论文标题
从演示视频中估算运动代码
Estimating Motion Codes from Demonstration Videos
论文作者
论文摘要
运动分类法可以将操作编码为二进制编码表示，我们称为运动代码。这些运动代码天生代表了嵌入式空间中的操纵作用，该空间描述了运动的机械特征，包括接触和轨迹类型。使用运动代码嵌入的主要优点是，可以通过机器人相关的功能更适当地定义运动，并且可以使用这些运动功能更合理地测量其距离。在本文中，我们开发了一条深度学习管道，以无监督的方式从演示视频中提取运动代码，以便可以将这些视频的知识正确地代表和用于机器人。我们的评估表明，可以从Epic-Kitchens数据集中的动作演示中提取运动代码。
A motion taxonomy can encode manipulations as a binary-encoded representation, which we refer to as motion codes. These motion codes innately represent a manipulation action in an embedded space that describes the motion's mechanical features, including contact and trajectory type. The key advantage of using motion codes for embedding is that motions can be more appropriately defined with robotic-relevant features, and their distances can be more reasonably measured using these motion features. In this paper, we develop a deep learning pipeline to extract motion codes from demonstration videos in an unsupervised manner so that knowledge from these videos can be properly represented and used for robots. Our evaluations show that motion codes can be extracted from demonstrations of action in the EPIC-KITCHENS dataset.
