INSPIRE: Signals to Symbols: From Bio-inspired Hardware to Cognitive Systems

INSPIRE：信号到符号：从仿生硬件到认知系统

• 批准号: 1248056
• 负责人: Cornelia Fermuller
• 金额: $ 60万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1248056&HistoricalAwards=false
• 关键词: INSPIRE; Signals; Symbols; Bio; inspired

This INSPIRE award is partially funded by the Science of Learning Centers Program in the Division of Behavioral, Cognitive and Social Sciences in the Directorate for Social, Behavioral and Economic Sciences; the Perception, Action, and Cognition Program in the Division of Behavioral, Cognitive and Social Sciences in the Directorate for Social, Behavioral and Economic Sciences; the Energy, Power, and Adaptive Systems Program in the Division of Electrical Communication and Cyber Systems in the Directorate of Engineering; and the Applied Mathematics and Mathematical Biology Program in the Division of Mathematical Sciences in the Directorate for Mathematical and Physical Sciences. This research project draws on knowledge from many disciplines (neuroscience, cognitive science, computational science, mathematics and engineering) to create cognitive systems capable of interpreting observed, complex human movements and actions. New design methodologies will be developed for the integration of sensory modalities (vision, audition, touch) and their support of higher cognitive function (language, reasoning). In contrast to existing approaches which tend to be assemblies of modular components each solving its task in isolation, this team takes a novel approach called Active Cognition which has the following features: 1) Instead of modeling the different perceptual processes (vision, audition, and haptics), cognition, and motor control in isolation, the modules are integrated and capabilities co-developed in the tradition of dynamical systems theory to obtain a reasoning system where "the whole is greater than the sum of its parts"; 2) instead of segregating the low level processing of signals from the processing of higher level symbolic information, they will interact in a continuous dialogue, such that high level knowledge will leverage perception; and 3) instead of separating physical embodiment from algorithmic considerations, biologically inspired real-time hardware will be developed that implements complex functions by integrating signals and symbols. The project is organized in two working groups. The first group will develop a cognitive robot that can recognize complex human activities using visual and auditory signals captured by biological-inspired hardware. The second group will study attention in humans by measuring human response to audition and vision through EEG and MEG, and subsequently implementing the findings in robots. A yearly three-week, hands-on workshop will educate students, serve as testing ground for the team's ideas, and stimulate new collaborations. This workshop will also engage the involvement of the interdisciplinary research community that has formed around the goal of building biologically inspired cognitive systems. Success in integrating different components of a cognitive system (hardware, sensors, and software) has the potential to catalyze a new industry of biologically-inspired cognitive systems, including household and service robots, and systems for intelligent transportation and smart manufacturing. In addition, this interdisciplinary project will play a significant role in building capacity for a new emphasis area in engineering and training of cognitive systems engineers who need combined expertise in computer science, electrical engineering and cognitive neuroscience.

该INSPIRE奖部分由社会、行为和经济科学理事会行为、认知和社会科学司的学习中心计划科学资助;社会、行为和经济科学理事会行为、认知和社会科学司的感知、行动和认知计划;能源，电力和自适应系统计划在电气通信和网络系统的工程局的司;和应用数学和数学生物学计划在数学和物理科学局的数学科学司。 该研究项目借鉴了许多学科（神经科学，认知科学，计算科学，数学和工程）的知识，以创建能够解释观察到的复杂人类运动和动作的认知系统。将开发新的设计方法，以整合感官形式（视觉，听觉，触觉）及其对高级认知功能（语言，推理）的支持。 与现有的方法相比，这些方法往往是模块化组件的组装，每个组件都孤立地解决其任务，该团队采用了一种称为主动认知的新方法，该方法具有以下特点：1）而不是对不同的感知过程进行建模（视觉、听觉和触觉）、认知和运动控制，这些模块被集成，并在动力系统理论的传统中共同开发能力，以获得“整体大于其部分之和”的推理系统; 2）不是将低层次的信号处理与高层次的符号信息处理分开，而是在连续的对话中相互作用，这样高层次的知识将影响感知;以及3）代替将物理实施例与算法考虑分开，将开发生物启发的实时硬件，其通过集成信号和符号来实现复杂功能。 该项目分为两个工作组。第一个小组将开发一种认知机器人，它可以使用生物启发硬件捕获的视觉和听觉信号来识别复杂的人类活动。第二组将通过脑电波和脑磁图测量人类对听觉和视觉的反应来研究人类的注意力，然后在机器人中实施这些发现。每年为期三周的实践研讨会将教育学生，作为团队想法的试验场，并激发新的合作。 该研讨会还将吸引跨学科研究社区的参与，该社区围绕构建生物启发的认知系统的目标而形成。成功整合认知系统的不同组件（硬件，传感器和软件）有可能催化一个由生物启发的认知系统的新产业，包括家用和服务机器人，以及智能交通和智能制造系统。 此外，这一跨学科项目将发挥重要作用，建设能力的一个新的重点领域，在工程和培训认知系统工程师谁需要在计算机科学，电气工程和认知神经科学的综合专业知识。