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

Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search