  Grasp planning and most specifically the grasp space exploration is still an
open issue in robotics. This article presents an efficient procedure for
exploring the grasp space of a multifingered adaptive gripper for generating
reliable grasps given a known object pose. This procedure relies on a limited
dataset of manually specified expert grasps, and use a mixed analytic and
data-driven approach based on the use of a grasp quality metric and variational
autoencoders. The performances of this method are assessed by generating grasps
in simulation for three different objects. On this grasp planning task, this
method reaches a grasp success rate of 99.91% on 7000 trials.

Robotics