Zero-shot sim-to-real transfer for autonomous driving is achievable by training on a consistent semantic representation in simulation and applying the same representation to real sensor data, eliminating the need for manual policy adaptation.
MILER is a reinforcement learning framework for autonomous driving that bridges simulation and real-world deployment without manual tuning. It trains policies in simulation using a semantic bird's-eye-view representation, then transfers them to real vehicles by converting camera and LiDAR data into the same representation format.