ABB Robotics and NVIDIA have announced a partnership that aims to close the gap between digital training models and real‑world factory environments. The collaboration will introduce RobotStudio HyperReality, a simulation platform that integrates NVIDIA’s Omniverse libraries into ABB’s existing RobotStudio software. The product is scheduled for release in the second half of 2026 and is already attracting interest from manufacturers worldwide.
Background
Manufacturers have long struggled to transfer intelligent robotics from controlled test settings to the variable conditions of production lines. Differences in lighting, material properties, and part tolerances often cause robots to behave unpredictably when deployed. Historically, engineering teams have relied on physical prototypes to validate designs, a process that delays product launches and increases costs.
RobotStudio HyperReality addresses these challenges by providing a physically accurate digital twin of an entire automation cell. The system exports a fully parameterised station—including robots, sensors, lighting, kinematics, and parts—as a USD file that can be loaded directly into the Omniverse environment. Within this virtual space, a controller runs the same firmware that operates on the physical machine, achieving a 99 percent behavioural match between simulation and reality.
Instead of manually programming robot movements, the platform uses computer‑vision models that learn from synthetic images generated inside the software. When combined with NVIDIA’s Absolute Accuracy technology, the approach reduces positioning errors from 8–15 mm to approximately 0.5 mm, a level of precision suitable for many industrial applications.
Reactions
Marc Segura, President of ABB Robotics, stated that the integration of Omniverse libraries has closed the long‑standing “sim‑to‑real” gap, enabling the deployment of physical AI with industrial‑grade precision. Deepu Talla, Vice President of Robotics and Edge AI at NVIDIA, emphasized the need for high‑fidelity simulation to bridge the divide between virtual training and real‑world deployment of AI‑driven robotics at scale. He added that the collaboration accelerates how manufacturers bring complex products to market.
Early adopters are already testing the platform on active production lines. Foxconn is using the software for consumer device assembly, where frequent product changes and delicate metal components pose significant automation challenges. By generating synthetic data for virtual training, Foxconn reports higher accuracy on the factory floor and anticipates reduced setup times and lower costs associated with physical testing.
Workr, a California‑based automation provider, has integrated its WorkrCore platform with ABB hardware trained via Omniverse. The company plans to demonstrate at the NVIDIA GTC 2026 event in San Jose that its systems can onboard new parts in minutes without requiring specialised programming skills.
Implications
ABB is evaluating the integration of NVIDIA’s Jetson edge platform into its Omnicore controllers, a move that would enable real‑time inference across existing robotic fleets. The adoption of digital‑first simulation for physical AI could reduce setup and commissioning times by up to 80 percent. As AI moves from software applications to hardware operations, manufacturers that prepare data pipelines and upskill engineering teams to work with synthetic data are likely to maintain a competitive advantage.
According to the partnership’s specifications, the integration of Omniverse libraries into RobotStudio can cut deployment costs by up to 40 percent and accelerate time to market by as much as 50 percent. These efficiencies are expected to translate into measurable return on investment for factories that adopt the technology.
Future Outlook
RobotStudio HyperReality is slated for commercial release in the second half of 2026. ABB and NVIDIA plan to continue expanding the hardware ecosystem, including potential edge‑computing solutions that support real‑time inference. Manufacturers interested in reducing the gap between simulation and production are likely to monitor the rollout closely, as the platform promises significant cost savings and faster deployment of automated systems.
