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

SL-CN：用于鲁棒自适应感知和行动的皮质架构

• 批准号: 1540916
• 负责人: Cornelia Fermuller
• 金额: $ 74.98万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1540916&HistoricalAwards=false
• 关键词: SL; CN; Cortical; Architectures; Robust

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.

这种受生物启发的方法的动机是设计出能够在它们从未经历过的混乱和嘈杂的场景中感知和行动的系统。这与计算工程系统的最新技术形成了鲜明对比，计算工程系统在每次面对意外环境时都需要重新训练。主要原因是，目前的看法孤立地处理具体问题，没有考虑到看法的主要作用是支持有行动机构的系统。因此，他们被限制在他们接受培训的情况下，无法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特定的应用程序，这项工作预计将大大推进机器感知的艺术状态，并导致系统的发展，可以强大地和在线适应新的环境，对新的情况作出反应，并学习新的上下文。要做到这一点，新的理论公式的感知和行动和高速，低功耗，硬件实现与在线学习能力将进行研究，同时吸收新的见解从神经科学。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，以降低将交互式机器人保持在科幻小说领域的少数障碍之一。除了学术贡献，这项工作预计将提供专门知识的系统设计与自适应感知模块化的方式与可重用组件。这类系统在计算视觉和听觉感知问题中具有应用，可以促进认知生物启发的机器人和辅助设备的工业发展，这一提议在智能感知系统的设计和合成智能的发展方面提出了新的想法。智能系统解决的任何任务都涉及四个基本过程的相互作用：（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机器人技术的实现相结合来研究这些规范的认知原语。 然后，理论见解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。拟议的合作网络将由一个小型科学和工程研究团队组成，直接解决鲁棒自适应感知和行动中的问题。然后，它将指导人员，并将结果和教学内容注入旨在包括全球研究人员网络的夏季研讨会。