Incremental Learning for Mapping Image Variations to Actions

In this paper we introduce a method to learn the global relationship between camera motion and its effect on a feature space. Drawing from progress in vision based motion control, we derive an image-based method that learns an approximation of the visuomotor function while performing visual servoing tasks. The result is a reactive agent that measures its performance in the image feature space and controls a robot with position error commands. Thus combining image-based references with position-based commands.