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.
@article{arxiv.2509.24972,
title = {Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks},
author = {Vijja Wichitwechkarn and Emlyn Williams and Charles Fox and Ruchi Choudhary},
journal= {arXiv preprint arXiv:2509.24972},
year = {2025}
}