Beyond Patch Removal: Persistent Adversarial Effects in Vision-Language-Action Policies
cs.CV, cs.RO
Submitted: 2026-09-17
Updated: 2026-09-17
Terminology
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- Adversarial Patch
- Towards Powerful and Practical Patch Attacks for 2D Object Detection in Autonomous Driving
- Model-agnostic Adversarial Attack and Defense for Vision-Language-Action Models
- Uncovering Vulnerability of Vision-Language-Action Models under Joint-Level Physical Faults
- ARB4WM: An Adversarial Robustness Benchmark for World Models in Continuous Control
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- RT-1: Robotics Transformer for Real-World Control at Scale
- Adversarial Attacks on Robotic Vision Language Action Models
- VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking
- AttackVLA: Benchmarking Adversarial and Backdoor Attacks on Vision-Language-Action Models
- LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
- Eva-VLA: Evaluating Vision-Language-Action Models' Robustness Under Real-World Physical Variations
- Adversarial Attacks on Neural Network Policies
- Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies
- Distilling the Knowledge in a Neural Network
- robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
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