Human skill and task teaching is a complex process that relies on multiple means of interaction and learning on the part of the teacher and of the learner. In robotics, however, task teaching has largely been addressed by using a single modality. We present a framework that uses an action-embedded representation to unify interaction and imitation in human-robot domains, thereby providing a natural means for robots to interact with and learn from humans. The representation links perception and action in a unique architecture that represents the robot’s skills. The action component allows the use of implicit communication and endows the robot with the ability to convey its intentions through its actions on the environment. The perceptual component enables the robot to create a mapping between its observations and its actions and capabilities, allowing it to imitate a task learned from experiences of interacting with humans. This chapter describes a system that implements these capabilities and presents validation experiments performed with a Pioneer 2DX mobile robot learning various tasks.