Measuring the quality of visual learning

Biology often offers valuable example of systems both for learning and for controlling motion. Work in robotics has often been inspired by these findings in diverse ways. Nevertheless, the fundamental aspects that involve visual landmark learning has never been approached formally. In this paper we introduce results that explain how the visual learning works. Furthermore, these tools provide bases to measure the quality of visual landmark learning. Basically, the theoretical tools emerge from the navigation vector field produced by the visual navigation strategy. The learning process influence the motion vector field whose features are addressed