BOSTON, MA, May 27, 2026 (GLOBE NEWSWIRE) — Digital Twin Consortium® (DTC™) today announced that Xaba has joined the consortium. Xaba is advancing a new category of industrial automation by embedding Physics-Based AI directly into machines, enabling robots to understand and adapt to real-world conditions in real time.
As manufacturers face increasing pressure to scale operations, reduce costs, and adapt to variability in production environments, traditional automation systems often fall short. Xaba addresses this challenge through its Physics-AI based “xCognition” platform, which models physical forces, motion, pressure, temperature, acceleration, voltage, amperage and environmental constraints to allow machines to autonomously generate and adjust their own instructions. This approach enables robots to perform complex tasks such as welding, drilling, grinding, polishing, server and data-center assembly with greater precision, flexibility, and efficiency.
Xaba’s decision to join Digital Twin Consortium reflects its commitment to advancing the next generation of intelligent systems that bridge the gap between digital and physical environments.
“Industrial automation has long been constrained by rigid programming that cannot adapt to real-world variability,” said Massimiliano Moruzzi, CEO of Xaba. “By embedding intelligence directly into machines, we are enabling systems that can reason about their tasks, process parameters, environment and continuously improve performance. We look forward to collaborating with DTC members to help define how Physics-AI ushers in the era of the Digital Twin Synthetic Brain, where digital twins and Physics-AI converge to enable cognitive industrial automation.”
“Whether it’s a multi-axis industrial arm or a humanoid system, autonomous robots need a controller that can process multi-physics sensor data and generate control decisions in real time,” said Dr. Giulio Corradi, AMD Fellow, Principal Architect Industrial Robotics Healthcare, Adaptive and Embedded Computing Group, AMD. “xCognition, on Ryzen™ AI Embedded processors, does exactly that, running physics-grounded AI at the edge without depending on cloud connectivity for critical operations, while providing the same architecture for cloud-scale model training when it’s time to deploy across hundreds of machines.”
“Xaba represents a genuinely exciting marriage of innovation and deep expertise in physics-grounded AI that gives machines the ability to reason about forces, motion, and environmental constraints in real time,“ said Dan Isaacs, CTO and GM of the Digital Twin Consortium. “Bringing that capability directly into the control loop is precisely where digital twins become intelligent, self-directing systems. We’re thrilled to welcome them into the consortium and inspired to see them already deep in collaboration with fellow members.”
By contributing its expertise in Physics-Based AI, Xaba aims to support the development of interoperable frameworks and accelerate the adoption of digital twins across manufacturing and industrial applications.
About Xaba
Xaba is pioneering the application of industrial AI to modernize manufacturing processes. Its flagship product, xCognition, empowers industrial robots and collaborative robots (cobots) with AI-driven cognition and awareness, enabling them to autonomously generate programs and execute complex tasks such as welding, drilling, assembling, and additive manufacturing. By integrating real-time intelligence into automation, Xaba’s solutions significantly reduce deployment costs and enhance the quality, consistency, and flexibility of manufacturing operations. For more information, visit xaba.ai.
About the Digital Twin Consortium
The Digital Twin Consortium® (DTC) is Accelerating Digital Twin Innovation™. DTC advances digital twin adoption and market growth by working closely with members to foster development, raise awareness, and enhance interoperability of digital twin applications throughout the Digital Twin Lifecycle across industries. DTC is an EDM Association community. For more information, visit https://www.digitaltwinconsortium.org
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