SGER: Constructivist Learning using ASyMTRe in Multi-Robot Teams

SGER：在多机器人团队中使用 ASyMTRe 的建构主义学习

• 批准号: 0631958
• 负责人: Lynne Parker
• 金额: --
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0631958&HistoricalAwards=false
• 关键词: SGER; Constructivist; Learning; using; ASyMTRe

SGER: Constructivist Learning using ASyMTRe in Multi-Robot TeamsConstructivist learning is the process of actively learning new skills based on previous experience. This research project extends the state of the art for constructivist learning in multi-robot teams by developing new, computationally efficient techniques that allow robot team members to continually improve their skills over time. Current approaches to constructivist learning in robotics find correlations between existing low-level robot actions and a desired behavior. However, because these existing approaches begin with such a low level of action abstraction, they are extremely computationally intensive. Our new constructivist learning approach begins at a higher level of abstraction - the sensori-motor schema - which should enable much more computationally efficient learning. Our approach builds upon our prior work, called ASyMTRe, that forms multi-robot coalitions by automatically combining sensori-motor schema building blocks to solve the task at hand. This proposed work adds an important learning component allowing robot team members to continually improve their skills by "chunking" existing low-level schema building blocks into efficient higher-level task solutions. This new approach will provide important new lifelong learning capabilities to multi-robot teams, thus significantly facilitating their use in real-world applications, such as search and rescue, security, mining, hazardous waste cleanup, industrial and household maintenance, automated manufacturing, and construction. We also intend to show that the proposed techniques are applicable to other types of robotic systems, including humanoid and service robots, and thus have a broader impact on the robotics field as a whole.

SGER：在多机器人团队中使用ASyMTRe的建构主义学习建构主义学习是基于以前的经验积极学习新技能的过程。 该研究项目通过开发新的、计算效率高的技术，使机器人团队成员能够随着时间的推移不断提高技能，从而扩展了多机器人团队中建构主义学习的最新技术水平。 目前机器人中的建构主义学习方法发现了现有低级机器人动作与期望行为之间的相关性。 然而，因为这些现有的方法开始具有这样低的动作抽象级别，所以它们在计算上是极其密集的。 我们新的建构主义学习方法从更高的抽象层次开始-感觉运动图式-这应该能够实现更有效的计算学习。 我们的方法建立在我们之前的工作基础上，称为ASyMTRe，它通过自动组合感觉运动模式构建块来解决手头的任务，从而形成多机器人联盟。 这项拟议的工作增加了一个重要的学习组件，允许机器人团队成员不断提高他们的技能，通过“分块”现有的低级别架构构建块到高效的高级别任务解决方案。 这种新方法将为多机器人团队提供重要的终身学习能力，从而大大促进它们在现实世界中的应用，如搜索和救援，安全，采矿，危险废物清理，工业和家庭维护，自动化制造和建筑。 我们还打算表明，所提出的技术适用于其他类型的机器人系统，包括人形机器人和服务机器人，从而对整个机器人领域产生更广泛的影响。