EAGER: Innate Theories in Cognitive Robotics

EAGER：认知机器人的固有理论

• 批准号: 1021038
• 负责人: Thomas Henderson
• 金额: $ 3.64万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-15 至 2012-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1021038&HistoricalAwards=false
• 关键词: EAGER; Innate; Theories; Cognitive; Robotics

Research is performed to establish the role of innate theories in cognitive robotics. Current architectures have a major weakness: the semantic levels, although perhaps patterned after biological systems, are selected in an ad hoc way. This project develops domain theories for the following levels of cognitive function:? self-knowledge: ? dynamical vehicle control: rigid body motion is fundamentalto dealing with the physical world, and Lie algebra is used as the key domaintheory.? particular constraints ? rules of the game (e.g., traffic laws): rule-based systems provide the underlying methodology for understanding rules.? user interaction ? agent behavior: graph eigenvector clustering is used to segment basic behaviors of other agents, including people.? operational context ? active embodiment computation and communication control: high-level policies on latency, throughput, priority, and parameter selection provide a domain theory.Specifically, the project studies how symmetry theory can be exploited in a novel perception-action architecture. Symmetry plays a deep role in our understanding of the world, in that it addresses issues of invariance. The determination of operators which leave certain aspects of state invariant makes it possible to identify objects or maintain specific constraints while performing other actions. Symmetry operators in signal analysis, concept formation, learning and platform control are studied. The potential impact of the work is a more cohesive, expressive, adaptable and effective cognitiverobotics framework. Results will be disseminated at robotics and AI conferencesas well as at international workshops (e.g., Schloss Dagstuhl). Potential applications include: cognitive vehicles, sensor networks, buildings, interactive toys, etc.

进行研究以确立先天理论在认知机器人技术中的作用。当前的体系结构具有主要的弱点：语义水平虽然可能以生物系统为图案，但以临时方式选择。该项目为认知功能的以下级别开发域理论：自我知识：？动态车辆控制：刚性的身体运动是在与物理世界打交道的基础上，而Lie代数被用作关键域理论。特定的约束？游戏规则（例如，流量法）：基于规则的系统提供了理解规则的基本方法。用户互动？代理行为：图形特征向量聚类用于分割包括人员在内的其他代理的基本行为。操作环境？主动实施例计算和通信控制：关于潜伏期，吞吐量，优先级和参数选择的高级政策提供了域理论。特别是，该项目研究如何在新颖的感知练习中利用对称理论。对称性在我们对世界的理解中起着深厚的作用，因为它解决了不变性问题。保留状态不变的某些方面的操作员的确定使得在执行其他操作时可以识别对象或维护特定的约束。研究了信号分析，概念形成，学习和平台控制中的对称操作员。这项工作的潜在影响是一个更具凝聚力，表现力，适应性和有效的认知动物框架。结果将在机器人技术和AI会议上以及国际研讨会（例如Schloss Dagstuhl）中传播。潜在应用包括：认知车辆，传感器网络，建筑物，互动玩具等。