  Grasping is among the most fundamental and long-lasting problems in robotics
study. This paper studies the problem of 6-DoF(degree of freedom) grasping by a
parallel gripper in a cluttered scene captured using a commodity depth sensor
from a single viewpoint. We address the problem in a learning-based framework.
At the high level, we rely on a single-shot grasp proposal network, trained
with synthetic data and tested in real-world scenarios. Our single-shot neural
network architecture can predict amodal grasp proposal efficiently and
effectively. Our training data synthesis pipeline can generate scenes of
complex object configuration and leverage an innovative gripper contact model
to create dense and high-quality grasp annotations. Experiments in synthetic
and real environments have demonstrated that the proposed approach can
outperform state-of-the-arts by a large margin.
