Training a robot in simulation offers millions of cheap practice runs and safe testing, but simulation is always a simplification. Real surfaces have irregular friction, objects deform unpredictably, and cameras capture noise that renderers only approximate. When a policy trained purely in simulation meets the real world, performance often drops sharply — researchers call this the sim-to-real gap.

The main fix is domain randomization: instead of one simulated world, you randomize lighting, textures, masses, and physics parameters across thousands of variations, forcing the model to learn robust rules rather than memorize a single environment. Even so, most production teams mix simulated data with real demonstrations — simulation scales breadth cheaply, while real data anchors the model to genuine physical variability. The gap is narrowing, but it remains the central obstacle between a robot that works in a lab and one that works in your kitchen.