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Human-centered environments are rich with a wide variety of spatial relationsbetween everyday objects. For autonomous robots to operate effectively in suchenvironments, they should be able to reason about these relations andgeneralize them to objects with different shapes and sizes. For example, havinglearned to place a toy inside a basket, a robot should be able to generalizethis concept using a spoon and a cup. This requires a robot to have theflexibility to learn arbitrary relations in a lifelong manner, making itchallenging for an expert to pre-program it with sufficient knowledge to do sobeforehand. In this paper, we address the problem of learning spatial relationsby introducing a novel method from the perspective of distance metric learning.Our approach enables a robot to reason about the similarity between pairwisespatial relations, thereby enabling it to use its previous knowledge whenpresented with a new relation to imitate. We show how this makes it possible tolearn arbitrary spatial relations from non-expert users using a small number ofexamples and in an interactive manner. Our extensive evaluation with real-worlddata demonstrates the effectiveness of our method in reasoning about acontinuous spectrum of spatial relations and generalizing them to new objects.

Metric Learning for Generalizing Spatial Relations to New Objects