  In this paper we study grasp problem in dense cluster, a challenging task in
warehouse logistics scenario. By introducing a two-step robust suction
affordance detection method, we focus on using vacuum suction pad to clear up a
box filled with seen and unseen objects. Two CNN based neural networks are
proposed. A Fast Region Estimation Network (FRE-Net) predicts which region
contains pickable objects, and a Suction Grasp Point Affordance network
(SGPA-Net) determines which point in that region is pickable. So as to enable
such two networks, we design a self-supervised learning pipeline to accumulate
data, train and test the performance of our method. In both virtual and real
environment, within 1500 picks (~5 hours), we reach a picking accuracy of 95%
for known objects and 90% for unseen objects with similar geometry features.
