  Robotic grasping aims to detect graspable points and their corresponding
gripper configurations in a particular scene, and is fundamental for robot
manipulation. Existing research works have demonstrated the potential of using
a transformer model for robotic grasping, which can efficiently learn both
global and local features. However, such methods are still limited in grasp
detection on a 2D plane. In this paper, we extend a transformer model for
6-Degree-of-Freedom (6-DoF) robotic grasping, which makes it more flexible and
suitable for tasks that concern safety. The key designs of our method are a
serialization module that turns a 3D voxelized space into a sequence of feature
tokens that a transformer model can consume and skip-connections that merge
multiscale features effectively. In particular, our method takes a Truncated
Signed Distance Function (TSDF) as input. After serializing the TSDF, a
transformer model is utilized to encode the sequence, which can obtain a set of
aggregated hidden feature vectors through multi-head attention. We then decode
the hidden features to obtain per-voxel feature vectors through deconvolution
and skip-connections. Voxel feature vectors are then used to regress parameters
for executing grasping actions. On a recently proposed pile and packed grasping
dataset, we showcase that our transformer-based method can surpass existing
methods by about 5% in terms of success rates and declutter rates. We further
evaluate the running time and generalization ability to demonstrate the
superiority of the proposed method.
