A robot operating in a household environment will see a wide range of unique and unfamiliar objects. While a system could train on many of these, it is infeasible to predict all the objects a robot will see. In this paper, we present a method to generalize object manipulation skills acquired from a limited number of demonstrations, to novel objects from unseen shape categories. Our approach, Local Neural Descriptor Fields (L-NDF), utilizes neural descriptors defined on the local geometry of the object to effectively transfer manipulation demonstrations to novel objects at test time. In doing so, we leverage the local geometry shared between objects to produce a more general manipulation framework. We illustrate the efficacy of our approach in manipulating novel objects in novel poses -- both in simulation and in the real world.

翻译：在家庭环境中运行的机器人将遇到各种独特且不熟悉的物体。虽然系统可以针对其中许多物体进行训练，但预测机器人将遇到的所有物体是不现实的。本文提出一种方法，将从有限演示中获取的物体操作技能泛化到来自未见形状类别的新颖物体。我们的方法——局部神经描述符场（L-NDF）——利用定义在物体局部几何结构上的神经描述符，在测试时有效地将操作演示迁移到新物体上。通过这种方式，我们利用物体之间共享的局部几何特征，构建出更具通用性的操作框架。我们在模拟环境和真实世界中，通过操作不同姿态下的新物体，展示了该方法的有效性。