Zhi Tong Cai Jing·2025-11-03 01:05Core Insights - The development of embodied intelligence is accelerating due to the synergy of capital, policy, enterprises, and talent, with training data being a crucial element transitioning from "semi-commercial" to "fully commercial" [1] - The industry faces a significant challenge of a large-scale training data shortage, with real data being essential for effective model training [2] - Data collection factories are emerging as new commercial entities, with local governments and manufacturers collaborating to establish these facilities [3] - The industry is experiencing data silos, necessitating standardized data protocols and representative case studies to enhance interoperability [4] Group 1: Industry Development - The embodied intelligence industry is thriving, driven by collaboration among capital, policy, enterprises, and talent, leading to rapid industrial growth [1] - The lack of large-scale, usable training data is a major challenge for the development of embodied models, highlighting the importance of real data [2] - Data collection factories are being established across various regions, with significant potential for generating effective data and revenue [3] Group 2: Data Collection and Commercialization - Local governments are increasingly partnering with manufacturers to create data collection factories, which can produce thousands of hours of effective data annually [3] - The cost of data collection labor in China is significantly lower than in North America, providing a competitive advantage for scaling data collection efforts [3] - The emergence of data corpus companies is noteworthy, as they aim to reduce the cost of data acquisition in the industry [4] Group 3: Standardization and Collaboration - The industry is facing data silos due to non-standardized datasets and differing technical routes, which hinder data sharing and collaboration [4] - The establishment of the Embodied Intelligence Data Alliance and the release of the first humanoid robot dataset standard are steps towards addressing these challenges [4] - Companies that focus on data quality management are gaining recognition and certification, indicating a shift towards more standardized practices in the industry [4]