  Precise robotic grasping of several novel objects is a huge challenge in
manufacturing, automation, and logistics. Most of the current methods for
model-free grasping are disadvantaged by the sparse data in grasping datasets
and by errors in sensor data and contact models. This study combines data
generation and sim-to-real transfer learning in a grasping framework that
reduces the sim-to-real gap and enables precise and reliable model-free
grasping. A large-scale robotic grasping dataset with dense grasp labels is
generated using domain randomization methods and a novel data augmentation
method for deep learning-based robotic grasping to solve data sparse problem.
We present an end-to-end robotic grasping network with a grasp optimizer. The
grasp policies are trained with sim-to-real transfer learning. The presented
results suggest that our grasping framework reduces the uncertainties in
grasping datasets, sensor data, and contact models. In physical robotic
experiments, our grasping framework grasped single known objects and novel
complex-shaped household objects with a success rate of 90.91%. In a complex
scenario with multi-objects robotic grasping, the success rate was 85.71%. The
proposed grasping framework outperformed two state-of-the-art methods in both
known and unknown object robotic grasping.
