In order to anticipate dangerous events, like a collision, an agent needs to make long-term predictions. However, those are challenging due to uncertainties in internal and external variables and environment dynamics. A sensorimotor model is acquired online by the mobile robot using a state-of-the-art method that learns the optical flow distribution in images, both in space and time. The learnt model is used to anticipate the optical flow up to a given time horizon and to predict an imminent collision by using reinforcement learning. We demonstrate that multi-modal predictions reduce to simpler distributions once actions are taken into account.
@article{arxiv.1210.1104,
title = {Sensory Anticipation of Optical Flow in Mobile Robotics},
author = {Arturo Ribes and Jesús Cerquides and Yiannis Demiris and Ramón López de Mántaras},
journal= {arXiv preprint arXiv:1210.1104},
year = {2012}
}