AI Research
Imitation Learning for Autonomous Driving in CARLA
Closing the gap between offline training and real-world autonomous driving performance.
Behavioral cloning trains a policy offline on expert demonstrations, but deployment is closed loop: each action affects the observations the policy receives next. We study how much closed-loop driving competence a compact multimodal policy can acquire from offline demonstrations ...
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ArXiv CS.AI
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ArXiv CS.AI
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Bridging the gap between static offline demonstrations and dynamic closed-loop execution is essential for developing reliable autonomous navigation systems. This research highlights how compact models can be optimized to handle the compounding errors inherent in real-world driving environments.
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