具身智能要想往大规模应用走，像英特尔这样的头部芯片公司必须突破算力架构。 当俄罗斯首个 AI 人形机器人「艾多尔」伴着电影《洛奇》的主题曲蹒跚登场时，所有人都以为某个高光时刻 即将来临。 没想到，「帅不过三秒」 —— 向观众挥手后，它迅速失去平衡、倒地抽搐，被工作人员匆忙拖走。 近期翻车的机器人可不止这一家。 9 月，特斯拉 Optimus 因反应迟缓被吐槽； 1X 预售款 的「惊艳演示」因系远程遥控，被舆论 diss 到起 飞。 业内人士对此并不意外。很多演示高度依赖人工操控，大量机器人连「站稳完成操作」都难，在工厂里「插个 dongle 、贴个膜」，堪比「登月」。 英特尔在与数十家具身智能团队沟通过程中也发现，机器人「能跑会跳」和「能在产线干活」之间，还存在巨 大鸿沟。 到底是什么原因阻挡它们踏入生产一线呢？ ▍ 困在算力平台里的具身智能 11 月 19 日，重庆 ·2025 英特尔技术创新与产业生态大会的圆桌现场，训练数据、应用、「大脑 / 小脑分 家」的架构问题都被摆上台面。但有一个答案被反复提及，算力平台正成为横在具身智能落地面前的最大门槛 之一。 目前业内已量产、相对成熟的人形机器人，大多采用「 ...

Core Viewpoint - The article discusses the challenges and advancements in embodied intelligence, emphasizing the need for leading chip companies like Intel to overcome computational architecture barriers for large-scale applications [2][8]. Group 1: Challenges in Embodied Intelligence - Recent demonstrations of humanoid robots, such as Tesla's Optimus and Russia's AI robot "Eidol," have faced criticism for their performance, highlighting the gap between theoretical capabilities and practical applications [3][4][7]. - The primary obstacle for these robots entering production lines is the computational platform, which is identified as a significant barrier to the deployment of embodied intelligence [9][12]. - Current humanoid robots typically use a "brain + cerebellum" architecture, where the "brain" handles complex modeling tasks, while the "cerebellum" manages real-time control, requiring high-frequency operations [9][10]. Group 2: Computational Requirements - The demand for computational power has surged due to the integration of motion generation models and multimodal perception, with many companies struggling to meet the required performance levels [10][11]. - Companies often resort to using multiple systems for different tasks, leading to inefficiencies and delays in communication, which can result in operational failures [10][11]. - The return on investment (ROI) is a critical consideration for manufacturers, necessitating robots that are not only effective but also stable, safe, cost-efficient, and energy-efficient [10][11]. Group 3: Intel's Solutions - Intel proposes a "brain-cerebellum fusion" solution using a single System on Chip (SoC) that integrates CPU, GPU, and NPU, allowing for unified intelligent cognition and real-time control [13][14]. - The Core Ultra processor achieves approximately 100 TOPS of AI computing power while maintaining similar power consumption levels, enabling faster responses and improved privacy [17]. - The integrated GPU provides 77 TOPS of AI computing power, capable of handling large-scale visual and modeling tasks effectively [18]. Group 4: Software and Ecosystem - Intel offers a comprehensive software stack that includes operating systems, drivers, SDKs, and real-time optimizations, facilitating easier development for hardware manufacturers [24][26]. - The oneAPI framework allows developers to write code once and run it across various hardware platforms, promoting interoperability and efficiency [27]. - Intel's open approach to technology enables companies to adapt existing systems without being locked into specific vendors, fostering innovation in the embodied intelligence sector [31].

Group 1 - The humanoid robot market is on the verge of explosive growth, with a projected market size of approximately 9 billion in 2025, expected to soar to 150 billion by 2029, reflecting a compound annual growth rate (CAGR) exceeding 75% [2] - The core drivers of this market growth will be industrial handling and medical applications, highlighting the importance of advanced processing capabilities in humanoid robots [2][5] - The performance of processors is critical as it directly influences the intelligence level and application potential of humanoid robots, making them the foundational element of the robotics industry [1][5] Group 2 - The current processor supply for humanoid robots is dominated by NVIDIA and Intel, while domestic chip manufacturers are still in the catch-up phase [6] - Tesla is noted for its capability to develop its own chips, such as the Dojo chip for AI model training and the FSD chip for real-time operations in robots, while other manufacturers primarily rely on Intel and NVIDIA chips [6][8] - The Jetson Orin series from NVIDIA is widely used, providing up to 275 TOPS of computing power, significantly enhancing the capabilities of humanoid robots [9][10] Group 3 - Domestic manufacturers are accelerating the development of their own humanoid robot chips to compete with foreign dominance, focusing on integrating general intelligence with practical application needs [10][11] - The RK3588 and RK3588S chips from Rockchip have been adopted by several humanoid robot manufacturers, showcasing their potential in the robotics field [11] - The RDK S100 development kit from Horizon Robotics integrates both "brain" and "cerebellum" functions into a single SoC, simplifying hardware architecture and reducing development costs [12][14] Group 4 - The trend towards "brain-cerebellum fusion" architecture aims to enhance the synchronization and efficiency of humanoid robots by integrating cognitive decision-making and motion control into a unified system [15][17] - Current challenges in the humanoid robot market include insufficient data accumulation, hardware architecture optimization, high costs, and safety concerns, which need to be addressed for large-scale commercialization [18][19][20]