全自研仿真GPU求解器x虚实对标物理测量工厂，打造具身合成数据SuperApp，加速具身仿真生态丨光轮智能@MEET2026

Core Insights - The transition from large model intelligence in the "language world" to embodied intelligence in the "physical world" highlights the importance of simulation as a foundational infrastructure for practical applications [1] - The scale of embodied intelligence is significantly larger than that of text and visual models due to the more complex and realistic data dimensions involved [2] - The core of the embodied intelligence era is not the algorithms themselves, but the effectiveness and scalability of the data they rely on, with simulation being the only viable solution to address data challenges [3] Simulation Infrastructure - The company is developing a comprehensive simulation infrastructure that includes measurement, generation, and solving capabilities to address industry pain points such as unrealistic simulations and unreliable Sim2Real transitions [3][15] - The simulation ecosystem is anchored in real industry needs, with the creation of high-fidelity synthetic data assets being essential for training embodied intelligence [5] - The company has established the world's largest remote operation data collection factory, a large-scale RL training platform (LW-BenchHub), and the first industrial-grade robot evaluation platform (RoboFinals) to support the transition of embodied intelligence from the lab to the real world [6] Data Opportunities - The data opportunities in embodied world models are estimated to be 1000 times greater than those in large language models, due to the complexity of interactions and feedback mechanisms required in physical environments [14] - Traditional pre-training data for large models is based on existing data, while embodied intelligence faces a significant pre-training demand due to the lack of real-world instances [17][18] Challenges and Solutions - Past simulation failures are attributed to three main issues: unrealistic physics, visual distortion of assets, and inaccurate interaction behaviors [19][20] - The company has developed a "measurement, generation, solving" triad solution to create a simulation factory that aligns closely with the physical world, eliminating reliance on guesswork [21][23] - Accurate parameter identification is crucial for ensuring that simulated robots behave consistently with real-world counterparts, thereby bridging the Sim2Real gap [33] Ecosystem and Commercialization - A robust ecosystem is essential for the sustainable development of simulation platforms, with the company focusing on creating "killer applications" to support ongoing evolution [39][40] - The company’s applications include a global remote operation data collection factory, a large-scale RL training system, and the RoboFinals evaluation platform, which has become a leading standard for assessing robotic models [40][45]