How does the robustness of CLAM's latent action models to noisy or occluded inputs vary between multimodal and

Abstract

Abstract: Pre-trained vision-language (VL) models are highly vulnerable to adversarial attacks. However, existing defense methods primarily focus on image classification, overlooking two key aspects of VL tasks: multimodal attacks, where both image and text can be perturbed, and the one-to-many relationship of images and texts, where a single image can correspond to multiple textual descriptions and vice versa (1:N and N:1). This work is the first to explore defense strategies against multimodal attacks in VL tasks, whereas prior VL defense methods focus on vision robustness. We propose multimodal adver

Research Question

How does the robustness of CLAM's latent action models to noisy or occluded inputs vary between multimodal and unimodal training, as evaluated by degradation in task completion rates on BridgeData V2 under adversarial conditions?

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