We present a novel approach, MAGIC (manipulation analogies for generalizable intelligent contacts), for one-shot learning of manipulation strategies with fast and extensive generalization to novel objects. By leveraging a reference action trajectory, MAGIC effectively identifies similar contact points and sequences of actions on novel objects to replicate a demonstrated strategy, such as using different hooks to retrieve distant objects of different shapes and sizes. Our method is based on a two-stage contact-point matching process that combines global shape matching using pretrained neural features with local curvature analysis to ensure precise and physically plausible contact points. We experiment with three tasks including scooping, hanging, and hooking objects. MAGIC demonstrates superior performance over existing methods, achieving significant improvements in runtime speed and generalization to different object categories. Website: https://magic-2024.github.io/ .

翻译：我们提出了一种新颖方法MAGIC（用于泛化智能接触的操作类比），用于单次学习操作策略，并能够快速且广泛地泛化到新物体。通过利用参考动作轨迹，MAGIC能有效识别新物体上相似的接触点与动作序列，以复现已演示的策略，例如使用不同钩具抓取不同形状与尺寸的远处物体。本方法基于两阶段接触点匹配流程：首先结合预训练神经特征进行全局形状匹配，再通过局部曲率分析确保接触点的精确性与物理合理性。我们在包含舀取、悬挂和钩取物体的三项任务中进行了实验验证。MAGIC展现出优于现有方法的性能，在运行速度和对不同物体类别的泛化能力方面均取得显著提升。项目网站：https://magic-2024.github.io/。