Lights, Camera, Malfunction: When Illumination Robustness Leaves VLA Models Blind to Color
Overview
This research paper introduces FLARE, a framework demonstrating how targeted illumination can disrupt Vision-Language-Action (VLA) models. The authors identify that standard adversarial training often leads models to ignore color as noise, degrading performance on color-dependent tasks. They propose ChromaGuard, a chroma-preserving adversarial training method tested on a physical 6-DoF robotic platform using SmolVLA, which achieves 95.0% success in benign tasks and 88.8% under attack, compared to a maximum of 48.8% for naively defended models.
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Reported quantity · Reported success rate: 88.8 percent · reported trials
“le inputs, while its success rate on benign, color-dependent real-world tasks drops to at most 48.8%, compared with 78.8% for the undefended SmolVLA baseline. To address this, we propose ChromaGuard, a chroma-preserving adversarial training method. On a physical 6-DoF robotic platform using SmolVLA, we demonstrate that ChromaGuard achieves 95.0% and 88.8% success rates in benign and attacked color-dependent tasks, re”
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Reported quantity · robot platform degrees of freedom: 6 other · Basis not reported
“le inputs, while its success rate on benign, color-dependent real-world tasks drops to at most 48.8%, compared with 78.8% for the undefended SmolVLA baseline. To address this, we propose ChromaGuard, a chroma-preserving adversarial training method. On a physical 6-DoF robotic platform using SmolVLA, we demonstrate that ChromaGuard achieves 95.0% and 88.8% success rates in benign and attacked color-dependent tasks, re”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalLights, Camera, Malfunction: When Illumination Robustness Leaves VLA Models Blind to Color
This research paper introduces FLARE, a framework demonstrating how targeted illumination can disrupt Vision-Language-Action (VLA) models. The authors identify that standard adversarial training often leads models to ignore color as noise, degrading performance on color-dependent tasks. They propose ChromaGuard, a chroma-preserving adversarial training method tested on a physical 6-DoF robotic platform using SmolVLA, which achieves 95.0% success in benign tasks and 88.8% under attack, compared to a maximum of 48.8% for naively defended models.
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