自然言語コマンドに条件付けられた把持検出のための統合ネットワーク
A Joint Network for Grasp Detection Conditioned on Natural Language Commands
RGB画像と言語コマンドから直接、コマンドを満たす把持を出力するCGNetを提案し、物体検索と把持検出を別々に行う従来手法より高精度であることを示した。
著者: Yiye Chen, Ruinian Xu, Yunzhi Lin, Patricio A. Vela
分類: cs.CV, cs.RO
原文アブストラクト
We consider the task of grasping a target object based on a natural language command query. Previous work primarily focused on localizing the object given the query, which requires a separate grasp detection module to grasp it. The cascaded application of two pipelines incurs errors in overlapping multi-object cases due to ambiguity in the individual outputs. This work proposes a model named Command Grasping Network(CGNet) to directly output command satisficing grasps from RGB image and textual command inputs. A dataset with ground truth (image, command, grasps) tuple is generated based on the VMRD dataset to train the proposed network. Experimental results on the generated test set show that CGNet outperforms a cascaded object-retrieval and grasp detection baseline by a large margin. Three physical experiments demonstrate the functionality and performance of CGNet.
