CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration

Abhinav K Keshari, Hanwen Ren, Ahmed H Qureshi

Abstract:

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 humanaware robot grasps by contextualizing human preference models of object grasping into the robot grasp selection process. We validate our approach against existing state-of-theart 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 humanrobot objects’ co-grasping experience compared to a standard
robot grasping technique.