论文标题
操纵嵌入的运动分类法
A Motion Taxonomy for Manipulation Embedding
论文作者
论文摘要
为了从机械角度来表示动作，本文探讨了使用运动分类法嵌入的运动。通过这种分类法，可以将操作描述为称为运动代码的二元字符串。运动代码捕获机械性能，例如接触类型和轨迹，应用于定义运动或损失功能之间的合适距离指标，以进行深度学习和增强学习。运动代码也可用于合并共享类似属性的群集运动类型。使用现有数据集作为参考，我们讨论如何根据直觉和实际数据创建运动代码并分配给日常生活活动中通常看到的动作。将运动代码与预先训练的Word2Vec模型的向量进行比较，我们表明运动代码保持距离与操纵的现实非常匹配。
To represent motions from a mechanical point of view, this paper explores motion embedding using the motion taxonomy. With this taxonomy, manipulations can be described and represented as binary strings called motion codes. Motion codes capture mechanical properties, such as contact type and trajectory, that should be used to define suitable distance metrics between motions or loss functions for deep learning and reinforcement learning. Motion codes can also be used to consolidate aliases or cluster motion types that share similar properties. Using existing data sets as a reference, we discuss how motion codes can be created and assigned to actions that are commonly seen in activities of daily living based on intuition as well as real data. Motion codes are compared to vectors from pre-trained Word2Vec models, and we show that motion codes maintain distances that closely match the reality of manipulation.
