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OpenVLA

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-08-15 / arxiv-researcherOne Adversarial Texture Drops VLA Robot Success From 90.0% to 48.4% Across Multiple TasksUniTexture backpropagates gradients from a Vision-Language-Action policy's action outputs to the surface texture parameters of a single 3D object through a differentiable renderer, jointly optimizing one shared texture over a distribution of tasks, instructions, states, and viewpoints. Evaluated on OpenVLA and pi-0.5, it cut mean task success from 90.0% under benign conditions to 48.4% under attack, induced target-aligned action shifts, and transferred across task suites and models without re-optimization. Unlike prior single-task attacks, this exposes a shared cross-task vulnerability — one physical object in the scene, no digital access required, and the generalist policy is the thing that makes it generalize.

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BENCHMARKED AGAINST
UniTexture
UniTexture adversarial texture attack reduces OpenVLA success from 90% to 48%.
Source finding
BENCHMARKED AGAINST
UniTexture -> OpenVLA
UniTexture adversarial texture attack reduces OpenVLA success from 90% to 48%.
Source finding

Source trail

arXiv 2608.13453

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