  Robotic grasping is a fundamental skill required for object manipulation in
robotics. Multi-fingered robotic hands, which mimic the structure of the human
hand, can potentially perform complex object manipulation. Nevertheless,
current techniques for multi-fingered robotic grasping frequently predict only
a single grasp for each inference time, limiting computational efficiency and
their versatility, i.e. unimodal grasp distribution. This paper proposes a
differentiable multi-fingered grasp generation network (DMFC-GraspNet) with
three main contributions to address this challenge. Firstly, a novel neural
grasp planner is proposed, which predicts a new grasp representation to enable
versatile and dense grasp predictions. Secondly, a scene creation and label
mapping method is developed for dense labeling of multi-fingered robotic hands,
which allows a dense association of ground truth grasps. Thirdly, we propose to
train DMFC-GraspNet end-to-end using using a forward-backward automatic
differentiation approach with both a supervised loss and a differentiable
collision loss and a generalized Q 1 grasp metric loss. The proposed approach
is evaluated using the Shadow Dexterous Hand on Mujoco simulation and ablated
by different choices of loss functions. The results demonstrate the
effectiveness of the proposed approach in predicting versatile and dense
grasps, and in advancing the field of multi-fingered robotic grasping.
