Sim-to-Real Transfer

The field studies how to make models and control policies trained in simulation work reliably when they encounter real sensors, physics, objects, and environments. Common approaches include domain randomization, system identification and simulator calibration, real-to-sim-to-real data loops, and shared representations that align simulated and real visual, tactile, or proprioceptive inputs. Researchers apply these methods to tasks such as robotic manipulation, locomotion, navigation, and vehicle control, often testing zero-shot deployment alongside adaptation with limited real-world data. A central tension is that more realistic simulation can improve transfer but is costly to build, while broad randomization may not capture important mismatches; high simulation scores therefore do not always predict real-world performance.