2026年Las Vegas的CES展会上人潮涌动，当全球科技巨头们还在讨论人形机器人何时可以实现真实场景 的商业闭环时，North Hall区一个60平米的展位前却聚集了近百人围观。一台拥有双灵巧机械臂和生动 数字人交互的咖啡机器人成为在场所有人关注的焦点，多位现场观众都给出了类似的评价——"这应该 是本届CES里最落地的具身智能产品"。 这台名为XBOT的咖啡机器人来自于一家聚焦消费场景的中国具身智能企业：影智XBOT，而这家公司 的创始人正是先后作为腾讯与小米的早期成员，主导了QQ表情、QQ空间、小爱音箱、小米路由器等爆 款产品的唐沐。 当大部分具身智能企业还在工业场景和家庭场景的红海中搏杀内卷之时，这位中国顶级产品经理已经用 600台机器人部署、400万杯咖啡的销售数据，实现了具身智能首次以标准化商业产品形态进入全球最高 频消费场景的突破，这不仅是影智科技交出的第一份商用具身智能的落地答卷，对于整个具身智能行业 的商业落地进程而言，也具有里程碑的意义。 如何让机器人做出一杯不输于人类咖啡师的精品咖啡？ "这可不是一台装了机械臂的咖啡机，而是一台真正会做精品咖啡的机器人。"影智XBOT的负责人对钛 媒体 ...

IT之家 1 月 8 日消息，法拉第未来（FF）公司今日首次在美国拉斯维加斯举办股东日活动，发布了多项全新业务进展与规划。 本次活动与国际消费电子展（CES 2026）同期举行，该公司系统性阐述了其 2026 年规划，并首次对外披露 FX Super One 的关键里程碑，以及 FF 五年商 业计划（BP）的业务执行计划。 法拉第未来还正式宣布，经过长时间的潜行，全球 EAI 产业桥梁战略正式推出具身智能机器人战略，由 EAI 汽车与 EAI 机器人"双轨驱动"的格局将开启 FF 未来 10 年的全新增长曲线。 本次更新由 FF 与 FX 核心高管共同发布，包括 FF 创始人兼 Co-CEO 贾跃亭、 FF Co-CEO Matthias Aydt 、FF 全球总裁 Jerry Wang、FF 首席财务官 Koti Meka，以及 FX CEO Max Ma 。 法拉第未来同时宣布，在升级版全球 EAI 产业桥战略下推出具身智能机器人全新产品线。该公司认为，在 AI 进入大规模落地与商业化阶段的背景下， EAI 是本轮产业周期中最具长期价值和影响力的战略方向之一。 法拉第未来公布了 FX Super One ...

Group 1 - Tencent's Robotics X Lab has formed a strategic partnership with Yushu Technology to enhance the application of Yushu's robots in various scenarios such as cultural tourism sites, shopping malls, and corporate exhibition halls [2] - The collaboration will leverage Tencent's Tairos embodied intelligent model to support Yushu's robots in providing user-friendly tour guide and shopping services, aiming to set industry benchmarks [2] - Tencent participated in Yushu Technology's Series C financing, which was completed in June 2025, with major investors including China Mobile's fund, Tencent, Alibaba, Ant Group, and Geely Capital [2] Group 2 - The robot tour guide solution developed by both companies has been piloted at the Dunhuang Mogao Caves Digital Exhibition Center and the Shanghai Guoling Law Firm global headquarters [3] - The Yushu G1 AI tour guide robot, equipped with the Tairos model, has started its internship as a guide at the Dunhuang site, providing visitors with cultural insights [3] - At the Shanghai Guoling Law Firm, the G1 robot serves as a guide, offering information about the firm's history and allowing clients to engage in Q&A sessions [3] Group 3 - Other companies have also disclosed collaborations with Yushu Technology, focusing on models, hardware, and ecosystem development [4] - iFlytek announced its partnership with Yushu Technology, providing AI services based on its Super Brain 2030 platform, covering over 500 intelligent robot manufacturers [4] - JD.com launched its first offline store in collaboration with Yushu Technology, featuring products like the Go2 robotic dog and G1 humanoid robot, marking an expansion of the robot ecosystem into offline scenarios [4] - Rockchip has reported a high market share for its flagship product RK3588 in the robotics sector, collaborating with Yushu Technology and other notable clients on various robot forms [4]

Core Viewpoint - CES 2026 showcases a significant shift where AI is not just a digital entity but is increasingly integrated into physical hardware, impacting various aspects of daily life and industry [6][7]. Group 1: Robotics and Embodied Intelligence - CES 2026 marks a turning point for robotics, with a dedicated exhibition area for embodied intelligence, indicating that robots are moving from mere demonstrations to practical applications in manufacturing, transportation, and healthcare [9]. - The Chinese robotics sector is prominently represented, with over half of the exhibitors showcasing advancements, while Boston Dynamics introduced the all-electric Atlas robot, highlighting a competitive landscape for commercialization in embodied intelligence [9][10]. - The new Atlas robot is designed for practical factory work, featuring advanced mobility and AI learning capabilities, marking a milestone in the transition from prototype to product [12]. Group 2: AI Hardware - AI hardware is becoming more integrated into everyday products, with a focus on subtlety and functionality rather than overt complexity [32]. - The NotePin S device captures important audio moments and organizes them using AI, reflecting a trend towards more intelligent and user-friendly recording solutions [32][35]. - Sweekar, an AI pet, embodies the trend of companionship through physical entities, evolving from simple digital pets to interactive, breathing companions that learn and adapt to user interactions [36][38]. Group 3: Smart Mobility - The automotive sector at CES 2026 reveals a stark contrast between the vibrant Chinese market and a more subdued American presence, reflecting a shift in global automotive dynamics [44]. - Nvidia's Alpamayo introduces advanced logical reasoning capabilities for autonomous driving, moving beyond basic reflexive responses to more complex decision-making processes [45][48]. - Strutt's Ev1 smart electric wheelchair incorporates advanced sensors for enhanced navigation, addressing the unique challenges faced by wheelchair users [49][50]. Group 4: Innovative Products - The CES 2026 features innovative products that blend nostalgia with modern technology, such as Lego's interactive building blocks that respond to user actions without screens [60][61]. - Mui Board Gen 2 offers a unique approach to sleep monitoring through a wooden board that detects breathing patterns without the need for wearable devices, emphasizing a blend of aesthetics and functionality [76].

Core Viewpoint - The article emphasizes the importance of high-quality, large-scale, and diverse datasets for advancing embodied intelligence, highlighting the release of the "10Kh RealOmni-Open DataSet" by JianZhi Robotics as a significant milestone in the industry [1][4][16]. Dataset Overview - The "10Kh RealOmni-Open DataSet" consists of over 10,000 hours of data and nearly one million clips, making it the largest and most generalized open dataset in the field [1][4]. - The dataset focuses on 10 common household tasks, ensuring that each skill has over 10,000 clips, which enhances both the scale and depth of skills covered [4][5]. Data Quality and Specifications - The dataset features high-quality recordings with a resolution of 1600x1296 pixels and a frame rate of 30 fps, ensuring clarity and detail in the captured actions [4][5]. - It achieves centimeter-level trajectory precision, with advanced IMU hardware and cloud reconstruction techniques enhancing the accuracy to sub-centimeter levels [4][12]. Skill and Task Coverage - The dataset prioritizes tasks that can be performed with one hand in real scenarios, with 99.2% of the clips involving "two-handed, long-range tasks," providing a realistic representation of household activities [5][7]. - The average clip length is 1 minute and 37 seconds, capturing the complete process of tasks rather than static snapshots, which aids in understanding action logic and causality [5][7]. Data Collection Methodology - The data was collected from 3,000 real households, ensuring a rich variety of scenarios and natural human interactions, addressing the limitations of traditional data collection methods [7][9]. - JianZhi Robotics employs a comprehensive data production chain, allowing for rapid accumulation of data, with nearly one million hours collected in just two months [9][11]. Technological Infrastructure - The Gen DAS Gripper is a key component in the data collection process, enabling quick deployment without the need for extensive site preparation [11]. - The Gen Matrix data platform processes and cleans the collected data, achieving high precision in trajectory reconstruction and synchronization of heterogeneous data sources [13]. Future Directions - The open-sourcing of this dataset is seen as a way to accelerate innovation in embodied intelligence by filling data gaps, standardizing formats, and lowering research barriers [16]. - JianZhi Robotics plans to continue enhancing its data infrastructure and releasing more beneficial datasets and services, fostering a positive cycle of data sharing, model optimization, and practical application [16].

Core Viewpoint - The article emphasizes the significance of the newly open-sourced high-quality tactile operation dataset for dexterous robotic hands, which addresses the industry's urgent need for accurate physical interaction data and is expected to drive advancements in humanoid robotics in 2026 [6][7][42]. Group 1: Challenges in Dexterous Manipulation - Dexterous manipulation is challenging due to three main factors: the lack of mature hardware products, difficulties in model training that rely solely on visual data, and a scarcity of high-quality tactile data [6][9]. - The current limitation of dexterous manipulation is primarily due to the inability to effectively perceive physical properties like force and material through visual data alone, leading to the issue of "seeing but not touching" [9][24]. Group 2: Open-Sourced Dataset Details - The dataset consists of 800 high-quality tactile operation samples, which provide a continuous multi-modal learning resource that connects "visual-force-touch-action" [10][11]. - The dataset includes real-world scenarios such as fruit grabbing, package sorting, and material loading, ensuring a realistic representation of complex operational environments [9][12]. - It features multi-modal data with added tactile and six-dimensional force data, enhancing the robot's ability to perceive physical attributes of objects [9][11]. Group 3: Technical Advancements - The dataset introduces five key enhancements: arrayed tactile data, higher-dimensional force control data, 3D spatial information, synchronized perception of visual and tactile data, and a broad range of real-world scenarios to prevent overfitting [15][16]. - The tactile data is collected using a 6×12×5 sensor array, allowing the robotic hand to accurately sense material properties and contact states, while the six-dimensional force data provides high precision [15][20]. Group 4: Impact on Robotics - The open-sourced dataset is expected to improve the success rate of robotic operations by enabling real-time perception and adjustment of grasping techniques based on object shape and force [25][26]. - The integration of tactile and visual data allows robots to break through the limitations of pure visual perception, enhancing operational stability in complex environments [26][27]. - The dataset's broad coverage across various fields, including household, logistics, and consumer goods, will facilitate the adaptation of robots to different materials and shapes [27][31]. Group 5: Future Prospects - The open-sourcing of this dataset is anticipated to catalyze the development of the entire embodied intelligence industry chain, fostering innovation and application in robotics [40][41]. - The establishment of the Leju OpenLET community aims to create a collaborative platform for developers and researchers, accelerating the development and industrial application of embodied intelligence technologies [43].

Core Viewpoint - The article discusses the development and implementation of SOP (Scalable Online Post-training), a system designed for the scalable deployment and intelligent operation of general-purpose robots in real-world environments, emphasizing the need for continuous evolution and learning in robotic systems [2][3][23]. Group 1: SOP Overview - SOP is the first system in the industry to systematically integrate online learning, distributed architecture, and multi-task versatility for real-world deployment of robots [2]. - The core goal of SOP is to enable distributed and continuous online learning for robots in real-world settings [5]. Group 2: Challenges in Real-World Deployment - General-purpose robots face the challenge of meeting high task specialization requirements while leveraging existing VLA pre-trained models, which often require post-training for improved task success rates [3][4]. - Current mainstream VLA post-training methods are limited by offline, single-machine, and serial data collection, hindering efficient and continuous real-world learning [3]. Group 3: SOP Framework Design - SOP redefines VLA post-training from "offline, single-machine, sequential" to "online, cluster, parallel," creating a low-latency closed-loop system [6]. - The system allows multiple robots to execute tasks in parallel, with cloud-based centralized online updates and immediate model parameter feedback [6][9]. Group 4: Key Advantages of SOP - SOP enhances state space exploration through distributed multi-robot parallel exploration, significantly improving state-action coverage [12]. - It mitigates distribution bias by ensuring all robots operate based on the latest low-latency strategies, enhancing online training stability and consistency [13]. - SOP maintains generalization capabilities while improving performance, avoiding the degradation of models into single-task specialists [14]. Group 5: Experimental Evaluation - SOP significantly improves performance metrics, with a 33% overall performance increase in complex scenarios and a 114% increase in throughput for specific tasks like folding clothes [16][18]. - The use of multiple robots enhances learning efficiency, with a four-robot setup achieving a 92.5% success rate, 12% higher than a single robot [19][20]. - SOP demonstrates stable effectiveness across different pre-training scales, with performance improvements correlating with the quality of VLA pre-training [21]. Group 6: Deployment and Evolution - SOP allows robots to adapt and improve in new environments, transforming them from fixed-performance products into evolving entities capable of continuous learning [23].

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本报讯 （记者王镜茹） 1月6日，在CES2026展会期间，奥比中光发布专为机械臂腕部设计超小型双目3D相机Gemini305，并正 式宣布其面向机器人的标志性产品Gemini330系列相机已完成与先进算力平台NVIDIAJetsonThor的适 配。同时，公司还首次展示了可支持全球客户的中国顺德与越南双工厂制造体系，打造机器人视觉产业 生态。 随着NVIDIAJetsonThor平台进入量产，机器人产业正迈入千TOPS级边缘算力新时代。 在数据传输层面，奥比中光也已完成Gemini335Lg双目3D相机对NVIDIAHoloscanSensorBridge（HSB） 的适配与验证，为双目3D视觉数据在高带宽、低时延条件下的稳定传输夯实基础。 机器人整机制造能力正在成为奥比中光服务全球客户的核心优势。在此次CES上，奥比中光展示了其中 国与越南双工厂制造布局，以及覆盖从新产品导入（NPI）到大规模量产的全流程服务能力，面向全球 机器人企业提供"研发+制造"一体化实力。 （公司供图）CES2026展会现场 奥比中光位于顺德的智能制造基地具备千万级传感器及百万级机器人终端量产能力。奥比中光越南工厂 预计于2026 ...

近日，上海龙旗科技股份有限公司（简称："龙旗科技"）通过港交所主板上市聆讯。 这家全球智能手机ODM出货量第一、消费电子ODM出货量第二的巨头，并非资本市场的新面孔。公司A股 （603341.SH）已于2024年上市，客户结构清晰、订单规模及财务数据高度透明。 然而，此次通过港股聆讯不能简单理解为"旧公司"奔赴"新市场"，而应置于一个更大的产业背景下审视：当前正处于 AI终端从概念期迈向规模化放量、ODM行业竞争壁垒系统性抬升、市场对"硬件+AI"估值框架进行重构的关键节点。 如果仅将龙旗此次赴港上市视作多一个融资渠道、多一份流动性的常规操作，就可能低估了这一时机背后的战略意 义。 一、行业逻辑重估：AI重塑终端，龙头强者恒强 AI终端带来的想象空间，已不再局限于为智能手机嵌入大模型这类单点创新。终端形态正呈现多元化分岔趋势：手机 虽仍是主赛道，但AI PC、智能眼镜、穿戴设备等品类正逐步成为新的AI入口，带动产业链需求的结构性转移。 以智能眼镜为例，市场端的增速足够激进。2025年第二季度，全球AI智能眼镜销量同比激增222%，维深XR预测至 2035年出货量将达14亿副，开启千亿级蓝海市场。如果这个趋势成 ...