  We present a robotic grasping system that uses a single external monocular
RGB camera as input. The object-to-robot pose is computed indirectly by
combining the output of two neural networks: one that estimates the
object-to-camera pose, and another that estimates the robot-to-camera pose.
Both networks are trained entirely on synthetic data, relying on domain
randomization to bridge the sim-to-real gap. Because the latter network
performs online camera calibration, the camera can be moved freely during
execution without affecting the quality of the grasp. Experimental results
analyze the effect of camera placement, image resolution, and pose refinement
in the context of grasping several household objects. We also present results
on a new set of 28 textured household toy grocery objects, which have been
selected to be accessible to other researchers. To aid reproducibility of the
research, we offer 3D scanned textured models, along with pre-trained weights
for pose estimation.
