Impact of Motion-Image Diffusion Priors on Vision-Language-Action Model Robustness Against Adversarial Perturbations

Machine learning (ML) systems have introduced significant advances in various fields, due to the introduction of highly complex models. Despite their success, it has been shown multiple times that machine learning models are prone to imperceptible perturbations that can severely degrade their accuracy. So far, existing studies have primarily focused on models where supervision across all classes were available. In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes. In this paper, we present a study aimed on evaluat

Research goal: What is the impact of integrating motion-image diffusion priors on the robustness of vision-language-action models against adversarial perturbations in zero-shot cross-domain settings?

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