VLA-R: Vision-Language Action Retrieval toward Open-World End-to-End Autonomous Driving

• URL: http://arxiv.org/abs/2511.12405v1
• Date: Sun, 16 Nov 2025 00:55:28 GMT
• Title: VLA-R: Vision-Language Action Retrieval toward Open-World End-to-End Autonomous Driving
• Abstract summary: We present Vision-Language Action Retrieval (VLA-R), an open-world end-to-end autonomous driving framework.<n>We leverage a frozen vision-language model for open-world detection and segmentation to obtain multi-scale, prompt-guided, and interpretable perception features.<n>To learn transferable driving behaviors, we introduce a vision-action contrastive learning scheme.
• Score: 6.785438664749581
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Exploring open-world situations in an end-to-end manner is a promising yet challenging task due to the need for strong generalization capabilities. In particular, end-to-end autonomous driving in unstructured outdoor environments often encounters conditions that were unfamiliar during training. In this work, we present Vision-Language Action Retrieval (VLA-R), an open-world end-to-end autonomous driving (OW-E2EAD) framework that integrates open-world perception with a novel vision-action retrieval paradigm. We leverage a frozen vision-language model for open-world detection and segmentation to obtain multi-scale, prompt-guided, and interpretable perception features without domain-specific tuning. A Q-Former bottleneck aggregates fine-grained visual representations with language-aligned visual features, bridging perception and action domains. To learn transferable driving behaviors, we introduce a vision-action contrastive learning scheme that aligns vision-language and action embeddings for effective open-world reasoning and action retrieval. Our experiments on a real-world robotic platform demonstrate strong generalization and exploratory performance in unstructured, unseen environments, even with limited data. Demo videos are provided in the supplementary material.

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