Simultaneously learning to recognize and control a low-cost robotic arm

In this paper we present a visual servoing method based on a learned mapping between feature space and control space. Using a suitable recognition algorithm, we present and evaluate a complete method that simultaneously learns the appearance and control of a low cost robotic arm. The recognition part is trained using an action precedes perception approach. The novelty of this paper, apart from the visual servoing method per se, is the combination of visual servoing with gripper recognition. We show that we can achieve high precision positioning without knowing in advance what the robotic arm looks like or how it is controlled.