具身智能的数据卡点还没解决，但好在我们走到了"数据平权"时刻。 此前，困住数据的无非是一个"荒"，百万小时级的数据集别说还只是"望梅"阶段，都不一定止渴。其本质在于当前数据量级远没有达到理想可行状态，尤 其是质量更高，数据金字塔最顶端的真机数据，其遥操作的采集方式，存在着结构性限制：本体成本高、部署复杂、采集效率低且数据受限于本体构型 等。 遥操作存在明显量级卡点，有量级优势的仿真数据又有填不平的Embodiment Gap。 用一个并不严谨的比喻来说，数据就像是饥荒，但真机和仿真路线，一个有饭、一个有菜，谁都凑不出一桌能吃饱的饭菜。 作者：彭堃方 编辑：吕鑫燚 出品：具身研习社 今天，这番景象正在发生变化。 一个迈向规模化、多样化、高质量的真实数据采集路径，被真正走通了。它比仿真数据有着更小的GAP，比真机遥操作数据有更明显的量级优势：UMI （Universal Manipulation Interface，通用操作接口）。 简单来说，它是一种通过手持夹爪、摄像头和位姿估计算法，将人类手势动作直接转化为机器人可学习轨迹的低成本数采方案。这种新范式，解决了真机 数据采集成本高、效率低、数据跨本体不可复用、数 ...

Core Viewpoint - The article discusses the emergence of a new paradigm in embodied intelligence, marked by the open-sourcing of EmbodiChain, which enables robots to be trained entirely on synthetic data and deployed in the real world without any real-world samples, signaling a shift towards data democratization in the industry [2][3][4]. Group 1: EmbodiChain and Its Impact - EmbodiChain is the world's first toolchain for embodied intelligence that can train robots using synthetic data and deploy them in real-world scenarios without any real samples, indicating the arrival of a data-equalization era [3][4]. - The open-sourcing of EmbodiChain is seen as a potential game-changer for the industry, allowing researchers and startups to generate their own training data and models, thus breaking the data monopoly held by a few large companies [14][26]. - The system operates through a closed-loop process of "dreaming - learning - validating," which eliminates the need for original physical machines [5][20]. Group 2: Technical Innovations - The first phase of the Real2Sim process includes two data generation paths: DexGen, which generates simulation scenes based on natural language, and DexDyna, which converts real operation videos into simulative action sequences [6][7]. - The second phase, Sim Data Scaling, allows for the intelligent expansion of data based on a few "seed" scenarios, achieving millions of data points through generative simulation technology [9]. - The final phase, Sim2Real, enables models trained entirely on synthetic data to be deployed directly on real robots, achieving zero-shot transfer and breaking the industry norm of mixing synthetic and real data [9][10]. Group 3: Efficiency Law and Market Potential - The article introduces the Efficiency Law, which states that the key variable determining the performance ceiling of embodied models is the rate of high-quality data generation, contrasting with the traditional Scaling Law observed in large language models [17][18]. - EmbodiChain serves as the first high data generation rate engine, transitioning the industry from a data-driven to an engine-driven paradigm, akin to the shift from manual to automated production [20][21]. - The company has already begun mass production of humanoid robots, with over 100 units shipped and nearly 100 million yuan in revenue, showcasing its commercial viability [24]. Group 4: Future Vision and Ecosystem Development - The ultimate vision for EmbodiChain is to create a complete evolutionary environment for robots, where not only strategies but also robot forms and perception systems can evolve within a physical engine [21][22]. - The open-sourcing of EmbodiChain is viewed as the beginning of an ecosystem-building effort, emphasizing the belief that the next breakthrough in embodied intelligence will arise from a standardized, shared infrastructure rather than closed proprietary models [26].

Core Viewpoint - The article discusses the emergence of TabTab.ai as a full-link Data Agent in the era of generative AI, aiming to revolutionize data analysis by enabling users to interact with data through natural language, thus democratizing data access and analysis capabilities [3][11][25]. Group 1: Market Context and Opportunity - The global data volume is expected to exceed 180ZB by 2025, with 80% being unstructured content, highlighting the limitations of traditional data analysis methods [2][9]. - TabTab.ai targets a market opportunity in the AI Agent space, which is projected to be ten times larger than the cloud-native market, positioning itself as a significant player in the data analysis landscape [5][9]. Group 2: Product and Technology - TabTab.ai offers a comprehensive Multi-Agent system that automates the entire data analysis process, from data acquisition to visualization, allowing for real-time insights and decision-making [3][11][12]. - The platform emphasizes the importance of diverse data sources, including private and vertical domain data, to enhance the accuracy and relevance of its analyses [11][12]. - The semantic layer of TabTab.ai ensures high accuracy in data interpretation, aiming for 100% accuracy in structured data analysis [12][13]. Group 3: User Engagement and Accessibility - The platform is designed for a wide range of users, including knowledge workers and small to medium-sized businesses (SMBs), enabling them to perform data analysis without technical expertise [14][25]. - TabTab.ai aims to transform data analysis from a technical task into a conversational process, allowing users to generate insights through simple language commands [23][25]. Group 4: Business Model and Growth Strategy - TabTab.ai plans to implement a product-led growth (PLG) strategy, starting in the domestic market before expanding internationally, leveraging its initial success to build a scalable model [26][27]. - The company has already secured seed funding and is actively recruiting talent to support its growth and product development [28].

Core Viewpoint - The core objective of China's data infrastructure is to address issues related to data supply, circulation, and utilization while ensuring data security, aiming for a system where data can be effectively supplied, circulated, utilized, and secured [3][6]. Group 1: Data Infrastructure Goals - The primary goal is to resolve the existing problems of data being "unable to circulate, slow to flow, and poorly utilized" [3]. - China's data infrastructure is defined as a new type of infrastructure that provides services for data collection, aggregation, transmission, processing, circulation, utilization, operation, and security [3]. Group 2: Effectiveness Indicators - The effectiveness of data infrastructure can be measured by the volume of data in circulation; significant platforms like Didi, Meituan, and Ctrip demonstrate effective data infrastructure with billions of users [4]. - The second indicator is the security of the data circulation process, which is crucial for ensuring efficient and trustworthy data flow [5]. Group 3: Key Technologies - Six key technology routes have been identified to ensure both data circulation and security: blockchain technology, privacy computing technology, data networking technology, data components, trusted data space technology, and data sandbox technology [5]. - Current technologies like blockchain and privacy computing are not yet mature enough for widespread application due to efficiency issues, particularly in sectors like finance where they are currently utilized [5]. Group 4: Future Directions - The future of national data infrastructure is expected to converge into a singular "space," "platform," or "network" where data can flow efficiently and securely [10]. - The construction of this space will involve various technologies, but the essential requirement is the presence of numerous data supply entities, application scenarios, and service providers [10]. Group 5: Addressing Data Inequality - The need to bridge the "data gap" across different industries is emphasized, with a focus on ensuring that all sectors, including manufacturing and agriculture, can leverage data for digital transformation [12]. - The national data infrastructure aims to solve the "data equality" issue, enabling artificial intelligence and other technologies to thrive by providing high-quality data [14].