  Quality-Diversity (QD) methods are algorithms that aim to generate a set of
diverse and high-performing solutions to a given problem. Originally developed
for evolutionary robotics, most QD studies are conducted on a limited set of
domains - mainly applied to locomotion, where the fitness and the behavior
signal are dense. Grasping is a crucial task for manipulation in robotics.
Despite the efforts of many research communities, this task is yet to be
solved. Grasping cumulates unprecedented challenges in QD literature: it
suffers from reward sparsity, behavioral sparsity, and behavior space
misalignment. The present work studies how QD can address grasping. Experiments
have been conducted on 15 different methods on 10 grasping domains,
corresponding to 2 different robot-gripper setups and 5 standard objects. An
evaluation framework that distinguishes the evaluation of an algorithm from its
internal components has also been proposed for a fair comparison. The obtained
results show that MAP-Elites variants that select successful solutions in
priority outperform all the compared methods on the studied metrics by a large
margin. We also found experimental evidence that sparse interaction can lead to
deceptive novelty. To our knowledge, the ability to efficiently produce
examples of grasping trajectories demonstrated in this work has no precedent in
the literature.
