Abstract

Sim-to-real transfer in robotics remains a significant challenge due to the inherent differences between simulated environments and real-world conditions, often leading to performance degradation when models are deployed in practical applications. This paper reviews the current state of sim-to-real transfer, exploring the key challenges such as sensor noise, domain shifts, and modeling inaccuracies contributing to this performance gap. The paper also examines existing techniques, including domain adaptation, reinforcement learning, and hybrid approaches, and discusses their limitations. To address these issues, we propose a novel framework that emphasizes the development of more realistic simulation environments and the integrating of adaptive learning strategies for continuous model refinement during real-world deployment. This framework aims to improve the robustness and adaptability of robotic systems, facilitating more reliable performance in diverse real-world scenarios. The paper concludes by outlining the implications for future research, highlighting open challenges, and suggesting directions for further validation and refinement of the proposed framework.