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Today marks a major step for industrial automation. ABB Robotics and NVIDIA have closed the sim-to-real gap - achieving 99% accuracy with RobotStudio® HyperReality. Design, test and optimize production lines virtually with unprecedented confidence.

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ABB Robotics

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55,764 просмотров • 5 месяцев назад •via X (Twitter)

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🚨 BREAKING: ABB Robotics + NVIDIA close the sim-to-real gap with 99% accuracy! 👾 ABB Robotics is integrating NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy. RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly manufacturers can scale production: reducing costs by up to 40%, accelerating time-to-market by 50%, and cutting setup and commissioning times by up to 80%. For decades, the deficit between simulation accuracy and real-world lighting, materials, and environments has limited manufacturers' ability to design advanced manufacturing processes in the virtual world. The only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near-perfect correlation between simulation and real-world performance. The system uses physically accurate simulations and foundation models endlessly optimized with real-world data feedback. These models can train any number of ABB robots anywhere in the world with industrial-grade reliability. Foxconn is using RobotStudio HyperReality for consumer electronics assembly. Assembly robots are trained virtually using synthetic data to perfect multiple production processes across various scenarios, then moved to production lines with 99% accuracy. This eliminates physical training and tests, reducing setup times and costs. Workr is demonstrating AI-powered robotic systems at NVIDIA GTC 2026. Built on ABB technology, trained with synthetic data using NVIDIA Omniverse, deployed without operators needing programming knowledge . 🚨 I’ll be onsite in San Jose during GTC 2026, and will be showing all the cool stuff that ABB Robotics prepared this year! Can’t wait! 🫡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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🚨 BREAKING: ABB Robotics + NVIDIA close the sim-to-real gap with 99% accuracy! 👾 ABB Robotics is integrating NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy. RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly manufacturers can scale production: reducing costs by up to 40%, accelerating time-to-market by 50%, and cutting setup and commissioning times by up to 80%. For decades, the deficit between simulation accuracy and real-world lighting, materials, and environments has limited manufacturers' ability to design advanced manufacturing processes in the virtual world. The only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near-perfect correlation between simulation and real-world performance. The system uses physically accurate simulations and foundation models endlessly optimized with real-world data feedback. These models can train any number of ABB robots anywhere in the world with industrial-grade reliability. Foxconn is using RobotStudio HyperReality for consumer electronics assembly. Assembly robots are trained virtually using synthetic data to perfect multiple production processes across various scenarios, then moved to production lines with 99% accuracy. This eliminates physical training and tests, reducing setup times and costs. Workr is demonstrating AI-powered robotic systems at NVIDIA GTC 2026. Built on ABB technology, trained with synthetic data using NVIDIA Omniverse, deployed without operators needing programming knowledge . 🚨 I’ll be onsite in San Jose during GTC 2026, and will be showing all the cool stuff that ABB Robotics prepared this year! Can’t wait! 🫡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

22,482 просмотров • 5 месяцев назад

People who've never set foot in a factory will never understand... I watched this three times. For decades, robotics simulation has promised faster deployment. But factories still had to build the real cell to see if it actually worked. Which meant expensive physical prototypes, weeks or months!!! of commissioning, constant surprises between simulation and reality That “sim-to-real gap” has quietly been one of the biggest bottlenecks in manufacturing automation. And it’s exactly what is changing. Today, ABB Robotics announced a partnership with NVIDIA Robotics aimed at closing this gap through the new RobotStudio HyperReality platform: Simulation and real robot behavior can match with near-perfect accuracy. That means manufacturers can design, test, and validate entire production lines before a single robot is installed on the factory floor. The implications are massive: • up to 80% faster setup and commissioning • roughly 40% lower costs by removing physical prototypes • about 50% faster time-to-market for new production lines In other words: Factories can move from trial-and-error engineering to software-driven manufacturing design. Production lines become something you build and validate digitally first. Then deploy physically once everything already works. For an industry that still measures deployment timelines in months or years, this is a major shift. It changes how automation projects are planned, how factories are designed, and how fast manufacturing can adapt to new products. Physical AI actually becomes deployable at an industrial scale. I’ll be at GTC in San Jose next week to see and talk to manufacturers and robotics engineers. If you are into manufacturing like I am, hit me up; my DMs are open!
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People who've never set foot in a factory will never understand... I watched this three times. For decades, robotics simulation has promised faster deployment. But factories still had to build the real cell to see if it actually worked. Which meant expensive physical prototypes, weeks or months!!! of commissioning, constant surprises between simulation and reality That “sim-to-real gap” has quietly been one of the biggest bottlenecks in manufacturing automation. And it’s exactly what is changing. Today, ABB Robotics announced a partnership with NVIDIA Robotics aimed at closing this gap through the new RobotStudio HyperReality platform: Simulation and real robot behavior can match with near-perfect accuracy. That means manufacturers can design, test, and validate entire production lines before a single robot is installed on the factory floor. The implications are massive: • up to 80% faster setup and commissioning • roughly 40% lower costs by removing physical prototypes • about 50% faster time-to-market for new production lines In other words: Factories can move from trial-and-error engineering to software-driven manufacturing design. Production lines become something you build and validate digitally first. Then deploy physically once everything already works. For an industry that still measures deployment timelines in months or years, this is a major shift. It changes how automation projects are planned, how factories are designed, and how fast manufacturing can adapt to new products. Physical AI actually becomes deployable at an industrial scale. I’ll be at GTC in San Jose next week to see and talk to manufacturers and robotics engineers. If you are into manufacturing like I am, hit me up; my DMs are open!

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68,927 просмотров • 5 месяцев назад

Physical AI is here—the next wave of AI and the foundation of the next industrial revolution. NVIDIA founder and CEO Jensen Huang outlines the full stack making it possible: Cosmos and Omniverse to develop physical AI in virtual worlds, Isaac and Newton where robots learn skills in physics-simulated gyms, and Jetson robotics computers where their intelligence runs. Together with Japan’s robotics and manufacturing leaders, NVIDIA is creating a new era of industrial automation.
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Physical AI is here—the next wave of AI and the foundation of the next industrial revolution. NVIDIA founder and CEO Jensen Huang outlines the full stack making it possible: Cosmos and Omniverse to develop physical AI in virtual worlds, Isaac and Newton where robots learn skills in physics-simulated gyms, and Jetson robotics computers where their intelligence runs. Together with Japan’s robotics and manufacturing leaders, NVIDIA is creating a new era of industrial automation.

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NVIDIA Cosmos 3 launches with a full model family — Cosmos Super for highest-accuracy robotics and AV post-training, Cosmos Nano for high-speed video and action reasoning, and Cosmos Edge for real-time edge inference. Read the release ➡️
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NVIDIA Cosmos 3 launches with a full model family — Cosmos Super for highest-accuracy robotics and AV post-training, Cosmos Nano for high-speed video and action reasoning, and Cosmos Edge for real-time edge inference. Read the release ➡️

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🚀 🔥 AgiBot deploys Real-World Reinforcement Learning (RW-RL) in industrial robotics with Longcheer Technology. Robots now learn new skills in tens of MINUTES (not weeks), adapt to variations autonomously, and reconfigure flexibly, solving rigid automation pain points in precision manufacturing. A giant leap of intelligent automation for precision manufacturing! #AgiBot #ReinforcementLearning #RealWorldRL #Robotics #AI #IndustrialRobotics
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🚀 🔥 AgiBot deploys Real-World Reinforcement Learning (RW-RL) in industrial robotics with Longcheer Technology. Robots now learn new skills in tens of MINUTES (not weeks), adapt to variations autonomously, and reconfigure flexibly, solving rigid automation pain points in precision manufacturing. A giant leap of intelligent automation for precision manufacturing! #AgiBot #ReinforcementLearning #RealWorldRL #Robotics #AI #IndustrialRobotics

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Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

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Jensen just announced Newton, an open-source physics engine for robotics simulation developed by NVIDIA, Google DeepMind, and Disney Research. ⦿ Built on NVIDIA Warp, it offers GPU-accelerated performance and compatibility with frameworks like MuJoCo and Isaac Lab. ⦿ Features differentiable physics, extensibility for multiphysics simulations, and integration with OpenUSD for flexible data modeling. ⦿ Newton addresses the sim-to-real gap by creating realistic virtual environments for robot training and testing. ⦿ Disney Research is using Newton to power next-gen entertainment robots, including Star Wars-inspired BDX droids.
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Jensen just announced Newton, an open-source physics engine for robotics simulation developed by NVIDIA, Google DeepMind, and Disney Research. ⦿ Built on NVIDIA Warp, it offers GPU-accelerated performance and compatibility with frameworks like MuJoCo and Isaac Lab. ⦿ Features differentiable physics, extensibility for multiphysics simulations, and integration with OpenUSD for flexible data modeling. ⦿ Newton addresses the sim-to-real gap by creating realistic virtual environments for robot training and testing. ⦿ Disney Research is using Newton to power next-gen entertainment robots, including Star Wars-inspired BDX droids.

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China-based TARS Robotics demonstrated a humanoid robot performing two-handed hand embroidery during a public showcase on December 22, marking a notable step in fine motor control for humanoid systems. The robot threaded a needle and stitched a logo using both hands with sub-millimeter accuracy, working with soft, flexible materials that are difficult for traditional industrial robots to handle due to deformation and variability. According to the company, this capability is enabled by a closed-loop "Data-Al-Physics" system that connects real-world data collection, embodied Al models, and physical execution, reducing the gap between simulation and real deployment. The models are said to be open source when released. Founded in February 2025, TARS Robotics has moved quickly from research to live demonstrations and has raised over $240 million in early funding from the Chinese government, reflecting growing interest in general-purpose humanoid robots for precision and dexterous tasks.
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China-based TARS Robotics demonstrated a humanoid robot performing two-handed hand embroidery during a public showcase on December 22, marking a notable step in fine motor control for humanoid systems. The robot threaded a needle and stitched a logo using both hands with sub-millimeter accuracy, working with soft, flexible materials that are difficult for traditional industrial robots to handle due to deformation and variability. According to the company, this capability is enabled by a closed-loop "Data-Al-Physics" system that connects real-world data collection, embodied Al models, and physical execution, reducing the gap between simulation and real deployment. The models are said to be open source when released. Founded in February 2025, TARS Robotics has moved quickly from research to live demonstrations and has raised over $240 million in early funding from the Chinese government, reflecting growing interest in general-purpose humanoid robots for precision and dexterous tasks.

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In robotics circles, there's a consensus: we're on the brink of a productivity boom — driven by the American labor shortage and an unprecedented demand for automation. Yet, over 80% of warehouses lack automation, and there are only 141 robots per 10,000 manufacturing employees. Inspired by recent conversations with robotics leaders like Vikas Enti, Adrian Macneil — 🤖/acc, vijay badrinarayanan, and Kevin Peterson, I have more conviction than ever: The next wave of robotics startups is poised to boost output in unprecedented ways, ushering in a new golden age of economic growth and prosperity. This post details why developing Embodied AI is the opportunity of our generation:
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In robotics circles, there's a consensus: we're on the brink of a productivity boom — driven by the American labor shortage and an unprecedented demand for automation. Yet, over 80% of warehouses lack automation, and there are only 141 robots per 10,000 manufacturing employees. Inspired by recent conversations with robotics leaders like Vikas Enti, Adrian Macneil — 🤖/acc, vijay badrinarayanan, and Kevin Peterson, I have more conviction than ever: The next wave of robotics startups is poised to boost output in unprecedented ways, ushering in a new golden age of economic growth and prosperity. This post details why developing Embodied AI is the opportunity of our generation:

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📈 NVIDIA tops AI leaderboards and benchmarks with open models driven by extreme co-design across compute, networking, memory, storage, and software. This includes models for biology, AI physics, agentic AI, physical AI, robotics, and autonomous vehicles. By being vertically integrated and horizontally open, NVIDIA enables its ecosystem to scale agentic workflows at every layer of the stack with the lowest cost per token.

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The "sim-to-real" gap just got a massive 2,300m² bridge. 🤖🏗️ NEURA Robotics and TUM are launching the TUM RoboGym at Munich Airport—Europe’s largest training center for Physical AI. Starting mid-2026, a fleet of humanoid robots will begin real-world training to fuel the Neuraverse ecosystem with high-quality, sovereign data.
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The "sim-to-real" gap just got a massive 2,300m² bridge. 🤖🏗️ NEURA Robotics and TUM are launching the TUM RoboGym at Munich Airport—Europe’s largest training center for Physical AI. Starting mid-2026, a fleet of humanoid robots will begin real-world training to fuel the Neuraverse ecosystem with high-quality, sovereign data.

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Today, we announced a partnership with some of tech’s most recognizable names to develop new ways to optimize our grid for customers. We’re applying expertise from @IonQ, Oak Ridge Lab and @NVIDIA to create new solutions and prepare our community for the future.

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NVIDIA AI Open-Sources ViPE (Video Pose Engine): A Powerful and Versatile 3D Video Annotation Tool for Spatial AI ViPE integrates bundle adjustment with dense optical flow, sparse keypoint tracking, and metric depth priors to estimate camera intrinsics, poses, and dense depth maps at 3–5 FPS on a single GPU. It significantly improves over prior uncalibrated pose estimation methods, achieving 18% and 50% error reduction on TUM and KITTI benchmarks, respectively, and shows robustness to dynamic scenes and diverse camera models. Beyond the method, the NVIDIA team also released a large-scale dataset comprising ~100K real-world internet videos, 1M AI-generated videos, and 2K panoramic videos (≈96M frames) annotated with metric depth and poses. This dataset and engine aim to accelerate training for spatial AI tasks such as 3D reconstruction, video generation, and robotics.... full analysis: paper: codes: NVIDIA NVIDIA AI NVIDIAnewsroom NVIDIA Robotics NVIDIA AIDev NVIDIAdeveloper

Marktechpost AI Dev News ⚡

217,453 просмотров • 11 месяцев назад

On January 20, at Davos 2026, CATL was honored with the World Economic Forum (World Economic Forum )'s 2026 MINDS Award, recognizing its groundbreaking project "Augmented Intelligence Leading Next-Generation Lithium-ion Battery Design," which has been acclaimed as a global benchmark for AI-driven industrial application. The project marks a fundamental shift from traditional "reverse design and trial-and-error experimentation" to "forward design with predictive intelligence before manufacturing." By integrating proprietary multimodal data across materials, design, processes and equipment, the initiative has built an intelligent battery cell design platform for the lithium-ion battery industry. The system supports customized performance targets and dynamic priority adjustments, achieving a design prediction accuracy of up to 95%. Compared with manual design, it generates recommendations in seconds and virtual cells in minutes, increasing design efficiency by 30%. CATL will continue to evolve AI from "enablement" to "creation," and from solving known problems to discovering new frontiers, building a cornerstone for the global energy transition. #CATL #WEF #Davos2026 #ArtificialIntelligence #BatteryInnovation
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On January 20, at Davos 2026, CATL was honored with the World Economic Forum (World Economic Forum )'s 2026 MINDS Award, recognizing its groundbreaking project "Augmented Intelligence Leading Next-Generation Lithium-ion Battery Design," which has been acclaimed as a global benchmark for AI-driven industrial application. The project marks a fundamental shift from traditional "reverse design and trial-and-error experimentation" to "forward design with predictive intelligence before manufacturing." By integrating proprietary multimodal data across materials, design, processes and equipment, the initiative has built an intelligent battery cell design platform for the lithium-ion battery industry. The system supports customized performance targets and dynamic priority adjustments, achieving a design prediction accuracy of up to 95%. Compared with manual design, it generates recommendations in seconds and virtual cells in minutes, increasing design efficiency by 30%. CATL will continue to evolve AI from "enablement" to "creation," and from solving known problems to discovering new frontiers, building a cornerstone for the global energy transition. #CATL #WEF #Davos2026 #ArtificialIntelligence #BatteryInnovation

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40,931 просмотров • 6 месяцев назад

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🤖⚙️AGIBOT G2: The Next Generation of Industrial-Grade Interactive Embodied Operation Robots Sub-millimeter precision, human-like motion, 24/7 non-stop operation — AGIBOT G2 is the industrial-grade interactive embodied operation robot your factory needs! Dual arms with ≤0.5N force control for submillimeter accuracy, 26-DOF full-body movement, and NVIDIA-powered ultra computing. From high-precision assembly to logistics sorting, it’s redefining human-robot collaboration across every industry. The future of industrial automation is here. #AGIBOT #HumanoidNo1 #HumanoidShipmentsNo1 #AGIBOTG2 #IndustrialRobotics #Automation #SmartManufacturing

AGIBOT

21,290 просмотров • 5 месяцев назад

THE FASTEST ROBOT IN THE FACTORY? 🌍 Everyone is talking about AI, humanoids and robotics. 🤖 But what if I told you that one of the fastest and most reliable “automation systems” doesn’t have a motor… doesn’t need software… and doesn’t consume electricity? ⚡ It simply uses gravity. ⬇️ With clever mechanical engineering, products can be rotated from vertical to horizontal in milliseconds—at high speed and with exceptional reliability. ⚙️🚀 Sometimes the best automation isn’t adding more technology. It’s removing it. What’s more valuable in industrial automation today? A) Smarter mechanics B) Smarter software 👇 Comment A or B. #Automation #MechanicalEngineering #IndustrialAutomation #Engineering #Manufacturing
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THE FASTEST ROBOT IN THE FACTORY? 🌍 Everyone is talking about AI, humanoids and robotics. 🤖 But what if I told you that one of the fastest and most reliable “automation systems” doesn’t have a motor… doesn’t need software… and doesn’t consume electricity? ⚡ It simply uses gravity. ⬇️ With clever mechanical engineering, products can be rotated from vertical to horizontal in milliseconds—at high speed and with exceptional reliability. ⚙️🚀 Sometimes the best automation isn’t adding more technology. It’s removing it. What’s more valuable in industrial automation today? A) Smarter mechanics B) Smarter software 👇 Comment A or B. #Automation #MechanicalEngineering #IndustrialAutomation #Engineering #Manufacturing

Advanced humanoid forum

2,986,637 просмотров • 9 дней назад

We’ve developed a protocol that allows AI agents to autonomously discover, integrate with, and pay for services in real time. Today is a big step forward. This marks the beginning of a machine-friendly economy, where agents can interact and transact independently.
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We’ve developed a protocol that allows AI agents to autonomously discover, integrate with, and pay for services in real time. Today is a big step forward. This marks the beginning of a machine-friendly economy, where agents can interact and transact independently.

positiveblue ⚡️🍠

20,532 просмотров • 1 год назад