One of the primary limitations of FLaRe's reliance on simulation for fine-tuning is the sim-to-real gap, which refers to the discrepancies between simulated environments and real-world conditions. While FLaRe employs techniques like domain randomization and feature extraction to mitigate this gap, there are still challenges when dealing with tasks that involve complex dynamics, such as those involving liquids or soft objects, where accurate simulation is difficult to achieve.
To adapt FLaRe for tasks where robust simulations are unavailable, a hybrid approach could be employed. This would involve combining simulated training with real-world data collection. For instance, FLaRe could initially be fine-tuned in simulation, followed by a phase of real-world fine-tuning using a small number of real-world interactions. This would allow the model to adjust its learned policies based on actual environmental feedback, enhancing its adaptability to real-world conditions.
Moreover, incorporating online learning techniques could enable FLaRe to continuously improve its performance as it encounters new scenarios in the real world. By allowing the model to learn from its experiences in real-time, it can adapt to unforeseen challenges and refine its policies accordingly. This approach would not only enhance the robustness of FLaRe in real-world applications but also reduce the dependency on high-fidelity simulations.