  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.
