Training an AI system in simulation is cheap and fast. Deploying it on real hardware is where most of the actual difficulty in robotics lives, a gap with its own name: the reality gap.
Real-world robot training is slow, a physical robot runs one trial at a time, and failures can damage expensive hardware. Simulation removes both constraints, thousands of parallel attempts, failures that cost nothing, months of training compressed into days. That’s why platforms like NVIDIA’s Cosmos and Isaac GR00T lean so heavily on it.
Where simulated physics quietly diverges from real physics
Contact and friction are hard to model precisely, and small errors compound across a sequence of interactions. Real sensors have noise, motion blur, and calibration drift that simulated ones render too cleanly. Real actuators have backlash and wear an idealized one doesn’t reproduce. Materials bend and break in ways simulation struggles to capture exactly.
How the gap actually gets closed
Rather than chasing perfect fidelity, domain randomization deliberately varies simulated conditions, friction, lighting, object weight, forcing the resulting policy to generalize instead of overfit. Combined with real-world fine-tuning after simulation, this has become the practical standard: accept that simulation will never be perfect, and build robustness to that imperfection directly into training.
This is a real part of why deployment timelines stretch years past an impressive simulated demo. An impressive simulation is a meaningfully weaker signal of real-world readiness than it looks.
See NVIDIA’s own Cosmos platform page for more on simulation training.
