Abstract: The intelligent perception, positioning, and tracking of an unknown object using only the current image feature has been a challenge in the field of visual servo robot systems because of the absence of any model information about the objectprior to the task. Here, "unknown" does not only refer to the absence of data on the classification properties and localization of all objects on the operating table, but also refers to not having the prior information on geometry such as shape and size of the manipulated objects. To solve this problem, we propose a vision-based robot positioning control strategy for unknown objects by employing the learning capability of convolutional neural network (CNN). First, we use a well-trained object detection network based on CNN to classify and detect objects to obtain the class labels of the objects on the operating table of the serving robot. Secondly, we select the object to be manipulated randomly by any user according to the results of automatic recognition and detection. Following this well-trained detection network, we detect the target object for subsequent images, and then compute the current image features to realize anintelligent perception for the unknown object. Finally, to achieve the robot visual positioning for unknown objects, we design a visual sliding mode positioning control law according to the image feature error to drive robotic hand/claw for expected motion. Five different experiments of robot visual positioning for unknown objects in complex natural scenes are carried out using a MOTOMAN-SV3X industrial manipulator. The experimental results confirmthe feasibility and effectiveness of the proposed robot visual positioning control strategy for unknown objects.