  Robot grasping is an actively studied area in robotics, mainly focusing on
the quality of generated grasps for object manipulation. However, despite
advancements, these methods do not consider the human-robot collaboration
settings where robots and humans will have to grasp the same objects
concurrently. Therefore, generating robot grasps compatible with human
preferences of simultaneously holding an object becomes necessary to ensure a
safe and natural collaboration experience. In this paper, we propose a novel,
deep neural network-based method called CoGrasp that generates human-aware
robot grasps by contextualizing human preference models of object grasping into
the robot grasp selection process. We validate our approach against existing
state-of-the-art robot grasping methods through simulated and real-robot
experiments and user studies. In real robot experiments, our method achieves
about 88\% success rate in producing stable grasps that also allow humans to
interact and grasp objects simultaneously in a socially compliant manner.
Furthermore, our user study with 10 independent participants indicated our
approach enables a safe, natural, and socially-aware human-robot objects'
co-grasping experience compared to a standard robot grasping technique.
