Sim-to-Real Transfer

Train robot policies in simulation and transfer them to real hardware, leveraging simulation data abundance while overcoming the domain gap between simulated and real environments. Sim-to-real transfer trains large quantities of experience in simulation (where data is cheap and fast) and then adapts the policy for real-world deployment. The key technique is domain randomization: systematically randomizing simulation parameters (mass, friction, lighting, textures) so the policy learns to generalize across the sim-to-real gap rather than overfitting to specific simulation values. More advanced methods use system identification (matching sim parameters to real observations) or domain adaptation (learning invariant features).