Existing simulation frameworks struggle to comprehensively address the four autonomy-oriented requirements: (i) dynamical fidelity, (ii) photorealistic rendering, (iii) context-relevant scenario orchestration, and (iv) real-time performance. Maximizing one constraint traditionally breaks one or more of others, forcing unwanted compromises on robotics engineers. We must harness the true potential of digital twins by fusing data, physics, and generative AI smartly. Hence, we present a unified framework for creating and curating physics-based, data-driven, and AI-enabled digital twins for autonomous driving.

Our innovation is an AI-powered digital twin platform that creates highly realistic virtual replicas of robots and their operating environments. The platform combines physics-based simulation with data-driven reconstruction of real-world scenes and assets with ~97% structural similarity, while preserving the physical behavior needed for real-time robotic testing. The resulting digital twins operate in real-time (~60 Hz), allowing robots to be trained, validated, and optimized in realistic environments before physical deployment.

A unique feature of the platform is its natural-language interface powered by a Large Language Model (LLM). Engineers can create, modify, and test complex robotic scenarios simply by describing them in everyday language, eliminating the need for manual programming. An optional Vision Language Model (VLM) further enhances scene realism by intelligently blending reconstructed and simulated environments, improving visual fidelity by ~ 80%, while maintaining consistent simulation performance.

The key innovation is the seamless integration of high-fidelity digital twins, real-time physics simulation, generative AI, and natural-language interaction within a single modular platform. Existing simulation tools typically require extensive manual scene creation, specialized expertise, or simplified environments that fail to represent real-world conditions. Our platform dramatically reduces scenario development time while producing simulations that are more realistic, repeatable, and adaptable to changing operational requirements.

The proposed technology can be applied across various verticals of the robotics industry: autonomous vehicles, warehouse automation, industrial robotics, drones, agricultural machinery, mining equipment, construction robots, defense systems, smart manufacturing, and intelligent infrastructure. Any robotic system requiring realistic testing, operator training, or virtual commissioning can benefit from the platform.

The proposed framework integrates seamlessly with existing robotic simulation software, digital engineering workflows, and commercial computing hardware without requiring specialized manufacturing or custom equipment. Organizations can deploy the platform using existing infrastructure, significantly reducing development costs compared to extensive field testing or building dedicated physical testing facilities.

By enabling safer, faster, and more comprehensive validation of autonomous systems, this platform improves public safety, accelerates innovation, reduces development costs, and shortens time-to-market for next-generation robotic technologies. As digital twins become a foundational technology for Industry 4.0 and autonomous systems, this scalable platform offers substantial commercial potential for robotics manufacturers, automotive companies, industrial automation providers, simulation software developers, and government agencies seeking reliable and cost-effective autonomous system validation.