Sim-to-Real and Real-to-Sim: The Core Engines Enabling Capable Physical AI

Advancing Physical AI that perceives and acts in the real world requires overcoming the Sim-to-Real gap (Reality Gap) between simulation and actual environments. This is because collecting tens of thousands of training data samples using real hardware alone is nearly impossible in terms of cost and risk.

Two strategies are used to narrow this gap.

• Improving Simulation Fidelity: Building realistic environments by precisely modeling physical laws and visual elements, as with NVIDIA Isaac Sim.
• Domain Randomization: Randomly varying lighting, textures, friction, and other factors so that the model operates stably across diverse environments.

Real-to-Sim is a technology that converts real-world environments into digital twins to improve simulation accuracy. By reconstructing real scenes into 3D assets through NeRF or 3D Gaussian Splatting, or by mirroring Teleoperation data into simulation, large volumes of high-quality Imitation Learning data can be generated.

As a result, the latest Physical AI pipelines are evolving toward combining the distribution-aligned supervision provided by real-world data with the scalability provided by simulation, building advanced AI models without physical constraints.