Rigid Body Adversarial Attacks
This addresses potential safety and accuracy issues in robotics and engineering applications where rigid assumptions may lead to errors, though it is incremental as it adapts adversarial attack concepts to a new domain.
The authors tackled the problem of rigid body simulators being vulnerable to adversarial attacks by constructing objects that behave identically in rigid simulations but maximally differently in deformable ones, demonstrating validity through comparisons in commercial simulators.
Due to their performance and simplicity, rigid body simulators are often used in applications where the objects of interest can considered very stiff. However, no material has infinite stiffness, which means there are potentially cases where the non-zero compliance of the seemingly rigid object can cause a significant difference between its trajectories when simulated in a rigid body or deformable simulator. Similarly to how adversarial attacks are developed against image classifiers, we propose an adversarial attack against rigid body simulators. In this adversarial attack, we solve an optimization problem to construct perceptually rigid adversarial objects that have the same collision geometry and moments of mass to a reference object, so that they behave identically in rigid body simulations but maximally different in more accurate deformable simulations. We demonstrate the validity of our method by comparing simulations of several examples in commercially available simulators.