  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.

Robotics;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning