ABB Robotics has partnered with NVIDIA to accelerate the deployment of physical AI robotics across industrial environments. The collaboration integrates NVIDIA Omniverse libraries into ABB’s RobotStudio platform, creating a new simulation-driven pathway for training and deploying AI-powered robots at scale.
The announcement signals a strategic shift in industrial automation: simulation environments now act as primary training grounds for intelligent robots before they enter production floors.
ABB says the integration enables manufacturers to simulate robotic systems inside digital twins while generating synthetic data to train physical AI models. As a result, developers can design, test, and refine industrial workflows virtually before deploying robots into real-world environments.
“Today, using NVIDIA accelerated computing and simulation technologies, we have removed the last barriers to making industrial and physical AI a reality at a global scale by closing the sim-to-real gap,” said Marc Segura, President of ABB Robotics. “For more than 50 years, ABB Robotics has led the evolution of intelligent industrial automation, from pioneering the first generation of fully electric industrial robots to advancing digital twin simulation through RobotStudio® and shaping a new area of autonomous and versatile mobile robots. Today’s announcement with NVIDIA brings physical AI to industry at scale.”
Closing the Long-Standing ‘Sim-to-Real’ Gap
The partnership directly targets the robotics industry’s most persistent challenge: the sim-to-real gap. This gap describes the mismatch between simulated environments and real-world manufacturing conditions such as lighting, materials, and spatial variability.
For decades, this mismatch slowed the adoption of advanced AI-driven automation because simulation results rarely translated perfectly into factory operations.
However, the integration of NVIDIA Omniverse libraries into RobotStudio introduces physically accurate simulation capable of generating synthetic datasets for robot training. Developers can therefore simulate production environments with far greater fidelity and train AI models before deployment.
“The industrial sector needs physically accurate simulation to bridge the gap between virtual training and the real-world deployment of AI-driven robotics at scale,” said Deepu Talla, Vice President of robotics and edge AI at NVIDIA. “Integrating NVIDIA Omniverse libraries into RobotStudio brings advanced simulation and accelerated computing to ABB Robotics’ unique virtual controller technology, accelerating how manufacturers of all sizes bring complex products to market.”
ABB notes that its virtual controller runs the same firmware as physical robots, allowing simulations to mirror real-world behavior closely. Combined with the company’s Absolute Accuracy technology, which reduces positioning errors from several millimeters to roughly half a millimeter the system aligns digital modeling with factory performance.
RobotStudio HyperReality and the Rise of Synthetic Training
ABB calls the combined platform RobotStudio HyperReality, a system designed to create physically accurate simulations that improve continuously through real-world feedback. The models trained inside these environments can scale across ABB’s global robotics fleet. Moreover, manufacturers can train robots for multiple tasks simultaneously, enabling automation strategies that extend across entire production networks.
The technology could also reduce industrial deployment timelines. Virtual production design allows companies to optimize manufacturing lines digitally before installing hardware on the factory floor.
Production setups may therefore move from months of commissioning to much shorter cycles. Manufacturers can eliminate the need for physical prototypes during testing phases and move complex products to market faster.
Foxconn Pilots Physical AI in Consumer Electronics Assembly
Foxconn is piloting the first industrial use case under the partnership. The electronics manufacturing giant is using RobotStudio HyperReality to automate delicate assembly tasks in consumer electronics production.
Training robots to handle small electronic components often requires extensive calibration and debugging because device variants demand different manufacturing processes. By training robots virtually using synthetic data, Foxconn can test multiple assembly scenarios before deploying them to physical production lines. Consequently, robots arrive on the factory floor already optimized for precision workflows.
“Precision is everything in consumer electronics manufacturing and until now, this level of accuracy and fidelity just wasn’t possible in simulation and digital twins,” said Dr. Zhe Shi, Chief Digital Officer of Foxconn. “We’re incredibly excited by the potential of ABB Robotics and NVIDIA’s collaboration, which enables parallel engineering for better designs, faster production ramp-up and greater product evolution through advanced AI inference and understanding.”
Expanding Robotics Access to Smaller Manufacturers
Meanwhile, WORKR plans to extend the technology’s reach beyond large enterprises. The California-based company delivers robotic workforce solutions and will showcase systems built on ABB robots trained through NVIDIA Omniverse simulation. The demonstration will take place at NVIDIA GTC 2026 in San Jose, where WORKR will present robotics platforms trained entirely through synthetic data.
Its systems combine ABB hardware with the company’s proprietary WorkrCore AI platform. These robots can learn tasks rapidly and operate without traditional programming expertise.
“This collaboration is about making industrial AI deployable today,” said Ken Macken, CEO & Founder of WORKR. “Together with ABB and NVIDIA, we’re proving that advanced automation can work for manufacturers of any size.”
Strategic Implications for AI-Driven Manufacturing
The partnership reflects a broader shift in industrial AI: simulation environments now function as training infrastructure for robotics. Consequently, manufacturers can prototype production systems digitally while preparing robots for real-world conditions. ABB is also exploring integration of the NVIDIA Jetson edge computing platform into its OmniCore controller to enable real-time AI inference directly on robots.
The development builds on previous collaboration between the companies, including the use of Jetson processors in autonomous mobile robots and joint work supporting large-scale AI data center development.
RobotStudio HyperReality will roll out to ABB’s global RobotStudio user base in the second half of 2026. Select industrial customers have already begun early testing of the platform. As physical AI moves from experimental research into factory deployment, partnerships like this may reshape how industrial automation evolves over the next decade.
