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We present KOVIS, a novel learning-based, calibration-free visual servoingmethod for fine robotic manipulation tasks with eye-in-hand stereo camerasystem. We train the deep neural network only in the simulated environment; andthe trained model could be directly used for real-world visual servoing tasks.KOVIS consists of two networks. The first keypoint network learns the keypointrepresentation from the image using with an autoencoder. Then the visualservoing network learns the motion based on keypoints extracted from the cameraimage. The two networks are trained end-to-end in the simulated environment byself-supervised learning without manual data labeling. After training with dataaugmentation, domain randomization, and adversarial examples, we are able toachieve zero-shot sim-to-real transfer to real-world robotic manipulationtasks. We demonstrate the effectiveness of the proposed method in bothsimulated environment and real-world experiment with different roboticmanipulation tasks, including grasping, peg-in-hole insertion with 4mmclearance, and M13 screw insertion. The demo video is available athttp://youtu.be/gfBJBR2tDzA

KOVIS: Keypoint-based Visual Servoing with Zero-Shot Sim-to-Real Transfer for Robotics Manipulation