arXiv AI
TAPDreamer: Transferable Adversarial Patches for World Action Models
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arXiv Machine Learning
Jul 17
BadWAM: When World-Action Models Dream Right but Act Wrong
arXiv:2607. 15207v1 Announce Type: new Abstract: World-action models (WAMs) are emerging as a promising foundation for embodied control: rather than predicting actions alone, they learn representations that couple action generation with future world prediction.
arXiv AI
Aug 5
Structure-Aware Robust Fine-Tuning: Defending Vision-Language-Action Robots Against Physical Attention Hijacking
arXiv:2608. 03231v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies promise general robotic manipulation, but their robustness against physical-world attacks remains fragile.
arXiv AI
Aug 14
UniTexture: Cross-Task Universal Adversarial Textures for Vision-Language-Action Models
arXiv:2608. 13453v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as generalist robotic policies capable of following diverse language instructions and performing a wide range of manipulation tasks.
arXiv AI
Jun 10
Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies
arXiv:2606. 10371v1 Announce Type: cross Abstract: Diffusion-based action generation has become a foundational component of embodied AI, but its reliance on visual conditioning leaves deployed visuomotor policies vulnerable to adversarial manipulation.
arXiv AI
Jun 16
ARB4WM: An Adversarial Robustness Benchmark for World Models in Continuous Control
arXiv:2606. 16605v1 Announce Type: new Abstract: World models are widely used in robotic and agentic engineering control systems due to their ability to learn latent dynamics for planning and decision-making.
arXiv Computer Vision
Sep 24
AWM-VLA: AlignedWorld Modeling for Efficient and Explainable Vision-Language-Action Policies
AWM‑VLA introduces a unified framework that embeds aligned world modeling directly into a diffusion‑transformer vision‑language‑action policy. By adding learnable future tokens aligned with vision‑language embeddings of future observations, the policy can anticipate long‑term consequences while generating actions. The method extends this with an object‑centric alignment objective and a principled weighting scheme, achieving up to 21% higher success rates on RoboCasa and humanoid tabletop benchmarks and producing object‑centric rationales preferred by human raters in 83% of cases.