SL-CN: Cortical Architectures for Robust Adaptive Perception and Action

这种受生物启发的方法的动机是设计一种系统，能够在他们从未经历过的混乱和嘈杂的场景中感知并采取行动。这与计算工程系统的最先进水平形成了鲜明对比，每次面对意想不到的环境时，计算工程系统都需要重新培训。主要原因是，目前关于感知的方法孤立地处理具体问题，并不认为感知的主要作用是支持具有运作中的机构的系统。结果，他们被限制在他们受过训练的情况下，不能对不断变化的任务和场景做出反应。通过专注于认知原语而不是具体的应用，这项工作有望极大地促进机器感知的技术水平，并导致开发出能够稳健和在线地适应新环境、对新情况做出反应并学习新环境的系统。为此，将研究感知和行动的新理论公式，以及具有在线学习能力的高速、低功耗硬件实现，同时吸收神经科学的新见解。因此，这项工作将把神经科学、认知科学、应用数学、计算机科学和工程学联系起来，以降低将交互机器人保持在科幻领域的为数不多的障碍之一。除了学术上的贡献，这项工作还有望为以模块化方式设计具有自适应感知的系统提供技术诀窍，这些系统具有可重复使用的组件。这类系统在计算视觉和听觉感知问题中有应用，并可以促进认知生物启发的机器人和辅助设备的产业。这一提议为智能感知系统的设计和合成智能的发展提出了新的想法。智能系统解决的几乎任何任务都涉及四个基本过程的相互作用，这四个过程致力于：(A)上下文、(B)注意力、(C)分割和(D)分类。该网络的成员将通过将神经建模与神经和行为实验、理论和计算建模以及在机器人学中的实施相结合来研究这些规范的认知原语。然后，理论洞察力的发现将被调整以满足现实行为的需求，并为稳健和不变的感知和行动的应用开发技术解决方案。拟议的协作网络将由一个小型的科学和工程研究小组组成，以直接解决稳健的适应性感知和行动方面的问题。然后，它将指导人员，并向夏季研讨会注入成果和教学内容，该研讨会旨在包括一个全球研究人员网络。

The motivation for this biologically-inspired approach is to design systems that perceive and act in cluttered and noisy scenes that they have never experienced. This stands in contrast with the state of the art in computational engineering systems that need to be re-trained each time they confront an unanticipated environment. The main reason is that current approaches to perception address specific problems in isolation and do not consider that the primary role of perception is to support systems with bodies in action. As a result, they are constrained to the situations for which they were trained and cannot react to changing tasks and scenes. By focusing on cognition primitives rather than specific applications, the work is expected to greatly advance the state of the art of machine perception and lead to the development of systems that can robustly and on-line adapt to new environments, react to novel situations and learn new contexts. To do so, novel theoretical formulations of perception and action and high-speed, low-power, hardware implementations with on-line learning capabilities will be studied while assimilating new insights from the neurosciences. Consequently, this work will network neuroscience, cognitive science, applied mathematics, computer science and engineering so as to lower one of the few remaining barriers that keeps interactive robots in the realm of science fiction. Beyond the scholarly contribution, the work is expected to provide know-how for the design of systems with adaptive perception in a modular fashion with reusable components. Such systems have applications in computational vision and auditory perception problems and can advance the industry of cognitive biologically-inspired robotics and assistive devices.This proposal sets forward novel ideas in the design of intelligent perceptual systems and the development of synthetic intelligence. Just about any task which an intelligent system solves involves the interplay of four basic processes that are devoted to: (a) context, (b) attention, (c) segmentation and (d) categorization. The members of the proposed network will study these canonical cognitive primitives by combining neural modeling with neural and behavioral experiments, theoretical and computational modeling and implementation in robotics. The findings of theoretical insights will then be adapted to satisfy the demands of realistic behavior, and to develop technological solutions for applications of robust and invariant perception and action. The proposed collaborative network will consist of a small science and engineering research team to directly address the questions in robust adaptive perception and action. It will then direct personnel, and inject results and pedagogical content to a Summer Workshop that aims to include a global network of researchers.