This paper presents a novel object-object affordance learning approach that enables intelligent robots to learn the interactive functionalities of objects from human demonstrations in everyday environments. Instead of considering a single object, we model the interactive motions between paired objects in a human-object-object way. The innate interaction-affordance knowledge of the paired objects are learned from a labeled training dataset that contains a set of relative motions of the paired objects, human actions, and object labels. The learned knowledge is represented with a Bayesian Network, and the network can be used to improve the recognition reliability of both objects and human actions and to generate proper manipulation motion for a robot if a pair of objects is recognized. This paper also presents an image-based visual servoing approach that uses the learned motion features of the affordance in interaction as the control goals to control a robot to perform manipulation tasks.