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，以建立先天理论在认知机器人中的作用。当前的架构有一个主要的弱点：语义层次，虽然可能是生物系统的模式，是在一个特设的方式选择。这个项目为以下认知功能水平开发领域理论：？自我认知：？车辆动力学控制：刚体运动是处理物理世界的基础，李代数是关键领域理论。特别限制？游戏规则（例如，交通法规）：基于规则的系统提供了理解规则的基本方法。用户交互？agent行为：图特征向量聚类用于分割其他agent的基本行为，包括人。操作背景？主动实施计算和通信控制：关于延迟、吞吐量、优先级和参数选择的高级策略提供了一个域理论。具体地说，该项目研究了对称理论如何在一个新的感知-动作架构中被利用。对称性在我们对世界的理解中起着深刻的作用，因为它解决了不变性的问题。保持状态的某些方面不变的运算符的确定使得在执行其他动作的同时识别对象或保持特定约束成为可能。研究了对称算子在信号分析、概念形成、学习和平台控制中的应用。这项工作的潜在影响是一个更具凝聚力，表达力，适应性和有效的认知机器人框架。研究结果将在机器人和人工智能会议以及国际研讨会（例如，施洛斯达格施图尔）。潜在的应用包括：认知车辆、传感器网络、建筑、互动玩具等。