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/ .

本文提出了一种新颖的方法MAGIC（可推广智能接触的操控类比），旨在一次性学习操控策略并快速广泛地适应新物体。通过参考动作轨迹，MAGIC能够高效识别新物体上的相似接触点和动作序列，从而复制展示的策略，显著提高了与不同物体类别的运行速度和适应性。

通过建立接触类比进行一次性操控策略学习