  In this paper, we are interested in the problem of generating target grasps
by understanding freehand sketches. The sketch is useful for the persons who
cannot formulate language and the cases where a textual description is not
available on the fly. However, very few works are aware of the usability of
this novel interactive way between humans and robots. To this end, we propose a
method to generate a potential grasp configuration relevant to the
sketch-depicted objects. Due to the inherent ambiguity of sketches with
abstract details, we take the advantage of the graph by incorporating the
structure of the sketch to enhance the representation ability. This
graph-represented sketch is further validated to improve the generalization of
the network, capable of learning the sketch-queried grasp detection by using a
small collection (around 100 samples) of hand-drawn sketches. Additionally, our
model is trained and tested in an end-to-end manner which is easy to be
implemented in real-world applications. Experiments on the multi-object VMRD
and GraspNet-1Billion datasets demonstrate the good generalization of the
proposed method. The physical robot experiments confirm the utility of our
method in object-cluttered scenes.
