SGER: Well Modeled Modular Robots for Complex Dynamic Motion

SGER：用于复杂动态运动的建模良好的模块化机器人

• 批准号: 0848118
• 负责人: Mark Yim
• 金额: $ 7万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0848118&HistoricalAwards=false
• 关键词: SGER; Well; Modeled; Modular; Robots; SGER; Well; Modeled; Modular; Robots

This SGER proposal concerns the accumulation and representation of skills and control knowledge by robots that interact with unstructured environments. There has been comparatively little work on representations that capture re-useable knowledge in robotics---an issue that lies at the heart of many future applications. Thus, this SGER represents a potentially transformative technology and addresses significant gaps in the state-of-the-art for which the payoff, despite the risk, is extremely high. We aim our 1 year study on learning techniques that accumulate knowledge related to grasping and manipulation. We shall extend pilot studies and build prototypes for self-motivated learning techniques and generative models for manipulation and multi-body contact relationships. The approach relies on learning to discover and exploit structure over the course of several staged learning episodes; from sensory and motor knowledge concerning the robot itself, to controllable relationships between the robot and external bodies, to multi-body contacts involved in tasks like stacking and insertion. The project has three principal technological goals: to advance the state-of-the-art of robotic manipulation and knowledge representation; to extend machine learning methods toward intrinsically motivated, cumulative, and hierarchical learning; and to advance computational accounts of the longitudinal processes of sensorimotor and cognitive development in humans and machines.

该SGER建议涉及与非结构化环境相互作用的机器人的技能和控制知识的积累和表示。在捕获机器人技术中可重复使用知识的表示形式上，几乎没有工作 - 这是许多未来应用的核心。因此，该SGER代表了一种潜在的变革性技术，并解决了最新的差距，尽管有风险，但该差距非常高。我们针对的是关于学习与抓握和操纵有关的知识的学习技术的一年研究。我们将扩展试点研究并建立针对自我激励学习技术的原型，以及用于操纵和多体接触关系的生成模型。该方法依赖于在几个分阶段学习情节的过程中学习发现和利用结构。从有关机器人本身的感觉和运动知识，到机器人与外部身体之间的可控关系，再到堆叠和插入等任务所涉及的多体接触。该项目具有三个主要的技术目标：促进机器人操纵和知识代表的最先进；将机器学习方法扩展到本质上的动机，累积和等级学习；并促进人类和机器中感觉运动和认知发展的纵向过程的计算说明。