（文章来源：南方都市报） 在仿真技术这一机器人发展核心赛道，英伟达Isaac Sim成为大会焦点，其作为高保真数字孪生平台，被 黄仁勋定义为降低物理AI落地成本的关键路径，也是机器人进化的"加速器"。 物理AI的成功关键在于海量数据生成能力，而仿真正是实现这一目标的核心手段。依托Isaac Sim与 Omniverse生态，英伟达为全球开发者提供了从仿真训练到真机部署的技术底座，而智元机器人正是基 于这一底座，打造出全球首个实现"仿真验证-真实部署"全闭环的Genie Sim 3.0仿真平台。该企业还与英 伟达达成全链路技术协同，成为其物理AI全球布局中的重要中国力量。 智元仿真业务负责人吴墨在大会发表主题演讲，详细展示了Genie Sim 3.0与英伟达技术体系的深度融 合。该平台构建了从数字资产生成、场景泛化、数据采集到自动评测的全流程闭环解决方案，可精准还 原超市上货、物流分拣等多类工商业场景。 此外，平台的工业化落地成果也成为英伟达仿真技术商用的标杆——依托其训练的模型搭配智元精灵 G2机器人落地物料搬运产线，仅2万帧仿真数据训练即实现真机抓取100%成功率，印证了英伟达Isaac Sim在工业场景的 ...

Core Insights - Synopsys and NVIDIA are showcasing their strategic partnership at GTC 2026, focusing on revolutionizing design and engineering across various industries through AI and accelerated computing solutions [1][2] - The collaboration aims to address significant engineering challenges such as workflow complexity, development costs, and time-to-market pressures by integrating NVIDIA's AI capabilities with Synopsys' engineering solutions [1][2] Group 1: Partnership Impact - The partnership is enabling R&D teams to design, simulate, and verify intelligent products more efficiently, resulting in lower costs and increased precision [1] - Synopsys is demonstrating how AI and accelerated computing are fundamentally changing engineering practices, particularly in product design and operation [2] Group 2: Engineering Workload Acceleration - Synopsys has the broadest portfolio of engineering applications that leverage AI and GPU-accelerated computing, enhancing the speed and intuitiveness of engineering processes [3] - Astera Labs achieved a 3.5X speedup in chip design simulations using Synopsys PrimeSim on NVIDIA B200 GPUs, significantly reducing design validation cycles [4][5] - Honda reported a 34X faster computation and 38% cost reduction in CFD simulations by utilizing four GB200 GPUs compared to 1,920 cloud-based CPU cores [7] Group 3: Advancements in Physical AI - Synopsys is playing a crucial role in physical AI development by grounding virtual processes with real-world physics, thereby improving simulation accuracy and reducing the need for physical testing [8] - ADI is using Synopsys' physics in its Isaac Sim environment to create high-fidelity simulation assets for robotic applications, enhancing predictive accuracy [8] Group 4: Quantum Chemistry and Materials Engineering - Applied Materials is collaborating with Synopsys and NVIDIA to accelerate quantum chemistry simulations, achieving a potential 30X speedup for complex workloads compared to traditional CPU models [7] - This collaboration aims to improve energy-efficient performance in advanced semiconductor devices, facilitating faster market entry for chip design innovations [7] Group 5: Agentic AI Development - Synopsys is developing an open, secure, hardware-accelerated agentic AI stack in partnership with NVIDIA, targeting applications from silicon to systems [9][10] - The AgentEngineer technology is designed to enhance electronic design automation workflows, improving productivity and managing design complexity in the AI era [10]

What’s exciting is how fast the technology is evolving. We see a lot of progress on large AI models into our physical world. We’re seeing development of some really groundbreaking technologies coming together, changing what we can do with robotics.Physical AI is going to have a transformational impact on the real physical world. Jensen’s done a really good job describing the compute and the platforms that it takes to be able to get robotics at scale. We have to take the data that we have, 100 years of data ...

What’s exciting is how fast the technology is evolving. We see a lot of progress on large AI models into our physical world. We’re seeing development of some really groundbreaking technologies coming together, changing what we can do with robotics.Physical AI is going to have a transformational impact on the real physical world. Jensen’s done a really good job describing the compute and the platforms that it takes to be able to get robotics at scale. We have to take the data that we have, 100 years of data ...

Core Insights - Microsoft Research has launched Rho-alpha, a new robotic model designed to help robots understand natural language commands and perform complex physical tasks in less structured environments [1] - Rho-alpha aims to advance the next generation of robotic systems, enabling them to perceive, reason, and act in dynamic real-world settings [1] - The model is part of a trend towards "visual-language-action" models that enhance the autonomy of physical systems [1] Group 1 - Rho-alpha integrates touch data and is currently being researched to support additional sensory modalities, such as force sensing [2] - The model is designed to improve continuously during deployment by learning from user feedback during interactions with robots [2] - The training of Rho-alpha heavily relies on synthetic data, utilizing a multi-stage training process that combines reinforcement learning and simulation technology [2] Group 2 - A major challenge for foundational models is the lack of diverse real-world robotic data [4] - Researchers are collaborating with Microsoft to enhance pre-training datasets using synthetic demonstrations, addressing the impracticality of remote operation in many cases [4] - NVIDIA emphasizes the role of synthetic data in accelerating the development of robotic technologies, highlighting the collaboration with Microsoft to generate high-fidelity synthetic datasets [4] Group 3 - Microsoft has opened registration for the early access program for Rho-alpha and plans to release more updates on its robotic research in the coming months [4]

Core Insights - The article discusses the transition of artificial intelligence (AI) from digital applications to physical applications, marking a significant shift in 2026 towards "physical AI" [1][4][12] Group 1: Development of Physical AI - Physical AI is characterized by its ability to understand physical laws and interact with the real environment, enabling a new research paradigm of "hypothesis - AI simulation - experimental verification" [4] - The development of physical AI is expected to take 5 to 10 years of deep cultivation, indicating a long-term investment in this area [4] - The concept of "world models" is crucial for physical AI, allowing intelligent agents to simulate actions in a virtual environment before executing them in reality, which is essential for safety and efficiency [5][6] Group 2: Data Generation and Training - The industry is leveraging "synthetic data" generated from high-fidelity physical simulation engines to train AI models at zero marginal cost, although there remains a challenge in bridging the "simulation to reality" gap [7] - A promising approach involves using human daily videos for pre-training AI models, allowing them to learn physical common sense and operational skills from real-world scenarios [7] - The future of physical AI data solutions may involve a "trinity" ecosystem composed of human experience, virtual simulation, and physical interaction [7] Group 3: Global Competitive Landscape - The development of physical AI shows a contrast between the U.S. and China, with the U.S. leading in foundational algorithms and cutting-edge exploration, while China excels in engineering and rapid deployment of technologies [9][10] - China's strategy emphasizes cost-effectiveness and clear application scenarios, supported by government initiatives that integrate AI into various sectors, setting ambitious goals for technology adoption [10] Group 4: Challenges and Future Directions - The ultimate goal of physical AI is to achieve generalization, enabling intelligent agents to adapt quickly to new environments and tasks, which remains a significant challenge [11] - Issues such as explainability, safety redundancy, and ethical standards are becoming increasingly important in the physical AI era, as the consequences of errors can have real-world implications [11] - The year 2026 is seen as a milestone, marking the beginning of AI's transition from virtual to physical applications, with ongoing advancements expected [12]

Core Insights - Nvidia's AI chip business generated nearly $148 billion in revenue over the past nine months, significantly surpassing the $27.5 billion from the same period in 2023, but the company's transition to an integrated hardware-software platform has faced major setbacks [1] - The Omniverse software, which was intended to be a core tool for creating digital twins in manufacturing and logistics, has seen minimal revenue and a stalled commercialization process, leading to the decision to shut down the Omniverse Cloud service by August 2025 due to lack of demand [1][3] - CEO Jensen Huang expressed frustration over the slow progress of the Omniverse division, criticizing the team for focusing on demonstrations rather than product development, and highlighting the lack of widespread adoption by large enterprises [1][4] Revenue and Market Response - Despite the explosive growth in AI chip revenue, the market has not reacted strongly to the revenue gap from Omniverse, indicating the challenges Nvidia faces in establishing a second growth curve [2] - The inability to address software usability and industry adaptation issues may hinder Nvidia's ambitions in robotics and industrial digitalization for the long term [2] Demand and Service Closure - Omniverse was launched in 2021 as a platform for designers to collaborate on 3D designs, but the reality has fallen short of expectations, with few clients actually signing on for large-scale simulations [3] - Developers have reported that the platform is difficult to use, incomplete, and prone to crashes, leading to the termination of the cloud service project [3] Internal Pressure and Management Concerns - Huang's anxiety over Omniverse's performance is evident, as he has pressured the team to find new revenue sources and has expressed frustration in internal meetings regarding the lack of profitability and the team's focus on demonstrations [4] - The actual outcomes of collaborative projects have also led to dissatisfaction among management, particularly regarding the scale of partnerships with companies like BMW [4] Long-term Challenges and Industry Barriers - Nvidia executives compare Omniverse to CUDA, suggesting that it may take years of investment to fully realize its potential in the "physical AI" market [6] - The company faces intense competition and structural barriers in the robotics simulation field, with many large enterprises preferring to develop their own internal simulation software rather than relying on Nvidia's platform [6] - Industry-specific technical challenges and cost-effectiveness issues also pose significant obstacles to the widespread adoption of Omniverse [6][5] Development and Market Creation - Currently, Omniverse is seen as a horizontal open platform for developers rather than a complete application, indicating that Nvidia's attempt to create a market from scratch will require a lengthy nurturing period [7]

Core Insights - Nvidia has experienced remarkable growth in its AI chip business, with revenue soaring from $27.5 billion in the first nine months of 2023 to nearly $148 billion in the same period of 2024, a growth rate that is rare in the tech industry history [1] - CEO Jensen Huang is not satisfied with this growth and is betting on the next phase of Nvidia's development in robotics and manufacturing through the Omniverse platform [2][4] - However, the Omniverse initiative has not met expectations, leading to frustration from Huang [3][9] Group 1: Omniverse Overview - Omniverse was initially launched with high ambitions, with Huang emphasizing its strategic importance and potential to capture a share of the $50 trillion manufacturing and logistics market [4][6] - Despite the high-profile endorsements and partnerships, insiders reveal that Omniverse has made little substantial progress over four years, with very few companies actually utilizing its cloud services for large-scale simulations [7][10] - Developers have criticized the Omniverse tools for being difficult to use and prone to crashes, with one developer noting that the platform fails when attempting complex simulations [8][12] Group 2: Challenges and Limitations - The complexity of simulating physical behaviors in robotics and manufacturing is far greater than anticipated, particularly when dealing with flexible materials and fluid dynamics [11][12] - Omniverse's initial vision of a universal simulation platform has proven inefficient, as specific simulations for particular scenarios are more effective [13][14] - Many companies prefer to develop their own simulation software, as seen with Tesla, which indicates a reluctance to adopt Nvidia's offerings [15][19] Group 3: Strategic Implications - The setbacks with Omniverse could have broader implications for Nvidia's strategic positioning within the tech industry, as it seeks to transition from a hardware manufacturer to a provider of comprehensive ecosystems [20][21] - If Omniverse fails, Nvidia risks losing its opportunity to define the next generation of standards in the manufacturing and robotics sectors, potentially relegating it to a mere hardware supplier [22][23] - Competitors are already encroaching on the market, with companies like Unity Technologies and Gazebo gaining traction, which could threaten Nvidia's market share [18][22] Group 4: Future Outlook - Huang's concerns about the slow adoption of Omniverse by large companies reflect a broader anxiety about establishing a unified standard in a fragmented market [27][28] - The rapid development of the robotics industry presents a critical window for Nvidia to establish its standards; failure to do so may hinder its influence in future technological landscapes [30][31] - While the market demand for simulation technology exists, the timing for its explosion remains uncertain, and Nvidia's ability to define the ecosystem will be crucial for its long-term success [31][33]

Group 1: Investment and Collaboration - Andhra Pradesh's IT and Industries Minister Nara Lokesh met with Sundar Pichai and Shantanu Narayen to review the $15 billion investment in the Visakhapatnam AI Data Center, which is expected to be one of the largest foreign direct investment (FDI) projects [1] - The Minister invited Google to establish a drone assembly, calibration, and testing unit in the upcoming Drone City and to enhance the server manufacturing ecosystem in Andhra Pradesh [2] - Discussions were held with NVIDIA's Raj Mirpuri regarding AI skill development, smart manufacturing, and future technologies, including a request to set up a Smart Factory Pilot using Omniverse & Isaac Sim [3] Group 2: Technology and Manufacturing Initiatives - The Minister invited Intel to explore the establishment of an ATMP (Assembly, Testing, Marking & Packaging) unit in Andhra Pradesh [4] - Meetings with OpenAI's CTO and AMD's Vice-President were conducted to consider potential investments in Andhra Pradesh [4] - The focus on deeper collaboration in fabless design, research, and leveraging health-tech and life sciences investments was emphasized during discussions with Adobe [2]

Core Viewpoint - Fanuc Corp. is collaborating with Nvidia to integrate its ROBOGUIDE robot simulation software with Nvidia's physical AI engine, marking a significant shift from traditional automation to a focus on intelligent industrial robotics and physical AI platforms [1][5]. Group 1: Collaboration Details - The partnership aims to enhance virtual simulation and real production line integration, strengthening Fanuc's position in high-end industrial manufacturing [1]. - Fanuc is integrating Nvidia's open-source robot simulation framework into its software system to facilitate virtual operation testing for its industrial robots [2]. Group 2: Market Context and Implications - The collaboration comes amid increasing competition in Japan's industrial robotics sector, particularly with SoftBank's planned acquisition of ABB's robotics division, which poses a direct challenge to Fanuc's core business [3]. - Analysts suggest that industries heavily reliant on manual labor, such as logistics, food, and automotive assembly, will be the first beneficiaries of the new wave of AI-driven industrial robotics [2][3]. Group 3: Future Outlook - The evolution towards a "physical AI" platform signifies a shift in the value chain from hardware sales to a model that includes hardware, computational power subscriptions, digital twin/simulation software, and AI model services [2]. - Nvidia's Isaac Sim is positioned as a core component of the physical AI technology stack, enabling robots to perceive, reason, and act in the real world [4].