OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher
As scaling pre-training data alone yields diminishing returns, post-training is becoming increasingly important across physical AI domains such as autonomous driving. End-to-end driving policies are pre-trained in open loop with behavior cloning on human demonstrations. However, compounding errors during closed-loop de...
D. Da Col, M. Igl, Peter Karkus et al.
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