Core Insights - The rapid development of models like VLA has led to the emergence of various testing benchmarks, but the growth in model capabilities has outpaced existing benchmarks, highlighting a significant issue in the embodied intelligence field: the lack of a standardized measurement system for assessing true model capabilities [2] - The reliance on experience and intuition for R&D decisions has become a systemic risk in the transition from research to engineering in embodied intelligence [2] Group 1: Challenges in the Embodied Intelligence Field - The field is transitioning from storytelling to productivity, showcasing advancements like medical robots and mobile operation robots, but there are underlying industry consensus issues regarding the limitations of models and their ability to generalize across different tasks and environments [3][4] - The need for comprehensive generalization capabilities is emphasized, as robots must perform well in varied scenarios without being overly specialized, which is currently a challenge for many companies in the industry [5][6] Group 2: Testing and Evaluation Issues - The current testing landscape lacks standardized, scalable evaluation methods, leading to a reliance on limited testing scenarios that do not adequately measure model capabilities [10][12] - The industry consensus is that real-world testing cannot be scaled effectively, making simulation the only viable path for evaluation [13][21] Group 3: The Need for Industrial-Grade Evaluation Systems - There is a pressing need for a unified, scalable, and deterministic evaluation infrastructure that can support industrial-level decision-making in embodied intelligence [21][22] - NVIDIA and Lightwheel Intelligence's collaboration to create the Isaac Lab-Arena represents a significant step towards establishing a scalable evaluation framework in the field [23][24] Group 4: Features of the Isaac Lab-Arena - The Arena allows for flexible task creation and evaluation, moving away from rigid scripts to a modular approach that can adapt to various tasks and environments [26][28] - It supports a diverse range of tasks and environments, enabling systematic measurement of model capabilities rather than isolated demonstrations [66][70] Group 5: RoboFinals as an Industrial Benchmark - Lightwheel Intelligence has developed RoboFinals, an industrial-grade evaluation platform with over 250 tasks that systematically expose model failure modes and capability boundaries [63][71] - RoboFinals has been integrated into the workflows of leading model teams, providing continuous evaluation signals rather than just a ranking system [71][73] Group 6: The Importance of Collaboration - The partnership between NVIDIA and Lightwheel Intelligence is notable for its depth, as it combines strengths in simulation technology and real-world application experience to create a comprehensive evaluation system [42][56] - The collaboration aims to ensure that the evaluation infrastructure is not only technically sound but also aligned with the practical needs of model teams and robotic companies [54][56]

Core Viewpoint - The collaboration between World Labs, led by Fei-Fei Li, and Guanglun Intelligent, a leading synthetic data company, aims to address the long-standing issue of "scalable evaluation" in the field of embodied intelligence, marking the entry into an evaluation-driven era for this technology [1][2][3]. Group 1: Companies Involved - World Labs is founded by Fei-Fei Li, a prominent figure in AI, known for her work on ImageNet and as a former chief AI scientist at Google Cloud [4][5]. - Guanglun Intelligent is recognized as a hot company in the embodied intelligence infrastructure sector, having established a strong partnership with NVIDIA and contributing to the development of simulation systems [54][55]. Group 2: Technological Innovations - World Labs is set to launch its first product, Marble, by the end of 2025, which can generate high-fidelity 3D worlds from minimal input [8][9]. - Marble aims to provide a visualized world model, allowing users to create and export 3D environments efficiently, thus serving as a productivity tool for visual effects and game developers [15][16]. Group 3: Challenges in Evaluation - The rapid advancement of models in embodied intelligence has outpaced existing benchmarks, creating a need for new evaluation methods [20][22]. - Traditional evaluation methods are inadequate for assessing the capabilities of embodied intelligence, necessitating the use of simulation as a scalable solution [29][30]. Group 4: Strategic Collaboration - The partnership between World Labs and Guanglun Intelligent is crucial for developing a comprehensive evaluation framework that integrates environment generation and physical interaction [37][49]. - Guanglun Intelligent's role is to provide the necessary physical assets and evaluation loops, ensuring that the simulated environments can support real physical interactions [49][50]. Group 5: Future Directions - The collaboration signifies a pivotal moment in the embodied intelligence sector, as it transitions into an evaluation-driven era, with the potential to shape research directions and identify technological bottlenecks [71][72][76]. - The establishment of robust evaluation standards, such as RoboFinals, highlights the industry's shift towards scalable and credible assessment frameworks for advanced robotic models [63][64].

Group 1 - The core viewpoint of the article highlights Beijing's rapid development as a leading hub for artificial intelligence (AI), showcasing significant advancements in AI technology, particularly in chip development and large model applications [1][2][3] - As of the first half of 2025, Beijing's AI core industry is projected to reach a scale of 215.2 billion yuan, with an expected annual growth rate of 25.3%, potentially reaching 450 billion yuan by the end of the year [1] - Beijing has established a "chip matrix" in the AI chip sector, featuring leading domestic products such as Kunlun, Cambricon, and Moore Threads, which are now integral to real business applications [3][8] Group 2 - The article emphasizes the importance of a cohesive ecosystem that integrates algorithms, chips, and data, enabling AI technologies to transition from laboratories to everyday applications [2] - The launch of the FlagOS 1.6 system software stack by Zhiyuan Research Institute aims to unify various AI chips and frameworks, addressing the compatibility challenges faced by large models [7][8] - Beijing is home to 209 registered large models, accounting for nearly 30% of the national total, with notable models like Doubao and Kimi being developed locally [9][10] Group 3 - The article discusses the innovative capabilities of Beijing's AI models, such as the GLM-4.7-flash, which enhances training efficiency while reducing resource consumption [10][11] - The MiniCPM-o 4.5 model, developed by Mianbi Intelligent, is highlighted for its unique features, including full-duplex and multimodal capabilities, showcasing advancements in human-computer interaction [12][13] - The "density law" of large models, which suggests that their capability density doubles approximately every 3.5 months, has been validated by recent research and is a key aspect of Beijing's AI development [13] Group 4 - Beijing plans to implement nine major actions to further establish itself as a global AI innovation hub, focusing on technology innovation, data quality, and application empowerment [14][19] - The RoboFinals platform, introduced by Guanglun Intelligent, represents a significant advancement in industrial-grade evaluation for embodied intelligence, creating a closed-loop system for data generation, model training, and capability assessment [18][19] - The article concludes by emphasizing the collaborative efforts of various stakeholders in Beijing's AI ecosystem, which have contributed to its sustainable growth and resilience [20]

编辑部 整理自 MEET2026 量子位 | 公众号 QbitAI 从大模型智能的"语言世界"迈向具身智能的"物理世界"，仿真正在成为连接落地的底层基础设施。 在本次量子位MEET2026智能未来大会上，光轮智能联合创始人兼总裁 杨海波 给出了他的观察： 具身智能的规模远大于文本与视觉模型，因为数据维度更真实、更复杂。 这也就意味着，具身智能时代的核心，不是算法本身，而是它所依赖的数据是否有效、可扩展——仿真是唯一能够解决数据问题的方案。 在仿真策略的路上，会遇到仿真不真实、Sim2Real不可靠等行业痛点， 光轮智能正在通过自研的一整套"测量、生成、求解"仿真基础设施来 解决这些问题 ，为具身智能提供数据、训练、评测的全流程解决方案。 △ 杨海波指出光轮智能深耕合成数据领域 另外杨海波还进一步指出， 仿真不是孤立的技术工具，需要以真实产业需求为锚点，通过应用场景构建生态。 其中， 具身仿真资产制作是生态的源头活水 ，依托自动化物理测量与生成技术，产出高物理真实的规范化数据资产，为具身训练提供核心燃 料； 大规模RL训练则通过并行的虚拟场景让智能体高效试错学习，将数据价值转化为具身实际技能 ，同时反向打磨仿真 ...