蒸馏特征场实现少样本语言引导的操作
Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation
AI总结：
本文提出利用蒸馏特征场结合3D几何与2D语义先验，实现基于CLIP特征的少样本6自由度抓取与放置，支持通过自由文本指定新物体并泛化至未见表达与类别。
摘要：
自监督和语言监督的图像模型包含对泛化至关重要的丰富世界知识。然而，许多机器人任务需要对3D几何的精细理解，而这在2D图像特征中通常缺失。本工作通过利用蒸馏特征场，将来自2D基础模型的精确3D几何与丰富语义相结合，从而弥合机器人操作中的2D到3D鸿沟。我们提出了一种针对6自由度抓取与放置的少样本学习方法，利用这些强大的空间与语义先验，实现了对未见物体的真实场景泛化。使用从视觉-语言模型CLIP中蒸馏出的特征，我们提出了一种通过自由文本自然语言指定待操作新物体的方法，并展示了其泛化至未见表达与物体新类别的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
