Arena Paolo, De Fiore Sebastiano, Patané Luca
Dipartimento di Ingegneria Elettrica, Elettronica e dei Sistemi, Universitá degli Studi di Catania, 95125 Catania, Italy.
Neural Netw. 2009 Jul-Aug;22(5-6):801-11. doi: 10.1016/j.neunet.2009.06.024. Epub 2009 Jul 1.
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.
本文提出了一种基于非线性神经晶格的新型通用感知控制架构，并将其应用于解决机器人导航任务。昆虫能够利用直接的感觉运动通路，通过简单反射对特定刺激做出反应，这些通路可被视为遗传和预先连接好的基本行为。最近在昆虫中发现了相关的脑区，即蘑菇体（MB）和中央复合体（CX）：尽管它们的功能细节尚未完全了解，但已知它们提供了允许认知行为出现的次级通路。这些行为是通过协调基本能力以满足昆虫需求而获得的。受此证据启发，我们的架构通过强化学习来调节一组竞争性和并行的基本行为，以完成通过奖励函数分配的任务。该架构的核心由所谓的表示层构成，用于创建当前环境状况的简洁图像，将不同刺激融合在一起以产生感知状态。这些感知状态是反应扩散细胞非线性网络（RD-CNN）晶格的稳态解，旨在展示图灵模式。通过对每个出现模式的吸引盆进行自适应塑造，强调了对网络多个平衡点动态的利用。报告了在标准机器人平台上开展的新实验活动，以证明该方法的潜力和有效性。
