We address the problem of imitation learning in interactive robots which learn from task demonstrations. Many current approaches to interactive robot learning are performed over a set of demonstrations, where the robot observes several demonstrations of the same task and then creates a generalized model. In contrast, we aim to enable a robot to learn from individual demonstrations, each of which are stored in the robot’s memory as source cases. When the robot is later tasked with repeating a task in a new environment containing a different set of objects, features, or a new object configuration, the robot would then use a case-based reasoning framework to retrieve, adapt, and execute the source case demonstration in the new environment. We describe our ongoing work to implement this case-based framework for imitation learning in robotic agents.