Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving

2026-06-29

AI Summary

This work addresses the challenge that autonomous driving policies struggle to continuously learn from their own mistakes in long-tail traffic scenarios while preserving previously acquired capabilities. To this end, the paper introduces the R²LPL framework, which uniquely integrates lifelong learning with an error-driven correction mechanism. By leveraging rollout-based error detection, recoverable state filtering, and corrective target retrieval, the method transforms sparse, unsupervised signals from closed-loop failures into compact supervisory knowledge for efficient policy updates. Requiring only a small number of rollouts and learning iterations, R²LPL elevates a moderately performing planner to state-of-the-art performance on the large-scale nuPlan closed-loop benchmark, demonstrating particularly significant improvements over existing approaches in the challenging Test14-hard long-tail scenarios.

Abstract

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 question, we propose Rollout-Retrieval Lifelong Policy Learning (R$^2$LPL), a policy learning framework that retrieves corrective targets from recoverable policy-induced mistakes and retains the resulting knowledge through lifelong policy learning. R^2LPL addresses a key bottleneck in continual policy improvement: closed-loop mistakes reveal where the policy is weak, but do not directly specify what the policy should learn. By filtering recoverable mistake-related states and retrieving feasible corrective targets, R$^2$LPL turns sparse failure evidence into compact supervised knowledge for stable and sample-efficient policy improvement. We evaluate R$^2$LPL on large-scale closed-loop nuPlan benchmarks. With only a few rollout and continual-learning cycles, R$^2$LPL elevates a learning-based planner with moderate initial performance to state-of-the-art performance across the evaluated benchmarks, especially on the challenging and long-tail Test14-hard split. These results demonstrate the effectiveness of R$^2$LPL in converting recoverable closed-loop mistakes into corrective knowledge for sustained policy improvement.

Problem

• autonomous driving
• lifelong learning
• policy improvement
• closed-loop mistakes
• continual learning

Innovation

• lifelong learning
• policy improvement
• mistake correction
• autonomous driving
• rollout-retrieval