  In this paper, we present a transformer-based architecture, namely TF-Grasp,
for robotic grasp detection. The developed TF-Grasp framework has two elaborate
designs making it well suitable for visual grasping tasks. The first key design
is that we adopt the local window attention to capture local contextual
information and detailed features of graspable objects. Then, we apply the
cross window attention to model the long-term dependencies between distant
pixels. Object knowledge, environmental configuration, and relationships
between different visual entities are aggregated for subsequent grasp
detection. The second key design is that we build a hierarchical
encoder-decoder architecture with skip-connections, delivering shallow features
from encoder to decoder to enable a multi-scale feature fusion. Due to the
powerful attention mechanism, the TF-Grasp can simultaneously obtain the local
information (i.e., the contours of objects), and model long-term connections
such as the relationships between distinct visual concepts in clutter.
Extensive computational experiments demonstrate that the TF-Grasp achieves
superior results versus state-of-art grasping convolutional models and attain a
higher accuracy of 97.99% and 94.6% on Cornell and Jacquard grasping datasets,
respectively. Real-world experiments using a 7DoF Franka Emika Panda robot also
demonstrate its capability of grasping unseen objects in a variety of
scenarios. The code and pre-trained models will be available at
https://github.com/WangShaoSUN/grasp-transformer
