  Grasp detection in a cluttered environment is still a great challenge for
robots. Currently, the Transformer mechanism has been successfully applied to
visual tasks, and its excellent ability of global context information
extraction provides a feasible way to improve the performance of robotic grasp
detection in cluttered scenes. However, the insufficient inductive bias ability
of the original Transformer model requires large-scale datasets training, which
is difficult to obtain for grasp detection. In this paper, we propose a grasp
detection model based on encoder-decoder structure. The encoder uses a
Transformer network to extract global context information. The decoder uses a
fully convolutional neural network to improve the inductive bias capability of
the model and combine features extracted by the encoder to predict the final
grasp configuration. Experiments on the VMRD dataset demonstrate that our model
performs much better in overlapping object scenes. Meanwhile, on the Cornell
Grasp dataset, our approach achieves an accuracy of 98.1%, which is comparable
with state-of-the-art algorithms.
