Disclosed herein are a control server and method for controlling a robot using an artificial neural network, and a robot implementing the same. The robot includes a driver, a processor controlling the driver using an artificial neural network-based algorithm model, and a learning processor learning the algorithm model, where the algorithm model includes a first GAN (generative adversarial network) and a second GAN, the processor generates a plurality of real-like-fake images using a first generator included in a learned first GAN, the learning processor learns a second GAN based on a plurality of random texture rendered images and the plurality of real-like-fake images, and the processor generates a canonical image by inputting a real image to a second generator included in a learned second GAN, and controls the driver using the canonical image.