  Grasping a particular object may require a dedicated grasping movement that
may also be specific to the robot end-effector. No generic and autonomous
method does exist to generate these movements without making hypotheses on the
robot or on the object. Learning methods could help to autonomously discover
relevant grasping movements, but they face an important issue: grasping
movements are so rare that a learning method based on exploration has little
chance to ever observe an interesting movement, thus creating a bootstrap
issue. We introduce an approach to generate diverse grasping movements in order
to solve this problem. The movements are generated in simulation, for
particular object positions. We test it on several simulated robots: Baxter,
Pepper and a Kuka Iiwa arm. Although we show that generated movements actually
work on a real Baxter robot, the aim is to use this method to create a large
dataset to bootstrap deep learning methods.
