Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | |
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| _version_ | 1866916976998219776 |
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| author | Wichitwechkarn, Vijja Williams, Emlyn Fox, Charles Choudhary, Ruchi |
| author_facet | Wichitwechkarn, Vijja Williams, Emlyn Fox, Charles Choudhary, Ruchi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24972 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks Wichitwechkarn, Vijja Williams, Emlyn Fox, Charles Choudhary, Ruchi Robotics 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. |
| title | Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.24972 |