Imitation learning of robot policies from few demonstrations is crucial in open-ended applications. We propose a new method, Interaction Warping, for learning SE(3) robotic manipulation policies from a single demonstration. We infer the 3D mesh of each object in the environment using shape warping, a technique for aligning point clouds across object instances. Then, we represent manipulation actions as keypoints on objects, which can be warped with the shape of the object. We show successful one-shot imitation learning on three simulated and real-world object re-arrangement tasks. We also demonstrate the ability of our method to predict object meshes and robot grasps in the wild.

翻译：从少量演示中学习机器人策略的模仿学习是开放式应用中的关键问题。我们提出了一种名为交互形变的新方法，用于从单个演示中学习SE(3)机器人操作策略。我们利用形状形变技术（一种跨物体实例对齐点云的方法）推断环境中每个物体的三维网格。随后，我们将操作动作表示为物体上的关键点，这些关键点可随物体形状发生形变。我们在三个模拟及真实物体重排任务中验证了成功的单样本模仿学习，并展示了该方法在未知环境中预测物体网格与机器人抓取的能力。