Sim-to-real transfer is the practice of training a robot’s control policy inside a physics simulator and then deploying it on a real robot. Simulation is attractive because it is fast, cheap, safe, and infinitely repeatable - a policy can experience years of practice in hours, with no broken hardware. The obstacle is the reality gap: simulators get physics, contact forces, sensor noise, and visual appearance slightly wrong, so a policy that is perfect in simulation often fails the moment it meets the real world.
The most influential answer is domain randomization, introduced by OpenAI and Berkeley researchers in 2017. Instead of trying to make the simulator perfectly realistic, you randomize it aggressively - textures, lighting, colors, masses, friction, and timing - across a wide range during training. A policy forced to succeed under all that variation treats the real world as just another variation it has already handled, so it transfers without ever seeing real data. Other approaches include system identification to tune the simulator to the real robot, and fine-tuning the transferred policy on a small amount of real experience.
Why business readers should care: sim-to-real is what makes it economically feasible to develop robots without thousands of hours of real-world trials. It underpinned OpenAI’s Rubik’s Cube robot hand and is standard in warehouse, autonomous-vehicle, and humanoid development - but the residual reality gap is also why simulation-validated systems still need careful real-world testing before deployment.