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Perception for Action: Dynamic Spatiotemporal Patterns Applied on a Roving Robot

机译：动作感知：动态漫游时空模式应用于漫游机器人

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In this article, we apply a bio-inspired control architecture to a roving robot performing different tasks. The key of the control system is the perceptual core, where heterogeneous information coming from sensors is merged to build an internal portrait representing the current situation of the environment. The internal representation triggers an action as the response to the current stimuli, closing the loop between the agent and the external world. The robot's internal state is implemented through a nonlinear lattice of neuron cells, allowing the generation of a large amount of emergent steady-state solutions in the form of Turing patterns. These are incrementally shaped, through learning, so as to constitute a "mirror" of the environmental conditions. Reaction-diffusion cellular nonlinear networks were chosen to generate Turing patterns as internal representations of the robot surroundings. The associations between incoming sensations and the perceptual core, and between Turing patterns and actions to be performed, are driven by two reward-based learning mechanisms. We report on simulation results and experiments on a roving robot to show the suitability of the approach.
机译：在本文中，我们将生物启发的控制架构应用于执行不同任务的巡回机器人。控制系统的关键是感知核心，来自传感器的异类信息将被合并以构建代表环境当前状况的内部肖像。内部表示触发一个动作，作为对当前刺激的响应，从而封闭了主体与外部世界之间的循环。机器人的内部状态是通过神经元细胞的非线性晶格实现的，从而允许生成大量以图灵模式形式出现的稳态解决方案。这些都是通过学习逐渐形成的，从而构成了环境条件的“镜子”。选择了反应扩散细胞非线性网络以生成图灵图案作为机器人周围环境的内部表示。传入的感觉与知觉核心之间，图灵模式与要执行的动作之间的关联由两种基于奖励的学习机制驱动。我们在粗纱机上报告了模拟结果和实验，以证明该方法的适用性。

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