While relying heavily on simulation data for training policies offers several advantages such as affordability and scalability of data collection, there are potential drawbacks and limitations to consider. One limitation is the fidelity of the simulation environment compared to real-world scenarios. If the simulator does not accurately capture all aspects of the real world (e.g., physics interactions, lighting conditions), there may be challenges in transferring policies from simulation to reality effectively. Additionally, over-reliance on simulated data may lead to a lack of diversity or bias in the training dataset if not carefully managed through appropriate augmentation techniques. Furthermore, unexpected discrepancies between simulation and reality could result in suboptimal performance when deploying trained policies on physical robots.