Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation
Authors: name: William Shen, name: Ge Yang, name: Alan Yu, name: Jansen Wong, name: Leslie Pack Kaelbling, name: Phillip Isola

Self-supervised and language-supervised image models contain rich knowledge
of the world that is important for generalization. Many robotic tasks, however,
require a detailed understanding of 3D geometry, which is often lacking in 2D
image features. This work bridges this 2D-to-3D gap for robotic manipulation by
leveraging distilled feature fields to combine accurate 3D geometry with rich
semantics from 2D foundation models. We present a few-shot learning method for
6-DOF grasping and placing that harnesses these strong spatial and semantic
priors to achieve in-the-wild generalization to unseen objects. Using features
distilled from a vision-language model, CLIP, we present a way to designate
novel objects for manipulation via free-text natural language, and demonstrate
its ability to generalize to unseen expressions and novel categories of
objects.