By probing a VLA's latent representations during task execution, you can detect failures 30% earlier than existing methods and maintain reliable failure predictions even when the robot encounters new tasks it wasn't trained on.
VLA-Tug is a lightweight monitoring system that detects when vision-language-action robots are about to fail by analyzing their internal representations. It predicts both whether failure will occur and what type of failure it will be, enabling corrective actions while running efficiently on edge devices.