Affordance perception refers to the ability of an agent to extract meaning and usefulness of objects in its environment. Cognitive affordance is a richer notion that extends traditional aspects of object functionality and action possibilities by incorporating the influence of changing context, social norms, historical precedence, and uncertainty. This allows for an increased flexibility with which to reason about affordances in a situated manner. Existing work in cognitive affordances, while providing the theoretical basis for representation and inference, does not describe how they can be learned, integrated, and used with a robotic system. In this work, we describe, demonstrate,and evaluate an integrated robotic architecture that can learn cognitive affordances for objects from natural language and immediately use this knowledge in dialogue-based learning and instruction.