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.
In this paper we present a biologically inspired neural architecture for visual perception based on anticipation. The main goal of this work is to demonstrate, that anticipation is a central key to improve the perception performance of technical systems. The presented approach is able to increase the robustness of the perception process against noise or sensory dropouts. We demonstrate these perceptional improvements through our architecture at the level of local navigation behavior of the miniature robot Khepera. We claim that perception is not an end in itself. Instead it is a sensorimotor process integrating the generation of behavior. There are many ways to define what constitutes a suitable landmark for mobile robot navigation, and automatically extracting landmarks from an environment as the robot travels is an open research problem. This paper describes an automatic landmark selection algorithm that chooses as landmarks any places where a trained sensory anticipation model makes poor predictions. The model is applied to a route navigation task, and the results are evaluated according to how well landmarks align between different runs on the same route. The quality of landmark matches is compared for several types of sensory anticipation models and also against a non-anticipatory landmark selector. We extend and correct the analysis presented in [6] and also present a more complete picture of the importance of sensory anticipation to the landmark selection process. Finally, we show that the system can navigate reliably in a goal-oriented route-following task, and we compare success rates using only metric distances with using a combination of odometric and landmark category information. 