作者 | QCon 全球软件开发大会 策划 | Kitty 编辑 | 宇琪 具身智能作为 AI 从数字世界迈向物理现实的核心跃迁，是通往 AGI 的关键路径，却依然受困于模型 泛化性不足、数据采集难、闭环难以实现等深层难题，真正的产业落地仍举步维艰。那么，具身智能 究竟卡在哪儿了？ 近日 InfoQ《极客有约》X QCon 直播栏目特别邀请 地瓜机器人算法副总裁隋伟博士 担任主持人， 和 地瓜机器人具身智能负责人何泳澔博士、乐享科技 CTO 李元庆、北京科技大学副教授彭君然博士 一起，在 2026 年 QCon 全球软件开发大会（ 北京站） 即将召开之际，共同探讨具身智能落地实战 中的卡点。 部分精彩观点如下： 在 4 月 16-18 日将于北京举办的 QCon 全球软件开发大会（北京站） 上，我们特别设置了 【具身智 能与物理世界交互】 专题。该专题将深度拆解具身智能技术链路，探讨模型现状、核心挑战与机会， 加速具身智能技术研发转化与产业规模化落地。查看大会日程解锁更多精彩内容： https://qcon.infoq.cn/2026/beijing/schedule 工业场景并不需要追求通用性，如果能将某个 ...

Core Viewpoint - The article discusses the competitive landscape of autonomous driving technology, highlighting the different methodologies and ambitions of various companies, particularly focusing on Xiaomi's approach to end-to-end algorithms and the integration of world models and reinforcement learning [4][5][6]. Group 1: Xiaomi's Strategy and Development - Xiaomi's autonomous driving team is focusing on end-to-end development, having established a dedicated department for algorithm and function development in 2024, which is relatively late compared to competitors like Li Auto and NIO [5][6]. - The company has rapidly advanced its technology, pushing out 3 million Clips of end-to-end (HAD) in February 2025 and 10 million Clips in July 2025, with the enhanced version of Xiaomi HAD officially launched at the Guangzhou Auto Show in November 2025 [5][15]. - The enhanced version incorporates a world model and reinforcement learning, allowing the model to simulate experienced drivers and understand the reasoning behind driving actions, thus enhancing its cognitive capabilities [5][6][19]. Group 2: Technical Approaches and Challenges - Xiaomi's approach emphasizes maximizing the "intelligence density" of models, regardless of whether they use VA, WA, or VLA methodologies, indicating a focus on cognitive-driven solutions rather than purely data-driven ones [5][18]. - The integration of world models and reinforcement learning presents challenges, such as ensuring the fidelity of the world model and managing computational efficiency during parallel exploration [6][59]. - Xiaomi's autonomous driving team is structured into three groups, exploring various methodologies, including VLA, WA, and VA, while maintaining a focus on end-to-end solutions [10][30]. Group 3: Industry Context and Competition - The autonomous driving industry is experiencing a "nomenclature overload," with various factions emerging around different technical approaches, leading to ongoing debates about the best methodologies [7][26]. - Xiaomi's rapid growth in its autonomous driving team, which has expanded to over 1,800 members in four years, contrasts with competitors who took longer to build their teams [13][46]. - The company has invested 23.5 billion yuan in R&D by the third quarter of 2025, with a quarter of that allocated to AI development, showcasing its commitment to advancing its autonomous driving capabilities [13][46]. Group 4: User Experience and Market Perception - Xiaomi emphasizes that the ultimate measure of technology is user experience, arguing that advanced technology does not guarantee better user perception or trust [12][24]. - The company acknowledges the pressures and criticisms it faces as a latecomer in the autonomous driving space, asserting the importance of resilience and long-term thinking in overcoming challenges [15][48]. - Xiaomi's strategy includes leveraging its existing infrastructure and data resources from other business units to enhance its autonomous driving capabilities, allowing for rapid development and deployment [44][46].

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]