Physical and Digital Adversarial Attacks on Grasp Quality Networks

Abstract

Grasp Quality Networks are important components of grasping-capable autonomous robots, as they allow them to evaluate grasp candidates and select the one with highest chance of success. The widespread use of pick-and-place robots and Grasp Quality Networks raises the question of whether such systems are vulnerable to adversarial attacks, as that could lead to large economic damage. In this paper we propose two kinds of attacks on Grasp Quality Networks, one assuming physical access to the workspace (to place or attach a new object) and another assuming digital access to the camera software (to inject a pixel-intensity change on a single pixel). We then use evolutionary optimization to obtain attacks that simultaneously minimize the noticeability of the attacks and the chance that selected grasps are successful. Our experiments show that both kinds of attack lead to drastic drops in algorithm performance, thus making them important attacks to consider in the cybersecurity of grasping robots. Source code can be found at https://github.com/Naif-W-Alharthi/Physical-and-Digital-Attacks-on-Grasping-Networks