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.

翻译：自动组合物体是机器人和3D计算机视觉中的一项基本任务,在机器人中已作为运动规划、动力器控制和避免障碍的问题进行了广泛研究。然而,开发一个对结构变体具有强大力量的普遍组装框架的任务仍然相对没有探讨。在这项工作中,我们利用一个经常性的图形学习框架来解决这个问题,其中考虑到各部门之间的关系和部分的逐步更新。我们的网络可以通过计算先前组装部件来了解对形状结构的更可信的预测。与目前的最新技术相比,我们的网络在部分精度上提高了10%,在PartNet数据集的连通性精确度上提高了15%。此外,我们产生的潜在空间促进了令人振奋的应用程序,例如从点球组件中恢复形状。我们进行了广泛的实验,以证明我们的设计选择的合理性,并展示了拟议框架的有效性。