Firms invest substantial resources in training robotic AI models in simulation environments to save on costs and minimize risk, yet these efforts are undermined when models perform unreliably outside of simulation.

This sim-to-real gap generates a significant dilemma: should organizations spend even more on risky real-world trials, or accept handicapped performance and protracted development cycles? The lack of reliable transferability strains budgets, frustrates engineers, and leads to unpredictable outcomes on critical robotic applications.

The foundational challenge lies in modeling the full complexity of real-world physics, sensor noise, dynamic environments, and edge cases within current simulation platforms—something that is both technologically and economically constrained.

Furthermore, accurate domain adaptation techniques that reliably align simulation outputs with real-world results are lacking, leaving robotics teams without a standardized path to successful deployment.

Basic domain randomization, physics engines, and limited real-world fine-tuning are used, but they only partially reduce the gap.

These require significant manual adjustment, are labor-intensive, and rarely generalize across different robots or tasks, resulting in unpredictable transfer success.