PriorVLA: Prior-Preserving Adaptation for Vision-Language-Action Models

Vision-Language-Action (VLA) models have shown promise for generalizable robot control but struggle when adapting to new domains while preserving prior capabilities. This paper introduces PriorVLA, a method that preserves pre-trained priors during VLA fine-tuning through constrained optimization in ... The method demonstrates significant improvements over existing approaches, providing both theoretical insights and practical benefits for real-world deployment. Comprehensive experiments validate the effectiveness of the proposed approach across diverse scenarios and task settings.