A robot operating in multiple settings must develop stable as well as flexible representations of the tasks and contexts associated with their environments. Taking inspiration from neurobiology, we apply a neural network model of schemas and memory consolidation to train the Toyota Human Support Robot to find and retrieve objects in indoor settings. We define schemas to be collections of objects bound together by a common context. In this case, the robot must learn schemas associated with rooms found in a school based on objects typically found in those rooms. Because the model develops schema representations for each room, the robot can rapidly perform object retrieval tasks associated with familiar schemas and disambiguate the tasks by context. Our experiment explores the effects of the model in an embodied setting and shows the benefits of applying research in memory consolidation to contextual awareness in robotics.