{"ID":2863735,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24972","arxiv_id":"2509.24972","title":"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.","short_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...","url_abs":"https://arxiv.org/abs/2509.24972","url_pdf":"https://arxiv.org/pdf/2509.24972v1","authors":"[\"Vijja Wichitwechkarn\",\"Emlyn Williams\",\"Charles Fox\",\"Ruchi Choudhary\"]","published":"2025-09-29T16:02:34Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}