论文标题
可推广的任务表示从人类演示视频中学习：几何方法
Generalizable task representation learning from human demonstration videos: a geometric approach
论文作者
论文摘要
我们研究了从人类演示视频中学习的可推广任务学习的问题，而无需在机器人或预录用的机器人运动上进行额外的培训。给定一组人类的演示视频，显示了具有不同对象/工具（分类对象）的任务，我们旨在学习视觉观察的表示，将其推广到分类对象并启用有效的控制器设计。我们建议将几何任务结构介绍到表示学习问题的几何任务结构，该问题几何地编码了人类演示视频的任务规范，并通过在分类对象之间构建任务规范对应关系来实现概括。具体来说，我们建议使用图形结构的任务函数Covgs-IL来学习结构约束下的任务表示。我们的方法通过从不同对象的几何特征中选择其内部连接关系在几何约束中定义相同的任务来实现任务概括。然后，使用未校准的视觉伺服（UVS）将学习的任务表示形式转移到机器人控制器上；因此，消除了需要额外的机器人训练或预录用的机器人运动的需求。
We study the problem of generalizable task learning from human demonstration videos without extra training on the robot or pre-recorded robot motions. Given a set of human demonstration videos showing a task with different objects/tools (categorical objects), we aim to learn a representation of visual observation that generalizes to categorical objects and enables efficient controller design. We propose to introduce a geometric task structure to the representation learning problem that geometrically encodes the task specification from human demonstration videos, and that enables generalization by building task specification correspondence between categorical objects. Specifically, we propose CoVGS-IL, which uses a graph-structured task function to learn task representations under structural constraints. Our method enables task generalization by selecting geometric features from different objects whose inner connection relationships define the same task in geometric constraints. The learned task representation is then transferred to a robot controller using uncalibrated visual servoing (UVS); thus, the need for extra robot training or pre-recorded robot motions is removed.
