Core Insights - The article highlights the critical shortage of high-quality data as a significant bottleneck in the development of embodied intelligence, suggesting that overcoming this challenge will provide a competitive edge in the industry [1]. Group 1: Galaxea Open-World Dataset - The Galaxea Open-World Dataset, launched in August, has achieved over 400,000 downloads within two months, indicating its widespread acceptance among core developers in the embodied intelligence community [2][8]. - The dataset includes over 100,000 mobile operation data points across 50 real-world environments, covering 150 task types and 1,600 operation objects, making it a comprehensive resource for developers [8][12]. - The dataset's rapid adoption reflects a collective endorsement from a technically proficient developer community, suggesting its high quality and relevance [6][11]. Group 2: Importance of High-Quality Data - High-quality real-world data is essential for training effective embodied intelligence models, as it addresses the limitations of internet and simulation data [13][14]. - The cost of acquiring high-quality real-world data is seen as a worthwhile investment, as it can significantly reduce subsequent model training costs, with a cost ratio of approximately 1:10 in the Chinese market [15]. - The article emphasizes that the competition in embodied intelligence will increasingly hinge on the availability and quality of data, making it a critical asset for building competitive advantages [13][15]. Group 3: Key Components of Data Collection - The successful collection of high-quality real-world data relies on three core elements: hardware, diverse environments, and engineering capabilities [17][20]. - The hardware used for data collection, such as the Starry Sky R1 Lite robot, is designed to operate effectively in a wide range of scenarios, ensuring data clarity and accuracy [17][18]. - Engineering capabilities are crucial for transforming raw data into usable assets through standardized processes, which enhances the dataset's overall value [22]. Group 4: Long-Term Strategy in Embodied Intelligence - The article suggests that the commitment to real-world data collection represents a strategic move to establish systemic barriers in a competitive landscape, positioning the company favorably in the industry [24][26]. - The integration of hardware, data-driven model training, and algorithmic enhancements is viewed as a pathway to building a closed-loop system that improves robotic efficiency and intelligence [26]. - The focus on long-term development and understanding of robotics is seen as a critical factor for success in the embodied intelligence sector, which is characterized by its complexity and need for sustained effort [26].

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].

Core Insights - The article discusses the development of embodied intelligence in robotics, emphasizing the importance of high-quality data for training robots to perform household tasks and other complex operations [4][5][6]. Group 1: Data Collection and Training Centers - Multiple data collection centers have been established in Beijing, including those by Qianxun Intelligent and Beijing Humanoid Robot Innovation Center, focusing on training robots in various tasks such as folding clothes and operating in kitchen and commercial environments [3][4][5]. - The training process involves repetitive actions performed by human operators to teach robots, with a significant emphasis on creating realistic environments for effective learning [4][5][6]. - Beijing is positioning itself as a hub for embodied intelligence, with government support and incentives for data collection and sharing among companies [4][12][18]. Group 2: Economic Value of Data - High-quality embodied intelligence data is now recognized as a valuable economic asset, with potential for trading, government subsidies, and as a means for companies to secure financing [4][6][18]. - The government has introduced measures such as "data vouchers" to encourage the development of a collaborative data ecosystem, shifting focus from subsidizing robots to incentivizing data collection [18][19]. Group 3: Training Efficiency and Technology - Qianxun Intelligent has improved training efficiency significantly, reducing the number of high-quality data points needed for training new actions from 600-700 to under 100, enhancing the learning speed of robots [6][8]. - The Beijing Humanoid Robot Innovation Center has achieved over 10,000 hours of action data collection monthly, focusing on the quality of data rather than just quantity [8][12]. Group 4: Industry Collaboration and Open Data - Companies like Xinghai Map Technology are releasing open datasets to promote industry standards and facilitate collaboration among developers and researchers [19][20]. - The industry is witnessing a trend towards combining real-world data collection with synthetic data generation to enhance training efficiency and model performance [26][28]. Group 5: Workforce and Training Roles - The role of data collection personnel, termed embodied intelligence trainers, is crucial in the training process, requiring physical demonstrations of tasks to gather data [21][22]. - The industry is experiencing a growing demand for skilled workers in data collection and algorithm development, with varying salary structures based on expertise and responsibilities [22][23]. Group 6: Future Directions and Challenges - The article highlights the ongoing debate between the merits of real-world data collection versus synthetic data generation, with companies exploring hybrid approaches to optimize training outcomes [26][27]. - The future growth of humanoid robots is anticipated to accelerate, driven by advancements in data collection methods and the integration of robots into real-world applications [27][28].

Core Viewpoint - The debate between the value of real-world data versus simulation data in robot training is intensifying, with industry leaders emphasizing the necessity of real data for complex tasks while acknowledging the cost-effectiveness of simulation data for simpler tasks [1][2][4]. Group 1: Importance of Real Data - Sergey Levine, co-founder of Physical Intelligence, argues that real-world data is essential for effective robot training, challenging the reliance on simulation data [1]. - Industry experts, such as Yao Maoqing from Zhiyuan Robotics, support Levine's view, stating that while some tasks can be trained using simulation, most complex tasks require real data [1][3]. - The CEO of Qingtong Intelligent, Li Tong, emphasizes that robots must be deployed in real environments to accumulate valuable training data, suggesting that a deployment scale of tens of thousands is necessary for effective data collection [3]. Group 2: Simulation Data Advantages - Companies like Galaxy General advocate for simulation data, claiming it allows for faster learning and lower costs, even enabling training without real data [2]. - The COO of Self-Variable Robotics, Yang Qian, acknowledges the role of simulation in training lower-body movements but stresses that real-world data is crucial for tasks involving complex interactions [10][12]. - The industry faces a dilemma in balancing the use of simulation and real data, with some companies using a 7:3 ratio of simulation to real data, while others prefer a 3:1 ratio favoring real data [9][10]. Group 3: Challenges and Future Directions - The industry is grappling with the technical challenge of integrating simulation and real data effectively, as highlighted by Chen Yuanpei from Lingchu Intelligent, who notes that data from different sources must be weighted differently [9]. - The consensus is that while simulation data is beneficial for initial training phases, real data is indispensable for achieving advanced capabilities in robots [10][12]. - Companies are increasingly focusing on building extensive real-world data sets to enhance their models, with Zhiyuan Robotics aiming to create a comprehensive dataset to support embodied intelligence [10][12].