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Industrial Software Leaders Build Secure, Autonomous AI Engineers With NVIDIA NemoClaw
5d agoTap to summarizeAccelerated computing has revolutionized industrial engineering, compressing simulation times from weeks to hours. Today’s remaining challenges sit in the end-to-end workflow surrounding the simulations: computer-aided design, meshing, simulation setup and debugging, as well as post-processing and generating summary reports of these processes. At GTC Taipei at COMPUTEX, NVIDIA and more than a dozen engineering software providers are showcasing how autonomous AI agents automate this entire workflow. These AI engineers are based on NVIDIA NemoClaw, an open blueprint for building specialized, long-running agents with a secure runtime and frontier models. NemoClaw includes a choice of harness — meaning it can be integrated with various orchestration frameworks enterprises use to deploy and coordinate agents, such as OpenClaw and Hermes — as well as a model router and NVIDIA NeMo libraries for customization. Users can easily deploy NemoClaw from NVIDIA DGX Spark personal AI supercomputers, as well as through enterprise data centers and cloud service providers. NVIDIA OpenShell — the open source runtime at its core — governs how each agent accesses files, networks and tools, enforcing policy-based security at every layer. Industrial Engineering Leaders Build AI Agents Across Design, Engineering, Simulation Industrial software leaders are building AI engineers for computer-aided engineering (CAE) and electronic design automation (EDA) use cases across automotive, aerospace, semiconductors and manufacturing. Cadence is building an autonomous register-transfer level (RTL) engineer with NemoClaw that orchestrates Cadence Design Systems ChipStack for design and verification. The workflow was featured yesterday in a GTC Taipei keynote demo and is cutting time for RTL verification — a key step in digital circuit design — from weeks to hours. Dassault Systèmes is actively productizing the 3DEXPERIENCE Agentic Platform to operate long-running and autonomous agents for design, simulation and manufacturing operations, in a secured environment powered by NVIDIA NemoClaw and OpenShell. Siemens is integrating NVIDIA NemoClaw and OpenShell into Fuse EDA AI Agent, a purpose-built autonomous agent that plans and orchestrates domain-scoped multi-tool workflows across semiconductor, 3D integrated circuit and printed circuit board system design. Synopsys is collaborating with NVIDIA to apply agents to end-to-end engineering workflows with NVIDIA NemoClaw. Ansys Icepak, part of the Synopsys portfolio, is being demoed on the COMPUTEX show floor this week, used within a NemoClaw-based autonomous AI engineer to mesh, simulate and optimize GPU electronics cooling designs. Image courtesy of Synopsys. Startups Extend the Reach of Agentic AI In addition, cutting-edge startups are building AI engineers for their workflows — all using NVIDIA NemoClaw. Flexcompute is applying OpenShell to its Tidy3D and PhotonForge agents for multiphysics co-packaged optics design. Flexcompute’s autonomous AI workflow combines optical, electrical and thermal simulation to explore thousands of design variants overnight, producing higher-performing components with lower energy consumption. NVIDIA is using Flexcompute technology for the design and optimization of advanced optical and photonic devices. https://blogs.nvidia.com/wp-content/uploads/2026/06/flexcompute-video-cut-1.mp4 Video courtesy of Flexcompute. Luminary is building a long-running AI engineer using NemoClaw to dramatically reduce the time and complexity of training AI physics models by autonomously orchestrating data generation, machine learning model selection, and training and re-training loops. https://blogs.nvidia.com/wp-content/uploads/2026/06/luminary-video-cut.mp4 Video courtesy of Luminary. Neural Concept is deploying an agent for electric motor design. The workflow chains electromagnetic, structural and noise, vibration and harness simulations in a multistep engineering pipeline. https://blogs.nvidia.com/wp-content/uploads/2026/06/neural-concept-video-cut.mp4 Video courtesy of Neural Concept. nTop, the geometry engine behind JetZero’s blended-wing-body aircraft program, is using NVIDIA NemoClaw to run autonomous design workflows that compress days of geometry iteration into hours. https://blogs.nvidia.com/wp-content/uploads/2026/06/ntop-video-cut.mp4 <p style="text-a...
Transcribe →NVIDIA Factory Operations Blueprint Gives Factories a New AI Brain
6d agoTap to summarizeAs factories move from isolated automation to plant-wide intelligence, manufacturers need AI systems that can connect live machine signals, quality systems, work instructions and operational alerts into a unified decision layer. Today at GTC Taipei at COMPUTEX, NVIDIA announced the NVIDIA Factory Operations Blueprint (FOX) — a reference design for building an autonomous factory manager agent that continuously monitors and reasons across the real-time data and orchestrates a fleet of speciality agents and machines to quickly resolve issues at scale. FOX helps developers build secure, centralized factory manager agents for orchestrating and optimizing specialized industrial AI agents for quality control, material transport and worker safety. Built with NVIDIA NemoClaw, AI-Q Blueprint and NVIDIA Nemotron open models, the blueprint provides a customizable foundation for connecting factory systems, automating model development and running intelligent operations at scale. The blueprint is optimized to run on NVIDIA DGX Station, the ultimate deskside AI supercomputer companion for factory managers. DGX Station is powered by the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, featuring 20 petaflops of FP4 performance and 748GB of coherent memory, and is capable of running large AI models up to 1 trillion parameters, making it ideal for developing and running powerful AI agents locally. The superchip features the NVIDIA Blackwell Ultra GPU connected to a high-performance NVIDIA Grace CPU using the NVIDIA NVLink-C2C interconnect to deliver best-in-class system communication and performance, ideal for lightning-fast interactions between NemoClaw and AI models. Key capabilities of the FOX blueprint include: Connecting factory systems and agents: FOX integrates with industrial data sources, machines, applications and robot fleets, and can connect to specialized agents from leading software developers through standard application programming interfaces and agent skills. Automating AI model training: Using NVIDIA TAO skills, factory manager agents can automate the full model-training lifecycle — identifying accuracy gaps, sourcing or synthetically generating training data, fine-tuning models and redeploying them into production. Operating intelligent factory workflows: Visual inspection, process compliance and material transport agents can be managed with NVIDIA open models and blueprints, including the NVIDIA Metropolis Blueprint for video search and summarization (VSS). Real-time factory data can also be visualized in an operational twin built with NVIDIA Omniverse libraries. Taiwan manufacturers Advantech, Foxconn, Pegatron and Wistron are the first to deploy autonomous factory manager agents using the NVIDIA FOX blueprint and NemoClaw. Foxconn, the world’s largest electronics manufacturer, is using the FOX blueprint and NemoClaw to build MoMClaw, a manufacturing operations multi-agent system. Running alongside a live production work, MoMClaw connects sensors, machine signals and other digital systems with hundreds of specialized agents in a single agentic layer — giving plant managers and operators real-time answers and action plans through a natural language interface with NVIDIA OpenShell privacy controls and safety guardrails. With MoMClaw, Foxconn projects an 80% improvement in root cause analysis time, a 15% increase in labor productivity and a 10% decrease in machine failure rates. https://blogs.nvidia.com/wp-content/uploads/2026/05/5172573_RoboticFactory_V27_BlogPost_Foxconn_Caption_v02.mp4 Pegatron is using the FOX blueprint and NemoClaw to build a factory manager agent that orchestrates specialized agents for material transport, AI inspection, standard operating procedure guidance and machine-to-machine coordination. With the factory manager agent, Pegatron can orchestrate robot utilization more efficiently, eliminating the need for expensive standby equipment, with an estimated 15% reduction in asset redundancy costs. https://blogs.nvidia.com/wp-content/uploads/2026/05/5172573_RoboticFactory_V27_BlogPost_Pegatron_Caption_v02.mp4 Advantech has introduced the AI Factory Brain, an intelligent multi-agent system led by a factory manager agent built with the FOX blueprint and NemoClaw. Advantech has deployed the factory manager agent in its own factories to autonomously manage energy across HVAC and lighting specialized agents and projects to cut energy consumption by 10%. Wistron is adopting the FOX blueprint and using NVIDIA Cosmos, NVIDIA Nemotron open models and the NVIDIA Metropolis VSS blueprint to build surface-mount technology agents that analyze and orchestrate production-line operations, enabling real-time root-cause analysis and quality control. To monitor manufacturing operations, improve quality, verify standard operating procedures and improve worker safety, companies including </span...
Transcribe →NVIDIA Research Advances Robotics From Simulation to the Real World
1w agoTap to summarizeRobotics is entering a new phase: moving from controlled demos and scripted automation toward generalizable, reliable embodied autonomy in the real world. At the International Conference on Robotics and Automation (ICRA), eight of NVIDIA Research’s 28 accepted papers show how simulation-to-real transfer is becoming a foundation for that shift, helping robots perceive, reason, plan and act across dynamic, unpredictable environments. Together, the papers span the full stack of challenges robot developers face: coordinating multiple arms in parallel, building policies that generalize across robot bodies, grasping novel objects in clutter, performing precise assembly and developing vision-language-action models that reason before they move. The throughline is clear: sim-to-real is becoming a foundation for robots that can adapt, generalize, and operate with greater reliability outside the lab. Coordinating Arms, Navigating Bodies, Grasping Objects Picture a pharmaceutical lab run by robotic arms: picking up tubes, transferring liquids, mixing reagents — each step taking different amounts of time, all requiring careful coordination. Traditional robot scheduling software handles those steps sequentially, one arm at a time. ScheduleStream changes that by running computations on GPUs, letting multiple arms plan movements and operate in parallel. The result — a 3x speedup across multi-arm planning scenarios, on hardware like the NVIDIA Jetson edge AI platform. Code for the framework is available on GitHub. https://blogs.nvidia.com/wp-content/uploads/2026/05/supplementary.mp4 A robot that learns to navigate through a space — avoiding obstacles and finding its destination — usually learns to do it in one body. Put the same navigation software into a differently shaped robot and it often falls apart, because its parts all move differently. The COMPASS policy framework solves this by first building the baseline navigation functionality using imitation learning and then using residual reinforcement learning in NVIDIA Isaac Lab to build specialists for diverse robot embodiments. Crucially, no real-world robot data is involved at any stage: everything is trained in Isaac Lab simulation. Compared with an imitation learning baseline, COMPASS achieved a 4.5x improvement in average success rate. It also seamlessly transfers to real-world environments, demonstrating around 80% success across 20 real-world navigation trials on autonomous mobile robots and humanoids. COMPASS is agent-friendly, with dedicated skills — and developers can connect the pipeline with NVIDIA Omniverse NuRec to post-train and validate robots in a digital twin of a novel environment before deployment. Most grasping systems identify the object, predict a grasp, plan a path, then execute. But the last few centimeters are where small errors matter most. Grasp-MPC adaptively computes robotic grasps, continuously correcting the robot’s motion as it closes in on the object, rather than carrying out a fixed plan — the way a person grabs something by feeling rather than calculating every joint angle in advance. To build the policy, the researchers generated 2 million simulated trajectories across 8,000 objects using annotations from the GraspGen dataset and motion planning data from cuRobo, a CUDA-accelerated library for robot motion generation. After training on both successful and failed trajectories, Grasp-MPC learned to grasp novel objects in cluttered tabletops and shelves — achieving around 75% overall success on real robots, compared with a baseline of 41%. https://blogs.nvidia.com/wp-content/uploads/2026/05/Sequential-Object-Grasping-2.mp4 Deformable Cluster Manipulation introduces a framework that tackles a parallel challenge: enabling systems to grasp not just one object, but a whole bundle of flexible, tangled material at once. The framework was motivated by a real-world task: clearing a mass of tree branches that have grown over a power line, where there’s no single clean object to grab. The system uses its entire arm, not just the gripper: wrapping it around the branch cluster and sweeping it aside, the way someone might gather an armful of cables or push a tangle of brush out of the way. The researchers built a tree generator using biological growth equations to create synthetic trees of many different shapes and sizes — then trained the system across thousands of them in NVIDIA Isaac open simulation frameworks. The policy deploys to real branches zero shot. Beyond power lines, the researchers see potential in cable management, agricultural inspection and anywhere robots need to handle a tangle rather than a single graspable item. Clearing tree branches in zero-shot sim-to-real deployment. Assembling With Precision Precise assembly — threading a nut onto a bolt, inserting a gear onto a gearshaft, pressing a peg into a hole — is notoriously hard to get right with simulation alone. The real world is complex. Real surfaces aren’t perfectly smooth. Sensors don’t behave as specified. Tiny discrepancies that a simulator ignores can stop a robot in its tracks. The SPARR method addresses this by splitting the job in two. A policy trained in Isaac Lab learns the general strategy for the assembly task in simulation. Then, on...
Transcribe →NVIDIA GTC Taipei at COMPUTEX: Live Updates on What’s Next in AI
2w agoTap to summarizeAt NVIDIA GTC Taipei at COMPUTEX, the world’s developers, researchers and industry leaders are converging to dive into the latest breakthroughs shaping every industry, covering topics spanning AI factories and scaling infrastructure to agentic and physical AI and more. This is the place to find all the latest — stay tuned to the blog for live updates. Jump to the keynote recap. Tuesday, June 2, 10:30 p.m. PT Build-a-Claw Comes to Taipei, Bringing Long-Running AI Agents The Build-a-Claw experience has come to GTC Taipei — putting secure, long-running agent development directly into the hands of the APAC developer community. Build-a-Claw signals how rapidly the developer community and ecosystem are scaling agents. Starting with OpenClaw and Hermes Agent, attendees configured their claw’s persona, added agent skills and set its schedule. Then, they used NVIDIA NemoClaw blueprints and the NVIDIA OpenShell runtime to deploy their agent safely and securely for their environment. https://blogs.nvidia.com/wp-content/uploads/2026/05/build-a-claw-13mb.mp4 Claws, aka long-running agents, are a class of AI systems that go beyond mere prompt-answering. Unlike agents that complete a single task and vanish, claws persist: They work toward a goal, adapt when they hit obstacles, surface status updates and keep executing in the background even after a developer steps away. They’re the engine behind intelligent enterprise automation, agentic commerce and autonomous infrastructure — and building them right demands more than clever architecture. It demands a secure runtime. NVIDIA NemoClaw combines flexible support for agent harnesses — aka agent orchestration frameworks — with NVIDIA OpenShell as the secure runtime. It works with harnesses such as OpenClaw and Hermes Agent, giving developers a hardened, sandboxed foundation for claw development. OpenShell provides the runtime security boundary: isolating agent workloads, enforcing policy and keeping autonomous execution within guardrails that developers and their organizations can trust. NemoClaw blueprints further lower the barrier to entry by giving builders ready-to-adapt patterns for creating secure, enterprise-ready agents. Tuesday, June 2, 5:00 p.m. PT NVIDIA Isaac GR00T Accelerates Humanoid Robot Development From Data to Deployment Building humanoids is complex, and progress often depends on how quickly teams can move through the full development loop: collect demonstrations, generate and refine data, train policies, test in simulation, validate the full software stack and deploy on real hardware. Developers today must handle many disconnected tools and handoffs across that workflow. Major updates to NVIDIA Isaac GR00T, an open, end-to-end development platform for humanoid robots, are accelerating that cycle. The platform unifies technologies including Isaac Teleop, Isaac Lab, Isaac Sim, Isaac ROS, GR00T open models and NVIDIA Jetson Thor for real-time inference and control, giving developers a prescriptive way to move from data to deployment. Agility, Boston Dynamics, Dyna Robotics, Figure, FieldAI, Noble Machines, Richtech Robotics and Skild AI are using core components of NVIDIA’s humanoid robotics stack to accelerate robot development. The development flywheel is already gaining momentum. GR00T models have reached 274,000 downloads, while the <a target="_blank" hre...
Transcribe →National Robotics Week — Latest Physical AI Research, Breakthroughs and Resources
Apr 7Tap to summarizeThis National Robotics Week, NVIDIA is highlighting the breakthroughs that are bringing AI into the physical world — as well as the growing wave of robots transforming industries, from agricultural and manufacturing to energy and beyond. Advancements in robot learning, simulation and foundation models are accelerating development, enabling robots to move from training in virtual environments to real-world deployment faster than ever. With NVIDIA platforms for simulation, synthetic data and AI-powered robot learning, developers now have the tools to build machines that can perceive, reason and act in complex environments. Check back here all week for coverage on the latest NVIDIA physical AI technologies. University of Maryland Researchers Develop Robots for Complex Household Tasks To bring robots into everyday life, researchers at the University of Maryland are developing AI-powered humanoid systems capable of performing complex household tasks with greater autonomy. The project centers on building robot foundation models that unify perception, planning and control. Using the NVIDIA Isaac open robotics development platform, researchers can create photorealistic, high-fidelity virtual home environments populated with diverse objects and layouts, allowing robots to practice millions of task variations and safely test rare or complex scenarios. NVIDIA RTX PRO 6000 Blackwell GPUs for training large models and NVIDIA Jetson AGX Thor developer kits for efficient deployment on physical robots help bridge the gap between research and real-world applications. By combining advancements in generative AI, sequential decision-making and scalable computing, the work represents a key step toward general-purpose robots that can support people in homes, healthcare settings and beyond. Announcing the MassRobotics Fellowship The second cohort of the Amazon Web Services (AWS) MassRobotics fellowship comprises startups being recognized for compelling industrial use cases harnessing robotics and computer vision. They will receive access to technical resources and AWS cloud credits. The cohort includes NVIDIA Inception members Burro, Config Intelligence, Deltia, Haply Robotics, Luminous Robotics, Roboto AI, Telexistence, Terra Robotics and WiRobotics, each developing technologies spanning humanoid robotics, industrial automation, haptics and agricultural systems. Burro creates autonomous agricultural robots for tasks like grape harvesting and crop scouting. Config Intelligence builds data infrastructure for general-purpose bimanual robotics to enable reliable two-handed tasks in real-world settings. Deltia provides AI-driven manufacturing intelligence that optimizes assembly lines using computer vision and analytics. Haply Robotics designs haptic control devices that serve as “steering wheels” for physical AI systems across industries. Luminous Robotics deploys AI-powered robotic systems for fast, low-cost solar-panel installation and maintenance. Roboto AI offers a data-analytics platform that accelerates robot development by managing and analyzing robotics data. Telexistence develops AI-powered humanoid robots and remote-controlled systems for retail and logistics. Terra Robotics develops laser-weeding agricultural robots to automate sustainable farming. WiRobotics creates wearable walking-assist and humanoid robots to enhance mobility and physical interaction, using training data from assisted products to train its humanoids. Accelerating How Utility-Scale Solar Projects Are Built in the Field <img src="https://s.w.org/images/core/emoji/17.0.2/72x72/1f517.png" alt="🔗" class="wp-smiley" style="height: 1em; max-height: 1em;...
Transcribe →Blowing Off Steam: How Power-Flexible AI Factories Can Stabilize the Global Energy Grid
Mar 25Tap to summarizeAt the half-time whistle of the UEFA EURO 2020 round of 16 football match between England and Germany, millions of viewers stepped away from their screens in the U.K. to do the same thing at the same time — turn on their kettles. National Grid, which provides electricity for England and Wales, saw a demand spike of about 1 gigawatt — an increase equivalent to the average output of a standard nuclear reactor — in a matter of minutes from this countrywide tea break. Grid operators must carefully manage these demand peaks to keep the system stable, and this could become even more difficult as the grid continues to add large new customers. But what if those new customers could actually be flexible and relieve the grid during periods of peak strain? In a recent white paper, Emerald AI — in collaboration with NVIDIA, EPRI, National Grid and Nebius — showcased how “power-flexible” AI factories can autonomously adjust their power usage during peak demand. For AI factories, this could unlock significantly faster grid connections without waiting for massive, years-long infrastructure upgrades. For the public, it helps limit grid build outs by curbing the peak load that the system needs to serve, helping keep electricity rates affordable for everyday bill payers. Boil the Kettle, Balance the Grid After successful proof-of-concept trials at AI factories in Arizona, Virginia and Illinois, Emerald AI took its flexible grid solution across the pond, last December, bringing the Emerald AI Conductor Platform to Nebius’ new AI factory in London, built on NVIDIA infrastructure — among the first of its kind in the U.K. At the AI factory, the research team ran production-grade AI workloads on a cluster of 96 NVIDIA Blackwell Ultra GPUs connected through the NVIDIA Quantum-X800 InfiniBand platform. The NVIDIA System Management Interface is used to retrieve consistent, seconds-level GPU power telemetry. EPRI and National Grid simulated stress scenarios on the power grid — from lightning strikes to long periods of low wind power supply — and sent signals instructing the AI factory, with the help of the Conductor Platform, to temporarily reduce its power use to relieve grid strain. One of these scenarios was the “TV pickup” phenomenon, where that very same Euro 2020 football match’s energy surge was reenacted. As millions of simulated tea kettles were about to be turned on, the AI cluster ramped down its power use — successfully acting as a shock absorber for the abrupt power surge without disrupting the highest-priority AI workloads running on the cluster. https://blogs.nvidia.com/wp-content/uploads/2026/02/Grid-Responsive-AI-Infrastructure-Chart_v4.mp4 In practice, this means the grid can manage sudden demand swings using existing capacity more efficiently, reducing the need to overbuild permanent infrastructure to meet worst-case peaks and helping keep rates affordable for everyday consumers. “With this technology, AI factories become friendly and helpful grid assets,” said Varun Sivaram, founder and CEO of Emerald AI. “Simultaneously, the AI factories get connected much faster to the grid because they can tap into existing power grids.” Stress Relievers, Not Query Crushers In the Nebius AI factory demonstration, despite the quick ramp down of energy to power the national tea break, Emerald AI Conductor ensured that the simulated high-priority AI workloads performed at peak throughput, while more flexible jobs were slowed down temporarily. Emerald AI recorded 100% alignment with over 200 power targets that EPRI and National Grid instructed the AI cluster to follow for this experiment. “We did tests that go beyond the ones that have been done so far in the U.S. because we tested not just the GPUs, but also the CPUs and everything that sits around it — as well as the total power consumption of the IT equipment,” said Steve Smith, group chief strategy officer of National Grid. “We’ve proved the value that this technology brings.” Scaling London’s Grid at Super Speed London’s power grid is constantly working to meet the ever-growing energy needs of its citizens. Its grid operators — including National Grid — face a key bottleneck: constraints in infrastructure upgrades to connect large customers. Plugging flexible AI factories into the grid with solutions like Emerald AI’s Conductor Platform won’t just help to stabilize energy spikes — it can optimize the use of existing grid infrastructure to propel new industry talent and economic opportunities in the U.K. “We have enormous skills and potential in AI,” said Smith. “We’re never going to be on the scale of the U.S. in terms of data centers, but relative to the size of the country, we could be — and we’re certainly seeing that interest from many of the hyperscalers. So, it gives us the opportunity to play our part as National Grid in helping unlock that economic growth for the country.” Four demonstrations in, Emerald AI and NVIDIA are gearing up to put power-flexible AI factories into real-world deployment with the Aurora AI Factory in Virginia, set to open this year. Learn more about the first power-flexible AI factory powered by NVIDIA GPUs.
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