergence of AI with spatial intelligence could lead to practical applications in robotics, enhancing their ability to navigate and interact with real-world environments [20][21]. - Potential use cases include virtual scene generation for design, therapy, and educational purposes, showcasing the versatility of AI in various fields [21][22]. - The ability to convert imagination into tangible reality presents significant opportunities for innovation and entrepreneurship [22][23].

11月23日，雷军发文总结小米端到端辅助驾驶HAD增强版的升级点。纵向加减速更舒适，旁车加塞时 可提前预判减速，及时跟车提速，行车更舒适安全。横向变道更丝滑，在变道并线、借道绕行时表现更 自然流畅。路况理解能力提升，在多车道复杂大路口能提前看懂导航信息，优化走对路、选对道的能 力。 此外，雷军还强调，辅助驾驶不是自动驾驶，驾驶时仍需时刻保持专注。此前在11月21日2025广州车展 开幕日，小米汽车端到端辅助驾驶"Xiaomi HAD增强版"正式发布，其在1000万Clips版本基础上引入"强 化学习"与"世界模型"，AEB防碰撞辅助升级，新增紧急转向辅助。 ...

IT之家 11 月 23 日消息，小米创办人、董事长兼 CEO 雷军今日发文，总结了小米端到端辅助驾驶 HAD 增强版的升级点。 纵向加减速更舒适，旁车加塞时提前预判减速，及时跟车提速行车更舒适安全。 横向变道更丝滑，变道并线、借道绕行时，更丝滑、不犹豫。 路况理解更充分，多车道的复杂大路口，提前看懂导航信息，优化走对路、选对道能力。 雷军也再次提醒：辅助驾驶不是自动驾驶，驾驶时仍需时刻保持专注。 据IT之家此前报道，在 11 月 21 日 2025 广州车展开幕日当天，小米汽车端到端辅助驾驶"Xiaomi HAD 增强版"正式发布，其在 1000 万 Clips 版本的基础上 引入了「强化学习」与「世界模型」，同时 AEB 防碰撞辅助升级，并新增紧急转向辅助。 车道保持辅助 - 预警 车道保持辅助 - 纠偏 紧急车道保持 盲区监测预警 车门开启预警 变道辅助预警 其他安全能力 超速告警 红绿灯提醒 自适应防眩目矩阵3 辅助驾驶不是自动驾驶，驾驶仍需时刻保持 侧向安全能力 ...

广州车展首日，雷军没有出现在小米汽车的发布会上。 作者 | 周智宇 编辑 | 张晓玲 站在台前的，是小米汽车副总裁李肖爽。除了技术迭新，李肖爽演讲的核心只有一个：安全。屏幕上， 是不加小字注释的三行大字——安全是前提，安全是基础，安全是一切。 营销味突然淡去，将安全牌打上牌桌。今年深陷舆论漩涡的小米，正在发生一些微妙的变化。 广州车展上，李肖爽没有谈论那些锦上添花的功能，重点展示的Xiaomi HAD增强版以及安全辅助功 能。 在智能驾驶的上半场，行业普遍采用"规则驱动"或基础的"数据驱动"。这就像是让司机死记硬背交规， 或者通过模仿记忆来开车。但这种模式很快遇到了天花板：面对极端稀缺的场景，由于缺少训练样本， 模型无法有效学习。 小米此次打出的"世界模型"，本质上是构建了一个高保真的虚拟仿真引擎。在这个虚拟世界里，系统不 再依赖现实中必须发生的事故来学习，而是可以通过"日夜兼程"的模拟演练，在海量生成的场景中试错 ——"走对了加分、走错了扣分"。 这种从"模仿"到"认知"的范式转移，目的是试图通过算法的泛化能力，去覆盖那些长尾的风险场景，从 而降低系统性的行车风险。 此次小米HAD增强版AEB也全面升级，A ...

机器人前瞻11月20日报道，在今天举行的2025智源具身Open Day上，智源研究院系统性公开了其在具身智能方向的最新研究进展，并举办了 围绕行业核心问题的圆桌讨论。 机器人前瞻（公众号：robot_pro） 作者 | 江宇 编辑 | 漠影 在现场，圆桌讨论从"世界模型是不是实现具身智能的关键"展开，随后延伸到"具身智能需不需要自己的统一架构、要不要有一套'具身版 Transformer'"。在数据层面，嘉宾们又讨论了在数据又重要又难的前提下，真实数据、仿真数据和视频数据该怎么组合使用。 第二场圆桌则进一步提出"人形机器人是不是具身智能的最终形态、硬件是不是现在最大的瓶颈"的问题。 大咖云集的圆桌讨论把业内当下关键与现实的议题都摆上了桌面。许多嘉宾在多个核心问题上给出了清晰、直接的判断，分歧与共识交织出 现。 一、智源的全栈布局：从世界模型到跨本体"具身大脑" 在开场演讲中，智源研究院院长王仲远系统介绍了过去一年在具身智能方向的多项关键进展，他将其概括为两条主线：世界模型的突破与具身 大脑全栈体系的成型。 首先，智源发布了原生多模态世界模型Emu3.5。相较上一代Emu3，新模型将训练数据从15年视频扩展至 ...

Core Insights - Xiaomi Auto officially launched the Xiaomi HAD Enhanced Version at the Guangzhou Auto Show, showcasing advancements in smart driving technology and talent acquisition in the AI field [1] - The company plans to invest over 7 billion yuan in AI research and development by 2025, with a current team of 1,800 experts, including 108 PhDs [1] Technical Developments - The new Xiaomi HAD Enhanced Version is built on a foundation of 10 million clips and incorporates reinforcement learning algorithms and world models to enhance driving performance [1] - The world model technology allows the system to simulate various scenarios, including extreme weather and complex road conditions, transitioning from a "rule-driven" to a "learning-driven" approach [1] User Experience Enhancements - The updated version focuses on optimizing longitudinal and lateral control experiences, particularly in scenarios like lane merging, reducing unnecessary deceleration and hard braking [2] - Significant upgrades to active safety features include the introduction of the AES emergency steering assist function, which can automatically change lanes to avoid collisions at speeds between 80 km/h and 135 km/h [2] Safety Features Expansion - The forward AEB (Automatic Emergency Braking) range has been expanded to 1 km/h to 135 km/h, with new capabilities to recognize various obstacles [2] - The backward AEB covers reversing scenarios from 1 km/h to 30 km/h, with a focus on balancing sensitivity to ensure accurate stopping while minimizing false triggers [2] Software Updates - The driving updates will be included in the Xiaomi HyperOS 1.11.0 version, with rollout times varying by model due to review progress [2]