  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.
