  We study a class of realistic computer vision settings wherein one can
influence the design of the objects being recognized. We develop a framework
that leverages this capability to significantly improve vision models'
performance and robustness. This framework exploits the sensitivity of modern
machine learning algorithms to input perturbations in order to design "robust
objects," i.e., objects that are explicitly optimized to be confidently
detected or classified. We demonstrate the efficacy of the framework on a wide
variety of vision-based tasks ranging from standard benchmarks, to
(in-simulation) robotics, to real-world experiments. Our code can be found at
https://git.io/unadversarial .
