(本文来自第一财经) 深圳市工业和信息化局近日印发《深圳市"人工智能+"先进制造业行动计划（2026—2027年）》，其中 提出，人工智能赋能机器人。支持世界模型、视觉-触觉-语言-动作（VTLA）等多模态交互技术研发， 构建具备交互、预测与决策功能的具身智能基座大模型及其训练、推理技术体系，培育长序列推理与自 主学习能力，支撑跨场景任务高效处理。强化场景资源统筹，支持建设具身智能技术试验场，开放工业 制造领域焊接、装配、喷涂、搬运等细分场景并实现落地应用，提升危险、恶劣环境下智能作业水平， 推动机器人进工厂、进车间、进仓库、进港口、进园区。 ...

深圳市工业和信息化局近日印发《深圳市"人工智能+"先进制造业行动计划（2026—2027年）》，其中 提出，人工智能赋能机器人方面，支持世界模型、视觉-触觉-语言-动作（VTLA）等多模态交互技术研 发，构建具备交互、预测与决策功能的具身智能基座大模型及其训练、推理技术体系，培育长序列推理 与自主学习能力，支撑跨场景任务高效处理。强化场景资源统筹，支持建设具身智能技术试验场，开放 工业制造领域焊接、装配、喷涂、搬运等细分场景并实现落地应用，提升危险、恶劣环境下智能作业水 平，推动机器人进工厂、进车间、进仓库、进港口、进园区。 ...

Core Insights - The industry of embodied intelligence is at a crossroads of capital and industrial focus, with increasing financing and frequent technological demonstrations, yet facing challenges in stability, scalability, and cost control [1] Group 1: Financing and Valuation - Starry Sea has completed a Series B financing round of 1 billion yuan, bringing its total financing to nearly 3 billion yuan and achieving a valuation of 10 billion yuan, making it a unicorn in the embodied intelligence sector [1] - The CFO of Starry Sea emphasizes that the success in the AI industry is driven by Scaling Law, where the efficiency of capital utilization is more critical than the amount of financing [1][2] Group 2: Industry Dynamics - The current phase of the embodied intelligence industry is compared to the "Hundred Groups War," where companies are advised to focus on understanding the essence of business rather than just technology [2] - The industry is transitioning from early-stage technology exploration to resource-intensive competition, with a shift in capital logic from broad investment to focusing on leading companies [2] Group 3: Commercialization and Technology - The commercialization of embodied intelligence is divided into technology-driven and business-driven aspects, with specific operational boundaries that need to be met for successful deployment [4] - The CFO believes that the industry is still in the early stages of a technological race, and companies must retain sufficient funds to cope with the increasing costs of data and model training [2][4] Group 4: Financial Potential and Business Model - The ToB (business-to-business) segment of embodied intelligence has significant revenue potential, with large orders capable of generating substantial income, but the focus should be on revenue quality metrics [5] - The long-term business model in this industry is likened to selling "tokens of the physical world," with the real barriers being intelligence levels and the ability to design and manufacture hardware [5] Group 5: Competitive Advantages - China is recognized for its data supply chain advantages, which are significantly more cost-effective than those in the U.S., allowing for greater data collection at lower costs [6] - The CFO highlights that the unique aspect of embodied intelligence companies lies in developing their foundational models for physical world execution, emphasizing the need to focus resources on building these capabilities [7]

Core Viewpoint - The article highlights the successful completion of a 1 billion yuan Series B financing round for Xinghai Map, indicating strong market recognition of its technological advancements and commercial progress in embodied intelligence [2]. Group 1: Financing and Shareholder Structure - The financing round attracted leading industry capital such as Jinding Capital, BAIC Investment, and Bihong Investment, which will accelerate technological integration in smart manufacturing and the automotive industry [4]. - Top-tier private equity funds like Zhengxin Valley Capital and Qianhai Ark's participation reflects long-term value investors' recognition of the company's growth potential [4]. - International capital from Yifeng Capital enhances the company's global strategy and resource access, facilitating overseas market expansion [4]. - The continued investment from five major existing shareholders demonstrates confidence in the company's execution capabilities and growth potential [4]. Group 2: Technological Strength - Xinghai Map maintains a "full-stack self-research" approach, investing in algorithms, hardware, and data to build a comprehensive technology system [5]. - The company has iterated its foundational model, launching the G0 model in August 2025 and its upgraded version G0 Plus in January 2026, which is recognized as the world's first out-of-the-box VLA model [5]. - The open-source dataset from Xinghai Map has been downloaded over 500,000 times, becoming the most widely adopted dataset among major global institutions and enterprises [5]. - The company has established a high-efficiency data collection and model training base, entering a phase of large-scale training with hundreds of thousands of hours of high-quality data [5]. Group 3: Commercialization and Market Position - Xinghai Map has secured thousands of orders, covering top global universities, research institutions, and industry leaders, indicating strong commercial capabilities [7]. - The company leads the market in wheeled dual-arm robots, with its R1 Pro and R1 Lite platforms being utilized by over 90% of top global developers [7]. - The company has received large-scale orders from leading domestic automotive manufacturers and smart logistics companies, marking a transition from feasibility demonstrations to large-scale deployments [8]. Group 4: Vision and Future Goals - Xinghai Map aims to redefine its mission beyond manufacturing single robots, focusing on building foundational infrastructure for the intelligent transformation of the physical world [11]. - The company envisions deploying 10 billion intelligent agents to serve 10 billion people, having established a closed-loop verification from technology research and product innovation to commercial implementation [11].

Core Viewpoint - The article discusses the collaboration between Beijing Humanoid Robotics Innovation Center and China Electric Power Research Institute to develop a fully autonomous intelligent solution for power operations, marking a significant advancement in the integration of artificial intelligence with the energy sector [2][12]. Group 1: Development of Autonomous Intelligent Solutions - The collaboration aims to create a "Fully Autonomous Power Operation Intelligent Solution" that can be validated in real scenarios and quickly replicated, enhancing the efficiency and safety of power operations [2][3]. - The solution addresses the limitations of current power robots, which often rely on preset paths and fixed targets, by enabling them to autonomously understand and execute tasks in dynamic environments [3][4]. Group 2: Technological Innovations - The development includes the "Wisdom Open Object" platform and the XR-1 VLA model, which facilitate a rapid feedback loop from perception to action, allowing robots to autonomously navigate and perform complex tasks such as equipment operation and state recognition [3][4]. - The intelligent model integrates multi-modal data for precise spatial awareness, enabling robots to operate effectively in crowded environments and perform tasks with high accuracy [4][5]. Group 3: Practical Applications and Testing - The intelligent robots have been successfully deployed in various power companies, completing tasks such as circuit operations and equipment adjustments autonomously, demonstrating their robustness and adaptability in real-world scenarios [8][9]. - The collaboration has led to the establishment of a joint laboratory that simulates standard operational scenarios, validating the technology and laying a foundation for future applications in complex environments [8][12]. Group 4: Strategic Importance and Future Directions - The initiative aligns with national strategies to promote the integration of AI in the energy sector, aiming for predictive maintenance and enhanced operational efficiency by replacing manual labor in high-risk tasks [12][13]. - The ongoing development of intelligent robots is expected to transform the power maintenance landscape, making them essential components in the operational framework of the energy industry [12][13].

【大河财立方消息】2月12日，灵心巧手宣布完成近15亿元B轮融资。本轮融资由道得投资、盛世投资领投，盈渊朴远基金、诺瓦星云、新鼎资本、中和 资本、智路资本、诚筑投资、临云资本、嘉铭浩春、财鑫资本、Singapore Eastern Epic Capitals等知名投资机构和产业方跟投。 创立初期，Linker Hand L20一经推出便引领全球高自由度灵巧手进入新的能力周期，依靠其高自由度和高性能表现成为行业标杆级产品。此后灵心巧手陆 续推出Linker Hand O6、L6、L20 Lite以及Linker Hand L20工业版、L30等多款全新产品，在轻量化、极致化、高质平价等方面齐头推进，形成全面丰富的 灵巧手产品矩阵。 2026年，灵心巧手目标实现5万~10万台的交付规模，以此驱动灵巧操作的技术变革，践行创新引领者的角色。 责编:史健 | 审核:李震 | 监审:古筝 官网显示，灵心巧手（北京）科技有限公司是全球灵巧手领军企业，系全球唯一实现高自由度灵巧手月产千台的企业，占据该领域市场80%以上份额， Linker Hand全系列灵巧手量产万台。该公司构建了涵盖Open TeleDex遥操作系统、Li ...

Group 1 - The core viewpoint of the article is the strategic cooperation agreement signed between Kailong High-Tech and the National Local Co-construction Intelligent Robot Innovation Center, focusing on the industrialization of the general embodied intelligent platform "Wisdom Opens Things" [1] - The collaboration will emphasize deep cooperation in vertical fields such as environmental monitoring and intelligent manufacturing in the automotive sector [1]

在技术路径上，该公司强调"全栈自研"策略，形成涵盖底层驱动、传感系统与上层算法的闭环能力，并 通过遥操作系统与多模态数据平台构建开放生态，推动技能数据沉淀与应用扩展。 经济观察网2月12日，全球灵巧手企业灵心巧手宣布完成近15亿元B轮融资。本轮融资由道得投资、盛 世投资领投，盈渊朴远基金、智路资本等多家产业与财务机构参与跟投。公司表示，资金将主要用于核 心产品研发、产能提升及全栈技术基座建设。 从产业位置看，灵巧操作部件正成为具身智能体系中的关键基础环节。灵心巧手旗下Linker Hand系列 已覆盖腱绳、直驱、连杆等多种技术路线，并在工业自动化、科研实验及复杂操作场景中实现应用落 地。公司目前已具备月产千台高自由度产品的规模化制造能力，在细分市场占据较高份额。 ...

Core Insights - The article discusses the advancements in embodied intelligence, particularly focusing on humanoid robots and their potential to transition from being mere technological showcases to practical assistants in various industries [1][2][86]. - The company, Luming Robotics, has developed a proprietary data collection system called FastUMI, which significantly reduces the cost of training data and enhances the efficiency of data collection for robotic applications [1][4][86]. Group 1: Company Overview - Luming Robotics was established in 2024 and is recognized as a leading full-stack embodied intelligence company with strong capabilities in data and hardware [4]. - The company has developed several core technologies, including the FastUMI Pro data collection system and high-torque lightweight joint modules, and has launched multiple humanoid robot series [4][86]. - Luming has formed partnerships with several Fortune 500 companies and has strategic shareholders with substantial industry backgrounds, accelerating its industrialization process [4]. Group 2: Technology and Innovation - The FastUMI system allows for low-cost data collection, with costs as low as 0.5-0.6 yuan per training data point, representing an 80% reduction compared to traditional methods [1][9][10]. - Each of the 25 workstations can produce over 10,000 high-quality data points daily, enabling precise data collection for transparent object manipulation and compatibility with over 90% of market mechanical arms and grippers [1][9][10]. - The company aims to collect 100 million data points this year, which is expected to propel embodied intelligence into a transformative phase akin to the "GPT-3 moment" [1][71][86]. Group 3: Product Applications - The MOS dual-arm robot can lift up to 50 kilograms, demonstrating its application in industrial settings such as 3C quality inspection and logistics handling [1][43][86]. - The LUS humanoid robot can perform complex movements, including a one-second rise and dance training in just a few hours, showcasing its versatility [1][49][86]. - The company also features a playful robot named Nezha Xiaoming, which adds a fun element to the technological advancements [1][86]. Group 4: Future Outlook - Luming Robotics believes that humanoid robots will gradually enter households within the next two to three years, expanding their utility beyond industrial applications [1][62][86]. - The company is focused on enhancing the operational capabilities of robots, addressing challenges such as stability, battery life, and heat management, which are crucial for broader deployment [58][60][62]. - The integration of high-quality data and advanced algorithms is expected to significantly improve the robots' performance and adaptability in various environments [65][66].

Core Insights - The article discusses the transformative impact of large models on natural language processing (NLP) and draws parallels to the current state of the robotics industry, highlighting the need for a unified approach in robotic systems similar to the shift seen in NLP with the introduction of models like GPT [1][2][5]. Group 1: Robotics Industry Challenges - The robotics industry is currently fragmented, with different manufacturers using incompatible action representation systems, leading to a lack of model reusability and requiring new systems for each scenario [2][8]. - The absence of a unified data representation and action modeling in robotics has hindered the development of scalable training methods, making it difficult to integrate diverse data sources [7][8]. - The industry's reliance on specialized models for different tasks limits the ability to generalize and adapt to new environments, resulting in a lack of robust performance in complex scenarios [9][23]. Group 2: Introduction of ABot Series - Alibaba's Amap has introduced the ABot series, consisting of ABot-M0 and ABot-N0, which aim to provide a unified base for robotic operations and navigation, respectively [3][4]. - ABot-M0 focuses on standardizing action language across various robot forms, enabling them to perform diverse tasks using a common model, thus reducing training costs and improving efficiency [12][14]. - ABot-N0 addresses the challenges of navigation in dynamic environments, integrating multiple navigation tasks into a single model, which enhances the robot's ability to operate in real-world scenarios [22][26]. Group 3: Technical Innovations - ABot-M0 employs a systematic reconstruction approach that includes data unification, algorithm innovation, and enhanced spatial perception to improve operational capabilities [12][15][17]. - The model has achieved state-of-the-art (SOTA) performance in various benchmarks, demonstrating significant improvements in task success rates, particularly in complex environments [20][32]. - ABot-N0 utilizes a hierarchical design philosophy that combines cognitive understanding with precise action generation, allowing for more natural and effective navigation in real-world settings [29][30]. Group 4: Future Implications - The release of the ABot series is expected to lower the barriers for smaller teams to develop robotic solutions, potentially transforming the development paradigm from extensive custom systems to fine-tuning existing models [38]. - The long-term vision includes the possibility of modular robotic capabilities akin to APIs, enabling developers to easily implement physical tasks through standardized models [38][39]. - The advancements in unified data formats and pre-training weights are anticipated to significantly reduce the time and cost associated with robotic training and deployment [38].