A Survey of Sim-to-Real Transfer Methods in Robot Learning.

Robots trained entirely in simulation often fail when deployed on real hardware — a problem known as the "reality gap." In this survey, I review the major approaches researchers have developed to close that gap, from simple randomization of simulator parameters to learned domain-adaptation networks and closed-loop system identification. I organize the literature into a clear taxonomy, compare methods on practical grounds (how much real-world data they need, what kind of gap they address, and when adaptation happens), and highlight open problems the field hasn't solved yet. I argue the most promising direction is treating the reality gap as something to be continuously monitored and corrected — the idea behind adaptive digital twins — rather than a fixed problem solved once before deployment. This ties directly into my own research interest in adaptive digital twin-based reinforcement learning for autonomous robots.