Sim-to-Real Transfer

Sim-to-real transfer is the process of training AI models—particularly reinforcement learning agents and robotic controllers—in simulated environments and then deploying them in the physical world. Simulation offers significant advantages: it is faster, cheaper, safer, and allows for unlimited data generation without physical wear or risk. However, the gap between simulation physics and real-world conditions, known as the sim-to-real gap, can cause policies that work perfectly in simulation to fail on actual hardware. Techniques for bridging this gap include domain randomization (varying simulation parameters to cover real-world variability), domain adaptation, and progressive fine-tuning on real data. Sim-to-real transfer is widely used in robotics, autonomous driving, and drone navigation. The approach connects to reward shaping for designing effective training signals and inverse reinforcement learning for capturing realistic behavior patterns from simulated demonstrations.