We propose a learning framework to find the representation of a robot's kinematic structure and motion embedding spaces using graph neural networks (GNN). Finding a compact and low-dimensional embedding space for complex phenomena is a key for understanding its behaviors, which may lead to a better learning performance, as we observed in other domains of images or languages. However, although numerous robotics applications deal with various types of data, the embedding of the generated data has been relatively less studied by roboticists. To this end, our work aims to learn embeddings for two types of robotic data: the robot's design structure, such as links, joints, and their relationships, and the motion data, such as kinematic joint positions. Our method exploits the tree structure of the robot to train appropriate embeddings to the given robot data. To avoid overfitting, we formulate multi-task learning to find a general representation of the embedding spaces. We evaluate the proposed learning method on a robot with a simple linear structure and visualize the learned embeddings using t-SNE. We also study a few design choices of the learning framework, such as network architectures and message passing schemes.
翻译:我们建议了一个学习框架,以利用图形神经网络(GNN)找到机器人运动结构和运动嵌入空间的表示。找到一个紧凑和低维嵌入空间以适应复杂现象是了解其行为的关键,这可能导致更好的学习性能,正如我们在图像或语言的其他领域所观察到的那样。然而,尽管许多机器人应用程序涉及各种类型的数据,但机器人学家对所生成数据的嵌入研究相对较少。为此,我们的工作旨在学习两种机器人数据类型的嵌入:机器人的设计结构,例如链接、联合和它们的关系,以及运动数据,例如运动联合位置。我们的方法利用机器人的树结构来训练与给定的机器人数据的适当嵌入。为了避免过度匹配,我们开发多功能学习,以找到嵌入空间的一般代表。我们用简单的线性结构来评价一个机器人的拟议学习方法,并用t-SNE来想象所学的嵌入方式。我们还研究了学习框架的一些设计选择,例如网络架构和传递信息计划。