Zero-Shot Visual Imitation

The current dominant paradigm of imitation learning relies on strong supervision of expert actions for learning both what to and how to imitate. We propose an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its own experience into a goal-conditioned skill policy using a novel forward consistency loss formulation. In our framework, the role of the human expert is only to communicate goals (i.e., what to imitate) during inference. The learned policy is then employed to mimic the expert (i.e., how to imitate) after observing just a visual demonstration. Our method is "zero-shot" in the sense that the agent never has access to expert actions either during training or for task demonstration at inference.