  We propose a novel approach to multi-fingered grasp planning leveraging
learned deep neural network models. We train a voxel-based 3D convolutional
neural network to predict grasp success probability as a function of both
visual information of an object and grasp configuration. We can then formulate
grasp planning as inferring the grasp configuration which maximizes the
probability of grasp success. In addition, we learn a prior over grasp
configurations as a mixture density network conditioned on our voxel-based
object representation.
  We show that this object conditional prior improves grasp inference when used
with the learned grasp success prediction network when compared to a learned,
object-agnostic prior, or an uninformed uniform prior. Our work is the first to
directly plan high quality multi-fingered grasps in configuration space using a
deep neural network without the need of an external planner. We validate our
inference method performing multi-finger grasping on a physical robot. Our
experimental results show that our planning method outperforms existing grasp
planning methods for neural networks.
