In this paper a new methodology for action-oriented perception will be introduced. It is based on a previous method that used Turing Patterns in CNNs for the arousal of "perceptual states" as representation of the environmental condition. The emerging patterns were associated to codes which gave rise to learnable actions on a moving robot. Recently the new paradigm of Winnerless Competition (WLC) was taken into consideration to represent a suitable, bioinspired and efficient method to generate sequences of neural activations, strictly related to the spatial-temporal activity of input sensors. This fascinating property was recently peculiarly measured in the olfactory system, in particular in groups of neurons belonging to the insects' Antennal Lobe and to the mammalians' Olfactory Bulb. Taking inspiration from these experimental results and from the analytical model of the WLC, a cellular nonlinear model generating sequences of cell activation, representing the input pattern at the sensory level, will be used in an action-oriented perception framework. In fact simulation results showed the potentiality of the WLC approach to design dynamic networks for discrimination and classification, with a potentially huge memory capacity. In the present manuscript the WLC principle, implemented in a network of FitzHugh Nagumo neurons will be used within the whole framework for action-oriented perception, and the results will be applied to a roving robot.