Less Language, More Latents: Annotation-Efficient VLAs for Driving
Vision-language-action models (VLA) promise human-steerable autonomous driving, but their training is bottlenecked by the scarcity of frames paired with natural-language instructions: while camera streams and expert trajectories are logged at scale, language annotations (e.g., turn left at the intersection) remain scarce and expensive to acquire. To address this challenge, we introduce Latent Action Driving Annotations (LADA), a three-stage pipeline that transforms abundant unlabelled observation-trajectory pairs into a substrate for language-conditioned control. First, we train a latent action model with a vector-quantised bottleneck, producing a compact codebook of high-level vehicle in...
| Authors | Alexey Zakharov, Kemal Oksuz, Puneet K. Dokania |
|---|---|
| Primary category | cs.RO |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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