Few-Shot Imitation

Learning a new manipulation task from a very small number of demonstrations (typically 1–10), enabled by strong pre-trained representations or meta-learned task embeddings. Few-shot imitation is the practical goal for deploying robots in varied real-world settings where collecting hundreds of demos per task is infeasible. Methods include task-conditioned policies, prompt-based VLAs, and retrieval-augmented approaches.