ベイズ固有物体による言語属性の物体への接地
Grounding Language Attributes to Objects using Bayesian Eigenobjects
自然言語の物理的記述と深度画像から物体インスタンスを識別するシステムを開発し、少量の言語注釈データで未学習視点や新規物体にも対応、ロボットでの物体選択を実現した。
著者: Vanya Cohen, Benjamin Burchfiel, Thao Nguyen, Nakul Gopalan, Stefanie Tellex, George Konidaris
分類: cs.RO, cs.CL, cs.CV
原文アブストラクト
We develop a system to disambiguate object instances within the same class based on simple physical descriptions. The system takes as input a natural language phrase and a depth image containing a segmented object and predicts how similar the observed object is to the object described by the phrase. Our system is designed to learn from only a small amount of human-labeled language data and generalize to viewpoints not represented in the language-annotated depth image training set. By decoupling 3D shape representation from language representation, this method is able to ground language to novel objects using a small amount of language-annotated depth-data and a larger corpus of unlabeled 3D object meshes, even when these objects are partially observed from unusual viewpoints. Our system is able to disambiguate between novel objects, observed via depth images, based on natural language descriptions. Our method also enables view-point transfer; trained on human-annotated data on a small set of depth images captured from frontal viewpoints, our system successfully predicted object attributes from rear views despite having no such depth images in its training set. Finally, we demonstrate our approach on a Baxter robot, enabling it to pick specific objects based on human-provided natural language descriptions.
