Sim-to-Sim-to-Real: Utilizing high and low-fidelity simulators for predicting Sim-to-Real Transfer and Analysis using small-scale autonomous vehicles
J.R. Buitenweg (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Cynthia CS Liem – Graduation committee member (TU Delft - Multimedia Computing)
A.J. Bartlett – Mentor (TU Delft - Multimedia Computing)
Annibale Panichella – Graduation committee member (TU Delft - Software Engineering)
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Abstract
The Sim2Real gap poses significant challenges for testing autonomous vehicles, often becoming apparent only during high-risk real-world deployments. This research proposes a novel pipeline that leverages both high-fidelity (CARLA) and low-fidelity (Gym-Duckietown) simulators to estimate this gap prior to deployment. The results reveal a strong corelation between performance in Gym Duckietown and real-world outcomes, suggesting it can serve as potential estimation for real world performance and the Sim2Real gap. Nonetheless, real-world testing remains an essential part of the validation process. Future work should build on these findings to further explore and validate the approach.