圆桌论坛：具身数据如何塑造行业未来？丨GAIR 2025

Core Viewpoint - The article discusses the current state and future potential of embodied intelligence, particularly focusing on the challenges and opportunities in data collection methods and the maturity of the industry [2][25]. Data Quality and Collection - High-quality data is becoming a bottleneck for breakthroughs in embodied intelligence performance and cost control [2]. - The roundtable forum at the GAIR conference emphasized the importance of data quality, with consensus that the effectiveness of models and the benefits to robots depend on the quality of data collected [2][4]. - Different data collection methods, such as UMI (Universal Manipulation Interface), remote operation, motion capture, and simulation data, are being explored, with a focus on adapting to various scenarios and hardware [3][4]. Industry Maturity and Challenges - The data collection industry is still in its early stages, with companies in data, embodiment, and modeling still aligning their needs and capabilities [4][7]. - There is a lack of a unified approach in the industry, with varying demands from different model companies, indicating that data companies need to understand models and provide suggestions to improve collaboration [7][8]. - The current focus on data collection methods is shifting, with a notable rise in interest in remote operation and UMI, particularly influenced by developments in North America [9][10]. In-the-Wild Data Collection - In-the-wild data collection is seen as a challenging yet promising approach, requiring advanced technical capabilities and effective management of hardware and software [3][21]. - The article highlights the need for low-friction, high-precision, and multi-modal data collection devices to effectively utilize in-the-wild data [3][21]. - The maturity of in-the-wild data collection is still developing, with current efforts primarily focused on improving data collection technology before addressing human resource management [21][22]. Government Support and Industry Dynamics - Government support for data collection factories is prevalent in China, which may influence the direction of data collection methods and industry growth [10][17]. - The article suggests that while government-backed data collection initiatives can stimulate the industry, they may not always align with the most effective technological advancements [17][18]. - The cost structure of data collection is critical, with significant portions attributed to equipment depreciation and labor costs, indicating a need for strategic investment in data collection methods [19][20]. Future Outlook - The industry is expected to evolve, with a potential shift towards more diverse data collection methods as companies adapt to changing demands and technological advancements [18][19]. - The article expresses skepticism about the current maturity of the embodied intelligence industry compared to other tech sectors, suggesting that significant challenges remain before widespread adoption can occur [25][26]. - Companies are encouraged to collaborate and share insights to enhance data collection processes and improve overall industry knowledge [28][30].