Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks

Abstract-- Recent advances in one-shot imitation learning have enabled robots to acquire new manipulation skills from a single human demonstration. While existing methods achieve strong performance on single-step tasks, they remain limited in their ability to handle long-horizon, multi-step tasks without additional model training or manual annotation. We propose a method that can be applied to this setting provided a single demonstration without additional model training or manual annotation. We evaluated our method on multi-step and single-step manipulation tasks where our method achieves an average success rate of 82.5% and 90%, respectively. Our method matches and exceeds the performance of the baselines in both these cases. We also compare the performance and computational efficiency of alternative pre-trained feature extractors within our framework. I. INTRODUCTION Recent advances in imitation learning have enabled robots to perform increasingly complex tasks. However, these methods still require hundreds to thousands of demonstrations per task [1], [2], [3], [4], making them impractical for real-world deployment.