Cellular Nonlinear Networks for the emergence of perceptual states: application to robot navigation control.
In this paper a new general purpose perceptual control architecture, based on nonlinear neural lattices, is presented and applied to solve robot navigation tasks. Insects show the ability to react to certain stimuli with simple reflexes, using direct sensory-motor pathways, which can be considered as basic behaviors, inherited and pre-wired. Relevant brain centres, known as Mushroom Bodies (MB) and Central Complex (CX) were recently identified in insects: though their functional details are not yet fully understood, it is known that they provide secondary pathways allowing the emergence of cognitive behaviors. These are gained through the coordination of the basic abilities to satisfy the insect's needs. Taking inspiration from this evidence, our architecture modulates, through a reinforcement learning, a set of competitive and concurrent basic behaviors in order to accomplish the task assigned through a reward function. The core of the architecture is constituted by the so-called Representation layer, used to create a concise picture of the current environment situation, fusing together different stimuli for the emergence of perceptual states. These perceptual states are steady state solutions of lattices of Reaction-Diffusion Cellular Nonlinear Networks (RD-CNN), designed to show Turing patterns. The exploitation of the dynamics of the multiple equilibria of the network is emphasized through the adaptive shaping of the basins of attraction for each emerged pattern. New experimental campaigns on standard robotic platforms are reported to demonstrate the potentiality and the effectiveness of the approach.
Arena
Paolo
P
Dipartimento di Ingegneria Elettrica, Elettronica e dei Sistemi, Universitá degli Studi di Catania, 95125 Catania, Italy. parena@diees.unict.it
De Fiore
Sebastiano
S
Patané
Luca
L
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D004777:Environment; D007313:Insecta; D007858:Learning / Q000502:physiology; D009038:Motion; D009042:Motivation; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010465:Perception / Q000502:physiology*; D012054:Reinforcement, Psychology; D012201:Reward; D012371:Robotics; D013028:Space Perception / Q000502:physiology; D013037:Spatial Behavior / Q000502:physiology*