  We propose a novel approach to multi-fingered grasp planning leveraging
learned deep neural network models. We train a convolutional neural network to
predict grasp success 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. We
efficiently perform this inference using a gradient-ascent optimization inside
the neural network using the backpropagation algorithm. Our work is the first
to directly plan high quality multifingered grasps in configuration space using
a deep neural network without the need of an external planner. We validate our
inference method performing both multifinger and two-finger grasps on real
robots. Our experimental results show that our planning method outperforms
existing planning methods for neural networks; while offering several other
benefits including being data-efficient in learning and fast enough to be
deployed in real robotic applications.
