A brand new ABB and NVIDIA partnership exhibits bodily AI simulation is driving actual ROI in manufacturing facility automation and fixing manufacturing hurdles.
Producers have usually discovered it tough to make clever robotics work reliably outdoors testing environments. The core situation is the hole between digital coaching fashions and precise manufacturing facility flooring, the place lighting, materials physics, and half variations refuse to behave as they do on a display.
Traditionally, this friction has beforehand pressured engineering groups to fall again on bodily prototypes, delaying product launches and driving up prices.
Overcoming the digital to bodily AI simulation divide
The partnership between ABB Robotics and NVIDIA makes an attempt to shut this hole by bringing industrial-grade bodily AI to manufacturing services. Slated for launch within the second half of 2026, RobotStudio HyperReality is already drawing curiosity from a worldwide buyer base.
By embedding NVIDIA Omniverse libraries inside its current RobotStudio software program, ABB offers a platform for bodily correct digital testing. On an operational degree, this integration permits engineers to chop deployment prices by as much as 40 p.c and speed up time to market by as a lot as 50 p.c.
Realising these effectivity beneficial properties calls for a workflow the place manufacturing leaders design, check, and validate full automation cells earlier than putting in any {hardware}. To do that, the system exports a completely parameterised station – encompassing the robots, sensors, lighting, kinematics, and components – as a USD file straight into the Omniverse atmosphere.
Inside this digital area, a digital controller runs the an identical firmware discovered on the bodily machine, enabling a 99 p.c behavioural match between the digital and bodily realms.
Somewhat than manually programming actions, laptop imaginative and prescient fashions be taught utilizing artificial photographs generated contained in the software program. When mixed with Absolute Accuracy expertise, this methodology cuts positioning errors down from 8-15 mm to roughly 0.5 mm, offering excessive precision for industrial functions.
Marc Segura, President of ABB Robotics, mentioned: “Combining RobotStudio with the physically accurate simulation power of NVIDIA Omniverse libraries, we have closed technology’s long-standing ‘sim-to-real’ gap—a huge milestone to deploying physical AI with industrial-grade precision, for real-world customer applications.”
Validating manufacturing facility automation earlier than deployment
Early adopters are already validating these capabilities on lively manufacturing traces.
Foxconn, for instance, is testing the software program for client machine meeting—an space the place frequent product modifications and delicate steel elements complicate conventional automation. By producing artificial knowledge to coach their programs just about, Foxconn achieves excessive accuracy on the manufacturing facility ground whereas anticipating a discount in setup time and the elimination of expensive bodily testing.
Equally, Workr – a California-based automation supplier – integrates its WorkrCore platform with ABB {hardware} skilled through Omniverse. On the NVIDIA GTC 2026 occasion in San Jose, Workr intends to showcase programs able to onboarding new components in minutes with out requiring specialised programming abilities.
Deepu Talla, VP of Robotics and Edge AI at NVIDIA, commented: “The industrial sector needs high-fidelity simulation to bridge the gap between virtual training and real-world deployment of AI-driven robotics at scale.
“Integrating NVIDIA Omniverse libraries into RobotStudio brings advanced simulation and accelerated computing to ABB’s virtual controller technology, accelerating how thousands of manufacturers bring complex products to market.”
The {hardware} ecosystem can be increasing to edge computing. ABB is evaluating the mixing of NVIDIA’s Jetson edge platform into its Omnicore controllers, a step that may facilitate real-time inference throughout current robotic fleets.
Adopting this kind of digital-first simulation for bodily AI can scale back setup and commissioning instances by as much as 80 p.c. As AI strikes from software program functions to {hardware} operations, making ready knowledge pipelines and upskilling engineering groups to work with artificial knowledge will dictate which producers keep a aggressive edge.
See additionally: Agentic AI in finance hastens operational automation
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