无需标注和训练，仅用一次示范就让机器人完成多步操作。
Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks
- 仅需一个演示，不需额外训练或标注即可学习复杂操作序列。
- 多步任务成功率82.5%，单步任务达90%，超越现有方法。
- 适用于需要快速部署新技能的工业机器人场景。
近期的一次性模仿学习进展使机器人能够从单一人类示范中习得新的操作技能。尽管现有方法在单步任务上表现优异，但在无需额外模型训练或人工标注的情况下，仍难以应对长时程、多步骤任务。本文提出一种新方法，在仅提供一次示范的前提下，无需额外训练或标注即可应用于此类场景。我们在多步与单步操作任务上评估该方法，平均成功率分别达到82.5%和90%，性能优于或匹配现有基线。同时，我们还比较了框架内不同预训练特征提取器的性能与计算效率。
原文摘要 · Abstract (English)
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.
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