Diversity Search for the Generation of Diverse Grasping Trajectories

Robotic grasping refers to making a robotic system pick an object by applying forces and torques on its surface. Despite the recent advances in data-driven approaches, grasping still needs to be solved. In this work, we consider grasping as a Diversity Search problem, where we attempt to find as many solutions as possible that verify a sparse binary criterion. We propose a variant of a state-of-the-art QD method for grasping based on a divide-and-conquer paradigm to handle grasping discontinuities. Experiments conducted on 3 different robot-gripper setups and several standard objects show that this variant outperforms state-of-the-art for generating diverse repertoires of grasping trajectories.