SGER: Constructivist Learning using ASyMTRe in Multi-Robot Teams

SGER：在多机器人团队中使用ASyMTRe的建构主义学习建构主义学习是基于先前经验主动学习新技能的过程。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他们的技能，从而扩展了多机器人团队中建构主义学习的最新技术。目前机器人中的建构主义学习方法发现了现有的低级机器人动作与期望行为之间的相关性。然而，由于这些现有的方法从如此低层次的动作抽象开始，它们的计算量非常大。我们新的建构主义学习方法从更高层次的抽象——感觉-运动图式——开始，这将使学习的计算效率更高。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-运动模式构建块来形成多机器人联盟，以解决手头的任务。这项建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通过将现有的低级模式构建块“分块”成有效的高级任务解决方案来不断提高他们的技能。这种新方法将为多机器人团队提供重要的新的终身学习能力，从而显著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。我们还打算证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。

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.