  While there exists many methods for manipulating rigid objects with
parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite
unexplored research topic. Reasoning and planning collision-free trajectories
on the additional degrees of freedom of several fingers represents an important
challenge that, so far, involves computationally costly and slow processes. In
this work, we present Multi-FinGAN, a fast generative multi-finger grasp
sampling method that synthesizes high quality grasps directly from RGB-D images
in about a second. We achieve this by training in an end-to-end fashion a
coarse-to-fine model composed of a classification network that distinguishes
grasp types according to a specific taxonomy and a refinement network that
produces refined grasp poses and joint angles. We experimentally validate and
benchmark our method against a standard grasp-sampling method on 790 grasps in
simulation and 20 grasps on a real Franka Emika Panda. All experimental results
using our method show consistent improvements both in terms of grasp quality
metrics and grasp success rate. Remarkably, our approach is up to 20-30 times
faster than the baseline, a significant improvement that opens the door to
feedback-based grasp re-planning and task informative grasping. Code is
available at https://irobotics.aalto.fi/multi-fingan/.
