  Autonomous assembly of objects is an essential task in robotics and 3D
computer vision. It has been studied extensively in robotics as a problem of
motion planning, actuator control and obstacle avoidance. However, the task of
developing a generalized framework for assembly robust to structural variants
remains relatively unexplored. In this work, we tackle this problem using a
recurrent graph learning framework considering inter-part relations and the
progressive update of the part pose. Our network can learn more plausible
predictions of shape structure by accounting for priorly assembled parts.
Compared to the current state-of-the-art, our network yields up to 10%
improvement in part accuracy and up to 15% improvement in connectivity accuracy
on the PartNet dataset. Moreover, our resulting latent space facilitates
exciting applications such as shape recovery from the point-cloud components.
We conduct extensive experiments to justify our design choices and demonstrate
the effectiveness of the proposed framework.
