# Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation Source: [Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:10:33.419Z Authors: William Shen, Ge Yang, Alan Yu, Jansen Wong, Leslie Pack Kaelbling, Phillip Isola Published: 2023-07-27T17:59:14.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Context-aware Robotic Grasping on Real-world context-aware grasping scenes](): Context-aware Robotic Grasping; Real-world context-aware grasping scenes; Object Success: 75. ## Original sources and implementation [Original paper]() [PDF]()