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 emphasizes the critical importance of data quality in embodied intelligence, highlighting that many issues arise from the data generation stage rather than the training phase itself [1][7][30]. Group 1: UMI Overview - Universal Manipulation Interface (UMI) is a framework proposed by Stanford in February 2024, designed to decouple robot bodies from human operation behaviors, integrating "operational intent + motion trajectory + multimodal perception" into a universal interface for various robots [5][8]. - UMI has gained traction since September 2023, with companies like Luming Robotics leading the way in this field [6][8]. Group 2: Data Collection Challenges - The cost of data collection for training is exceptionally high, with estimates of $100-200 per hour in the U.S., requiring vast amounts of data (e.g., 270,000 hours for Generalist's GEN 0) to train models comparable to GPT-3, which could cost hundreds of billions of dollars [19][21]. - Data collection efficiency is low, with remote operation yielding only about 35 data points per hour, leading to issues like data silos due to the unique designs of different robots [21][22]. Group 3: FastUMI Pro Product - Luming Robotics has developed FastUMI Pro, a data collection hardware that is lightweight (over 600 grams) yet capable of handling 2-3 kg objects, suitable for both industrial and domestic applications [10][12]. - FastUMI Pro supports multimodal inputs, including tactile, auditory, and six-dimensional force data, and boasts a spatial precision of 1mm, claimed to be the highest globally [11][12]. Group 4: Data Quality and Training Issues - The article discusses the misconception that UMI data collection is simple, emphasizing that high-quality data must meet strict alignment and synchronization criteria across multiple sensors [34][39]. - Many UMI devices fail to produce usable data due to inadequate hardware capabilities, leading to poor image quality and frame rate issues that disrupt the learning process [43][46]. - The distinction between "dirty data" and "waste data" is made, with waste data being unstructured and lacking design, making it unsuitable for training models [50][59]. Group 5: Systemic Approach to UMI - The article argues that UMI requires a systemic approach where hardware, data, and algorithms are interdependent, and any failure in one area can prevent the successful training of models [63][65]. - Luming Robotics aims to break the "impossible triangle" of high-quality data acquisition at low costs to accelerate the development of the embodied intelligence industry [68].

Core Viewpoint - The development of embodied intelligence over the past 25 years has focused on a closed-loop process of data collection, model training, data scaling, and model optimization, with data remaining a key focus for future advancements [1][5]. Group 1: Data Routes - The industry is not selecting a single optimal solution but is progressing along four distinct data routes simultaneously, each addressing different constraints and stages [3]. - The four data routes have led to the emergence of a competitive landscape termed the "Four Little Dragons of Embodied Data," with key players including Zhiyuan, Galaxy, Tashi, and Luming [4][34]. Group 2: Data Route Descriptions - **Remote Control Real Machine**: This route provides the most authentic data but is also the most expensive and slow, requiring real robots and specialized operators, making it difficult to scale [8][12][14]. - **Simulation Data**: Offers high efficiency and scalability, but faces challenges due to the domain gap, limiting its effectiveness in real-world applications [16][18][20]. - **Human Video**: This route is cost-effective and covers a wide range of scenarios but lacks critical feedback mechanisms and is not a primary data source for initial capabilities [22][25]. - **UMI Data**: This approach decouples real interaction data from specific robots, allowing for more versatile and scalable data collection, thus becoming a foundational infrastructure for embodied data [27][30][31]. Group 3: Industry Practices - In the remote control real machine data direction, Tesla is advancing its remote operation system, while Zhiyuan Robotics is deepening its focus on real bodies and task loops [35]. - In the simulation data route, Galaxy General is expanding synthetic data scale through computational power and simulation engines [35]. - In the human video data direction, Tashi is developing large-scale human behavior video datasets to enhance semantic coverage [35]. - The UMI route is represented by Luming Robotics, which has made significant strides in scaling and engineering UMI data collection systems [35][39]. Group 4: Future Implications - As the industry transitions from proving feasibility to continuous evolution, the ability to consistently produce high-quality real data will become increasingly critical [37]. - The four data routes are not mutually exclusive; they each play distinct roles in the overall ecosystem, contributing to a clearer path forward for embodied intelligence [38][40]. - The importance of time accumulation is emphasized, particularly for the UMI route, which relies heavily on early choices and sustained investment [41][42]. - The current landscape of the "Four Little Dragons" serves as a structural description of the industry, with future success dependent on which routes and teams can maintain operational continuity and data advantages [44][45].

Core Viewpoint - Lumos Robotics has launched a revolutionary product, the FastUMI Pro, which addresses the critical bottleneck in acquiring high-quality, large-scale, multi-modal data essential for the advancement of embodied intelligence [1][21]. Group 1: Product Features - FastUMI Pro features a lightweight design with a total weight of only 600g and a load capacity of up to 2kg, enhancing portability and enabling data collection in any scenario [3][10]. - The system boasts a global leading pure visual positioning technology with an accuracy of up to 3mm, eliminating reliance on heavy laser and fixed base stations [6][17]. - It supports multi-modal data collection, integrating pressure-sensitive and tactile sensors, which allows for comprehensive data capture during operations [8][19]. - FastUMI Pro can quickly adapt to various robots without the need for body replacement, showcasing high compatibility [9]. - The system significantly improves data collection efficiency by three times and reduces costs to one-fifth of traditional solutions, addressing the industry's high-cost and high-barrier challenges [11][21]. Group 2: Ecosystem and Integration - FastUMI Pro is not just a hardware device but a complete ecosystem that provides an end-to-end solution from hardware design to model training, facilitating a seamless data pipeline for researchers [13][21]. - The system includes a real-time pre-processing architecture that allows users to verify data validity during collection, thus preventing ineffective data acquisition [19][20]. - It features a four-eye vision system that enhances environmental feature capture, ensuring stable operation even in challenging lighting and occlusion conditions [19]. Group 3: Market Impact - The introduction of FastUMI Pro marks a significant step for Lumos Robotics in the core infrastructure of embodied intelligence, aiming to lower the barriers for acquiring high-quality data [21]. - The product is positioned to accelerate the scaling process of intelligent systems by enabling global research teams to obtain diverse data at lower costs and higher efficiency [21].