Abstract This paper proposes a novel framework that enables a robot to learn ordinal object relations. While most related work focuses on classifying objects into discrete categories, such approaches cannot learn object properties (e.g., weight, height, size, etc.) that are context-specific and relative to other objects. To address this problem, we propose that a robot should learn to order objects based on ordinal object relations. In our experiments, the robot explored a set of 32 objects that can be ordered by three properties: height, weight, and width. Next, the robot used unsupervised learning to discover multiple ways that the objects can be ordered based on the haptic and proprioceptive perceptions detected while exploring the objects. Following, the robot’s model was presented with labeled object series, allowing it to ground the three ordinal relations in terms of how similar they are to the orders discovered during the unsupervised stage. Finally, the grounded models were used to recognize whether new object series were ordered by any of the three properties as well as to correctly insert additional objects into an existing series.