  Robotic grasping refers to making a robotic system pick an object by applying
forces and torques on its surface. Many recent studies use data-driven
approaches to address grasping, but the sparse reward nature of this task made
the learning process challenging to bootstrap. To avoid constraining the
operational space, an increasing number of works propose grasping datasets to
learn from. But most of them are limited to simulations. The present paper
investigates how automatically generated grasps can be exploited in the real
world. More than 7000 reach-and-grasp trajectories have been generated with
Quality-Diversity (QD) methods on 3 different arms and grippers, including
parallel fingers and a dexterous hand, and tested in the real world. Conducted
analysis on the collected measure shows correlations between several Domain
Randomization-based quality criteria and sim-to-real transferability. Key
challenges regarding the reality gap for grasping have been identified,
stressing matters on which researchers on grasping should focus in the future.
A QD approach has finally been proposed for making grasps more robust to domain
randomization, resulting in a transfer ratio of 84% on the Franka Research 3
arm.
