A Survey on Sim-to-Real Transfer Methods for Robotic Manipulation

The effective deployment of robotic systems in real-world environments requires the development of reliable control policies, which can be challenging due to safety, cost, and time constraints associated with direct real-world training. Sim-to-real transfer provides a solution by allowing robots to learn policies in simulated environments that can be seamlessly applied to real-world scenarios. This comprehensive review examines key methodologies for sim-to-real transfer in robotic manipulation, including domain randomization, domain adaptation, meta-learning, knowledge distillation, policy performance correlation, “green-screening” backgrounds, alternative 3d representations, and video generation. These approaches are critical for bridging the gap between simulated and actual environments, thereby facilitating the safe and efficient deployment of robotic systems. The review categorizes recent significant studies, explores major application areas and identifies primary challenges and future research directions. Our objective is to provide a thorough overview of current progress and to highlight promising avenues for future research in sim-to-real transfer, ultimately enhancing the robustness, efficiency, and adaptability of robotic systems in diverse real-world applications.