Bin Picking of Reflective Steel Parts using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment

We consider the case of robotic bin picking of
reflective steel parts, using a structured light 3D camera as a
depth imaging device. In this paper, we present a new method
for bin picking, based on a dual-resolution convolutional neural
network trained entirely in a simulated environment. The dualresolution
network consists of a high resolution focus network
to compute the grasp and a low resolution context network to
avoid local collisions.The reflectivity of the steel parts result
in depth images that have a lot of missing data. To take this
into account, training of the neural net is done by domain
randomization on a large set of synthetic depth images that
simulate the missing data problems of the real depth images.
We demonstrate both in simulation and in a real-world test that
our method can perform bin picking of reflective steel parts