Incremental learning of an optical flow model for sensorimotor anticipation in a mobile robot

In this paper we study the mechanisms that enable an active agent to make long-term predictions of optical flow with a model that is learned dynamically. We analyse the optical flow distribution in terms of space and time, that is, what are the experienced optical flow values and how do they change in time. We show how complex the posterior distributions become when long-term predictions are needed, which breaks time-consistency assumption. The choice of one predictor or another should be made in terms of how the data is distributed. Moreover, we use a generic state-of-the-art incremental online learning algorithm [8] for the task of building a model to predict the optical flow perceived by a mobile robot. Finally, as an application, the model is also used to learn a simple predictor for anticipating an imminent collision.