Problem
Research questions and friction points this paper is trying to address.
end-to-end driving
closed-loop deployment
safety-critical incidents
post-training
sensor-based policies
OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher
🤖 AI Summary
为解决自动驾驶中闭环部署时的误差累积问题，提出OPTED方法，利用无渲染教师进行策略微调，提高驾驶性能并减少模拟成本。
📝 Abstract
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 deployment can take the vehicle outside the training data distribution, increasing the risk of safety-critical incidents. Closed-loop post-training can mitigate this risk but requires costly simulation for sensor-based policies. We propose OPTED (on-policy fine-tuning for end-to-end driving) which decouples reinforcement learning from the post-training of the end-to-end policy: a privileged teacher is trained using RL on vectorized inputs (HD-map and bounding boxes). This teacher then provides supervision to the pre-trained student during closed-loop post-training. We apply OPTED to two camera-based models, TransFuser and VaVAM, and fine-tune them in AlpaSim, using neural reconstructions (3DGS) of real driving logs. Driving scores increase by factors of 1.6$\times$ and 9.5$\times$, respectively. In controlled experiments OPTED matches closed-loop performance with approximately three orders of magnitude fewer simulator interactions than direct RL post-training, while staying closer to the human prior. Project page: https://01dami23.github.io/opted/
Innovation
Methods, ideas, or system contributions that make the work stand out.
on-policy fine-tuning
end-to-end driving
privileged teacher
reinforcement learning
closed-loop post-training
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