Past work on acquisition of word-object associations in robots has focused on either fast instruction-based methods which accept highly constrained input or gradual cross-situational learning methods, but not a mixture of both. In this paper, we present an integrated robotic system which allows for a combination of these methods to contribute to the task of learning the labels of objects in AI agents. We demonstrate the expanded word learning capabilities in the outcome system and how learning from both human-human and human-robot dialogues can be achieved in one integrated system.