（文章来源：21世纪经济报道） 地平线副总裁吕鹏在接受21世纪经济报道记者专访时，被问到"如果地平线推出VLA，会将现在积累的 端到端团队全盘不要吗？" 吕鹏给出了非常肯定的回答——不会。他认为，端到端永远是基座，没有好 的端到端，VLA也很难做好。 21世纪经济报道记者易思琳 ...

【东北计算机】20260128 【智元：VLA端侧推理性能提速15倍，并于精灵G2机器人完成真机验证】 3. 若交易达成，机器人流程自动化软件公司 AutomationAnyw 【东北计算机】20260128 【智元：VLA端侧推理性能提速15倍，并于精灵G2机器人完成真机验证】 1. 2035年人形机器人固态电池需求或超74GWh。 （来源：DoNews） 2. 智元：VLA端侧推理性能提速15倍，并于精灵G2机器人完成真机验证。 （来源：央视） 1. 2035年人形机器人固态电池需求或超74GWh。 （来源：DoNews） 2. 智元：VLA端侧推理性能提速15倍，并于精灵G2机器人完成真机验证。 （来源：央视） 3. 若交易达成，机器人流程自动化软件公司 AutomationAnywhere 将收购企业人工智能软件公司 C3.ai并借此实现上 市。 （来源：新浪） 4. LG能源将向特斯拉供应人形机器人用电池。 （来源：新浪） 5. 山东2026年力争机器人和智能装备产业规模破2000亿元。 （来源：中新网） 国产人形机器人每日公告 —————————————— —————————————— ————— ...

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 21号，轻舟首个基于单征程6M的城市NOA方案，已正式上车理想L系列智能焕新版。23号轻舟开了一场发布 会，里面技术的部分，给大家分享一下。 单J6M实现一段式端到端+强化学习，说实话是有点东西的。 和大家一起拆解下整体的网络架构： 以上的部分是一个常见的OneModel架构，下面是不一样的地方： 后续利用Safe RL（增加规则的判断）进一步优化自车轨迹。这一套架构整体上来说，其实不复杂，难的是在 J6M 128TOPS的算力上实现。第一时间就有人问柱哥这是不是真的。 DiffusionDrive和Flow Matching已经是多家公司验证过可量产的算法了。有两个算法也推荐一下，Diffusion Planner和Flow Planner，Flow Planner是Diffusion Planner的改进版本，是清华AIR詹仙园老师团队下面的工作。 轻舟也放了几个困难场景的demo。下图是L2实车的表现，严重错位道路和复杂路口的无保护左转，效果都很 不错。严重错位的道路很考验静态的基本功，不止是道路/车道 ...

Group 1 - The core viewpoint expressed by the CEO of Xiaopeng Motors is that the current challenges in the electric vehicle (EV) sales are temporary and the industry will recover in due time, indicating a positive outlook for the future [1] - The CEO emphasized that the worst times can also present the best opportunities, suggesting a resilient approach to the current market conditions [1] - Xiaopeng Motors is focusing on the production of its VLA, VLM, and humanoid robots, indicating a strategic shift towards innovation and new product lines to capitalize on future opportunities [1]

Core Viewpoint - The article discusses the evolution of the autonomous driving industry in 2025, highlighting the dual focus on technology proliferation and technical challenges, with traditional automakers pushing for accessibility and new players striving for technological advancements [4][5]. Group 1: Industry Trends - In 2025, traditional automakers like BYD, Geely, and Chery are leading the charge in making autonomous driving technology more accessible by integrating mid-level highway NOA features into vehicles priced over 100,000 yuan [4]. - New entrants and leading autonomous driving suppliers are focused on pushing the limits of technology, adhering to a model of annual technological iteration [4][5]. - The industry is witnessing a bifurcation, with one camp focused on accessibility and the other on technological challenges, particularly in the realm of algorithm development [4]. Group 2: Technological Advancements - The transition from "passive perception" to "active cognition" is marked by the introduction of world models, which represent a significant paradigm shift in autonomous driving technology [5][6]. - 2025 is characterized as a year of significant technological transition, with the widespread adoption of end-to-end systems and the emergence of world models and VLA (Vision-Language-Action) technologies [6][9]. - NIO is highlighted as a pioneer in the world model space, having launched its world model in 2024, transitioning from "perception-driven" to "cognition-driven" systems [5][6]. Group 3: Data Infrastructure and Chip Development - The importance of data infrastructure is emphasized, with companies like NIO benefiting from early investments in data collection and model training capabilities [7][8]. - The year 2025 is noted as a pivotal year for integrated hardware and software solutions, with companies like NIO and XPeng achieving self-developed chip integration [7][8]. - The article warns of the risks associated with outsourced chip development, contrasting it with NIO's genuine self-development efforts, which involve significant technical team investments [8]. Group 4: Regulatory and Market Dynamics - The issuance of L3 licenses is seen as a significant step towards the next phase of autonomous driving, indicating a shift from L2+ mass production to L3 and L4 capabilities [8][9]. - While traditional automakers have secured initial L3 licenses, their capabilities are questioned, suggesting that true advancements will come from new players and those with strong model capabilities [9][10]. - The ultimate value of autonomous driving technology is framed around enhancing driver convenience and significantly reducing traffic accidents, with a focus on safety as a primary goal [9].

Group 1 - The core achievement of GESON Technology in the 2025 Shenzhen Intelligent Robot Dexterous Hand Competition, winning the championship by setting a new limit for long-range delivery tasks, is supported by RoboSense's VLA model and advanced sensor systems [1][3][9] - The competition showcased the industry's leading capabilities in robot-eye coordination technology, emphasizing the commercial strength of creating industrial value through collaboration [3][9] - The event attracted 53 high-level teams from various regions, highlighting the significance of the competition in the context of the "robot mass production year" [9] Group 2 - RoboSense's recent video release demonstrated the robot's ability to perform complex tasks, indicating the integration of its core technologies aimed at flexible automation in delivery, manufacturing, and logistics [5][13] - The competition tested robots under real-world conditions, including human traffic and elevator sharing, emphasizing the challenges faced in the last 100 meters of delivery [11][13] - The success of GESON Technology illustrates the potential for RoboSense's AI robot technology to support autonomous completion of complex tasks, establishing a comprehensive technological barrier from foundational technology to application [11][13]

Core Insights - The robotics field is still in its early stages, as highlighted by Jim Fan, NVIDIA's robotics head, indicating a lack of standardized evaluation metrics and the disparity between hardware advancements and software reliability [1][8][11]. Group 1: Hardware and Software Disparity - Current advancements in robotics hardware, such as Optimus and e-Atlas, outpace software development, leading to underutilization of hardware capabilities [14][15]. - The need for extensive operational teams to manage robots is emphasized, as they do not self-repair and face frequent issues like overheating and motor failures [16][17]. - The reliability of hardware is crucial, as errors can lead to irreversible consequences, impacting the overall patience and scalability of the robotics field [18][19]. Group 2: Benchmarking Challenges - The lack of consensus on benchmarking in robotics is a significant issue, with no standardized hardware platforms or task definitions, leading to everyone claiming to achieve state-of-the-art (SOTA) results [20][21]. - The field must improve reproducibility and scientific standards to avoid treating them as secondary concerns [23]. Group 3: VLA Model Insights - The Vision-Language-Action (VLA) model is currently the dominant paradigm in robotics, but its reliance on pre-trained Vision-Language Models (VLM) presents challenges due to misalignment with physical world tasks [25][49]. - The VLA model's performance does not scale linearly with VLM parameters, as the pre-training objectives do not align with the requirements for physical interactions [26][51]. - Future VLA models should integrate physical-driven world models to enhance their ability to understand and interact with the physical environment [50]. Group 4: Data Importance - Data plays a critical role in shaping model capabilities, with the need for diverse data sources and collection methods being highlighted [31][43]. - The emergence of new hardware and data collection methods, such as Generalist and Egocentric-10K, demonstrates the growing importance of data in the robotics field [36][42]. - The current data collection strategies remain open-ended, with various approaches still being explored [43]. Group 5: Industry Trends - The robotics industry is projected to grow significantly, from $91 billion currently to $25 trillion by 2050, indicating a strong future potential [57]. - Major tech companies, excluding Microsoft and Anthropic, are increasingly investing in robotics software and hardware, reflecting the sector's attractiveness [59].

Core Viewpoint - The robotics field is still in its early stages, with significant advancements in hardware but limitations in software reliability and performance [1][12]. Group 1: Hardware and Software Dynamics - Current hardware advancements outpace software development, leading to reliability issues that hinder software iteration speed [11][14]. - Many demonstrations of robotic capabilities are often the result of selecting the best performance from numerous attempts, rather than consistent reliability [7][22]. - The need for extensive operational teams to manage robots highlights the challenges in hardware reliability, including overheating and motor failures [18][19]. Group 2: Benchmarking Challenges - The robotics sector lacks standardized benchmarks, making it difficult to assess performance consistently across different hardware platforms and tasks [21][22]. - The absence of consensus on evaluation criteria leads to a situation where every new demonstration can be considered state-of-the-art, complicating progress in the field [22][23]. Group 3: VLA Model Limitations - The Vision-Language-Action (VLA) model, currently a dominant paradigm, faces structural issues as it is primarily optimized for visual question answering rather than physical task execution [24][50]. - The performance of VLA models does not improve linearly with the increase in VLM parameters due to misalignment in pre-training objectives [26][52]. - A shift towards video world models is suggested as a more suitable pre-training target for robotics, as they inherently encode physical dynamics [27][53]. Group 4: Importance of Data - Data plays a crucial role in shaping model capabilities, and the integration of hardware and data is essential for effective robotic performance [31][32]. - Recent advancements in hardware, such as Figure03 and others, demonstrate improved motion capabilities, but challenges remain in enhancing hardware reliability [35][37]. - The Generalist model illustrates the scaling law in embodied intelligence, where larger datasets lead to better task performance [38][41]. Group 5: Future Trends and Market Potential - The robotics industry is projected to grow from $91 billion to $25 trillion by 2050, indicating significant investment potential [60]. - Major tech companies are increasingly investing in robotics software and hardware, reflecting the sector's attractiveness despite current challenges [62].

Core Insights - The new Weipai Blue Mountain Intelligent Advanced Edition is positioned as the first "six-seat plug-in hybrid SUV" equipped with the VLA large model, starting at a limited-time price of 275,800 yuan [1] - The vehicle features advanced capabilities such as voice control, CoT reasoning cards, defensive driving, and special scenario understanding, achieving an intelligent closed-loop from perception to execution [1] - The Hi4 performance version boasts a unique four-speed full-speed direct drive technology, achieving 0-100 km/h acceleration in 4.9 seconds, a low fuel consumption of 6.5L/100km, and a comprehensive range of 1,343 km [1] Group 1 - The VLA and Hi4 systems work in synergy to create a comprehensive "perception-decision-control" chain, enhancing safety and performance in various driving conditions [2] - The collaboration between VLA as the "navigator" and Hi4 as the "driver" results in a dual safety defense of "active avoidance" and "active stability," providing a superior travel experience [2] - The Coffee OS 3.4 system integrates AI services and user-friendly interactions, creating a "five good cabin" experience with features like 23 speakers and a 17.3-inch 3K entertainment screen [2] Group 2 - The vehicle includes innovative features such as AI multi-screen expansion and a motion-sickness relief display, demonstrating the practical value of technology in enhancing user comfort [2] - The "Little Wei Classmate" feature actively senses the environment and passengers, offering thoughtful collaborative services [2]

Core Viewpoint - The article discusses the rapid growth and market dynamics of urban NOA (Navigation on Autopilot) suppliers, highlighting the strategic investments and partnerships that are shaping the industry landscape [4][5]. Group 1: Investment and Market Position - Yuanrong has secured strategic investments from leading Tier 1 suppliers and luxury car manufacturers, indicating strong industry interest in high-quality urban NOA suppliers [4]. - Major players like Huawei, Yuanrong, and Momenta each hold over one million urban NOA project orders, suggesting a competitive market structure [5]. Group 2: Growth and Market Trends - Yuanrong has delivered 200,000 vehicles equipped with urban NOA, achieving a nearly 40% market share in the third-party supplier market by October 2025 [4]. - The urban NOA market is expected to experience significant growth, surpassing highway NOA as the mainstream solution due to the increasing adoption and technological advancements [4][6]. Group 3: Future Projections and Challenges - By 2026, urban NOA is projected to see a major surge in volume, driven by reduced hardware costs and the integration of intelligent driving in traditional fuel vehicles, potentially adding millions of units to the market [6]. - Achieving a production scale of over one million units will be a critical milestone for leading intelligent driving companies, as it will help establish data barriers and competitive advantages [6][7]. Group 4: Technological Evolution - The article emphasizes the importance of technological iteration, particularly the transition from VLA (Vehicle Level Automation) from initial production to significant performance improvements in 2026 [7]. - Companies must balance the need for cost-effective urban NOA solutions with advancements in cutting-edge technologies to remain competitive in the evolving market [8].