Domain Randomization Helps Robots Transfer Skills from Simulation to Reality

Domain randomization is a sim-to-real technique in robot learning that trains policies across widely varied simulation parameters, making the real world appear as just another variation the model has already encountered. Rather than precisely replicating real-world conditions in simulation, developers randomize factors such as lighting, object textures, friction, mass, and sensor noise during training. The approach addresses four key gaps between simulation and reality: visual appearance, physics and dynamics, sensor behavior, and camera placement. Randomization is applied at each episode reset so every training run draws from a fresh combination of parameters. The technique is designed to integrate with existing simulators like MuJoCo, making it a practical addition to standard robot learning pipelines.