深度｜登顶世界第一，全球具身核心圈用脚投票，卡住行业脖子的数据难题现破局曙光

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].