专访光轮智能总裁杨海波：为什么具身智能需要仿真数据

Core Insights - The article highlights the rapid growth of the embodied intelligence sector, with a significant demand for synthetic data, which is currently being met by the company Guanglun Intelligent, founded in 2023 [1][2][4]. Group 1: Company Overview - Guanglun Intelligent has positioned itself as a key player in the synthetic data market, providing AI simulation services that fill a critical gap in the industry [1][2]. - The company claims that over 80% of the simulation assets and synthetic data for leading international embodied intelligence teams come from them [1][7]. - The founder, Yang Haibo, emphasizes the importance of high-quality synthetic data for the development of embodied intelligence, which is expected to be as ubiquitous as smartphones and cars in various industries [2][18]. Group 2: Market Demand and Growth - The demand for synthetic data in the embodied intelligence sector is at least 1000 times greater than that for autonomous driving, driven by the need for complex physical interactions [2][8]. - Initially focused on the autonomous driving sector, the company has seen a surge in demand from the embodied intelligence and world model fields since mid-2024 [8][18]. - The industry has shifted from questioning the use of synthetic data to focusing on how to effectively produce it [6][8]. Group 3: Technical Challenges and Solutions - The main challenges in generating high-quality synthetic data include ensuring physical accuracy and adapting to evolving data requirements from embodied models [10][12]. - The company employs a proprietary "solve-measure-generate" approach to simulation, which allows for precise modeling of complex physical interactions [11][12]. - The training process for synthetic data must balance quality and scalability, with the company aiming to produce large volumes of high-quality data [10][14]. Group 4: Future Outlook - The company envisions itself as a foundational infrastructure provider for the physical AI era, focusing on continuous development of simulation technologies [18]. - The industry is expected to transition from a tool-based phase to a foundational industry phase, with a growing reliance on reliable data support for the widespread application of robots and intelligent agents [18].