NRI: Small: Online Training of Hierarchical Multirobot Teams

NRI：小型：分层多机器人团队的在线培训

• 批准号: 1317813
• 负责人: Sean Luke
• 金额: $ 50万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1317813&HistoricalAwards=false
• 关键词: NRI; Small; Online; Training; Hierarchical

This project involves a new approach to robot learning from demonstration which allows experimenters to teach multiple robots nontrivial, heterogenous collective behaviors in real-time. Training multiple robots is challenging because while the experimenter may know what emergent collective behavior he wishes to achieve, it is unlikely that he knows what individual robot behaviors, and their interactions, will achieve it. This project decomposes robot teams or swarms into a social hierarchy of leaders and subordinates. Small groups are then trained at the bottom of the hierarchy relatively simple collective behaviors, then groups of leaders are trained progressively with more complex and abstract behaviors, until all trained top-level root behaviors have been learned. The project examines homogeneous behaviors within robot groups, heterogeneous behaviors among the robots, and mixtures of the two at any level in the hierarchy. The method builds on prior work in single-robot behavior training, which used decomposed hierarchies of behaviors consisting of finite-state automata and learned transition functions. Beyond robotics, the project also allows the development of group behaviors for virtual agents found in animation and in emerging agent-based models in the social sciences and biology. The work integrates with a teaching and research robotics laboratory, with agent-based modelers in the social sciences, and with robotics education efforts in K-12 underprivileged schools.

该项目涉及一种新的方法，机器人学习从演示，允许实验者教多个机器人非平凡的，异构的集体行为实时。 训练多个机器人是具有挑战性的，因为虽然实验者可能知道他希望实现什么样的集体行为，但他不太可能知道什么样的个体机器人行为及其相互作用将实现它。 然后在层次结构的底部训练小团体相对简单的集体行为，然后用更复杂和抽象的行为逐步训练领导者群体，直到所有训练过的顶层根行为都已经学会。该项目检查机器人组内的同质行为，机器人之间的异构行为，以及在层次结构中的任何级别上的两者的混合。 该方法建立在单机器人行为训练中的先前工作的基础上，该工作使用由有限状态自动机和学习的过渡函数组成的行为的分解层次。除了机器人技术，该项目还允许在动画和社会科学和生物学中新兴的基于代理的模型中发现的虚拟代理的群体行为的发展。 这项工作与教学和研究机器人实验室，社会科学中基于代理的建模人员以及K-12贫困学校的机器人教育工作相结合。