Core Viewpoint - The article emphasizes the importance of high-quality data in the development of embodied intelligence, highlighting that this data must be collected in real or simulated environments to train robots effectively, similar to teaching a child through demonstration and correction [1][5]. Group 1: Data Collection and Training - In Beijing, various companies and institutions are establishing data collection centers for embodied intelligence, with a focus on creating immersive environments that replicate real-life scenarios for robots to learn tasks like opening refrigerators and serving tea [3][4]. - The training process involves thousands of data collectors who perform repetitive tasks to teach robots to execute actions naturally and accurately, with a significant emphasis on the quality of the data collected [4][22]. - The Beijing Human-Robot Innovation Center has created a 1:1 replica of various environments, such as kitchens and supermarkets, to facilitate realistic training for robots [6][8]. Group 2: Economic Value of Data - High-quality embodied intelligence data is now recognized as having clear economic value, being tradable and eligible for government subsidies, which can aid in financing and expanding applications [5][12]. - The Beijing Economic and Technological Development Zone has introduced measures to incentivize data collection, including financial rewards for high-quality data sets and the issuance of "data vouchers" to support businesses [17][18]. Group 3: Technological Approaches - The industry is currently exploring diverse technological routes for data collection, with some companies focusing on real-world data while others prioritize synthetic data for efficiency and cost-effectiveness [29][30]. - Companies like Galaxy General are adopting a "virtual-real combination" approach, using synthetic data primarily while supplementing it with real data for fine-tuning, which significantly enhances training efficiency [30][31]. Group 4: Workforce and Training Roles - The role of data collectors, now termed embodied intelligence trainers, is crucial in the data collection process, requiring physical capability and coordination to perform tasks that robots will eventually learn [24][25]. - The job market for data collectors is evolving, with companies seeking individuals who can adapt to the physical demands of the role, and there is a growing trend of remote data collection systems being implemented [26][28].

Group 1 - The core viewpoint emphasizes the importance of high-quality datasets for the development of embodied intelligence, as highlighted by Liu Liehong, the head of the National Data Bureau [1] - The National Data Bureau aims to provide high-quality datasets for the embodied intelligence industry, viewing this as a breakthrough for the marketization and valuation of data elements [1] - Liu Liehong encourages leading companies in the field, such as Ruierman and Galaxy General Robotics, to actively explore data value and open up the domestic embodied intelligence data market [1] Group 2 - The focus is on the standardization, practicality, and productization of simulation and real data within the industry [1] - The initiative aims to foster industry consensus and promote the release of data element value [1] - The visit included discussions with representatives from Ruierman and Galaxy General Robotics, indicating a collaborative approach to advancing the industry [1]

Group 1 - The core viewpoint is that embodied intelligence data is driving growth in the computer and software development industry, with a notable increase in the computer sector, as evidenced by the computer ETF (512720) rising over 1.4% [1] - Embodied intelligence data is categorized into real data, which is costly but highly authentic, and simulated data, which is cost-effective but has limited authenticity [1] - The industry faces challenges due to the scarcity of high-quality and diverse datasets, necessitating the establishment of standardized datasets to support model generalization [1] Group 2 - Open-source datasets like AgiBot World and Open X-Embodiment have emerged, covering multiple scenarios and tasks [1] - Simulation data technology paths include synthetic video combined with 3D reconstruction and end-to-end 3D synthesis, with companies like Qunkong Technology and Hillbot making strides in this area [1] - The establishment of data collection standards, such as the "Embodied Intelligence Data Collection Specification," is expected to accelerate positive industry development, with future training likely to use a mix of both data types to balance cost and effectiveness [1]