  Grasp planning and most specifically the grasp space exploration is still an
open issue in robotics. This article presents a data-driven oriented
methodology to model the grasp space of a multi-fingered adaptive gripper for
known objects. This method relies on a limited dataset of manually specified
expert grasps, and uses variational autoencoder to learn grasp intrinsic
features in a compact way from a computational point of view. The learnt model
can then be used to generate new non-learnt gripper configurations to explore
the grasp space.
