First author of a regularization-based study of Vision-Language-Action (VLA) models, now available as a preprint on arXiv. The project introduces a regularization term into VLA training to improve the behavior and robustness of learned policies, with both a principled motivation and empirical evaluation.
My contribution covers the method design, the theoretical motivation, and the experimental evaluation.
Keywords / skills involved: vision-language-action models, robot learning, regularization, imitation learning, policy training.