Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving
Autonomous driving policies should be able to improve continually as deployment exposes them to increasingly diverse and long-tail traffic situations. However, most learning-based policies are trained or fine-tuned on expert demonstrations and then rely largely on generalization to handle challenging closed-loop scenarios, lacking an explicit mechanism to correct and retain the mistakes exposed in these scenarios. This paper studies autonomous driving policy improvement from a lifelong learning perspective: Can a pretrained policy improve continually by accumulating corrective knowledge derived from its own mistakes, while retaining previously acquired driving competence? To answer this q...
| Authors | Cheng Gong, Haoyang Wang, Chao Lu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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