To operate intelligently in domestic environments, robots require the ability to understand arbitrary spatial relations between objects and to generalize them to objects of varying sizes and shapes. In this work, we present a novel end-to-end approach to generalize spatial relations based on distance metric learning. We train a neural network to transform 3D point clouds of objects to a metric space that captures the similarity of the depicted spatial relations, using only geometric models of the objects. Our approach employs gradient-based optimization to compute object poses in order to imitate an arbitrary target relation by reducing the distance to it under the learned metric. Our results based on simulated and real-world experiments show that the proposed method enables robots to generalize spatial relations to unknown objects over a continuous spectrum.
翻译:为了在国内环境中明智地运作,机器人需要能够理解天体之间的任意空间关系,并将其推广到不同大小和形状的物体。在这项工作中,我们提出了一个基于远程计量学习的新型端对端方法,以普及空间关系。我们训练一个神经网络,将天体的三维点云转换成一个能捕捉描述的空间关系相似性的公制空间,只使用天体的几何模型。我们的方法是使用梯度优化来计算天体的构成,以便通过减少与天体的距离来模仿任意目标关系。我们基于模拟和现实世界实验的结果显示,拟议方法使机器人能够将空间关系概括到连续频谱上的未知天体。