  One goal of dexterous robotic grasping is to allow robots to handle objects
with the same level of flexibility and adaptability as humans. However, it
remains a challenging task to generate an optimal grasping strategy for
dexterous hands, especially when it comes to delicate manipulation and accurate
adjustment the desired grasping poses for objects of varying shapes and sizes.
In this paper, we propose a novel dexterous grasp generation scheme called
GrainGrasp that provides fine-grained contact guidance for each fingertip. In
particular, we employ a generative model to predict separate contact maps for
each fingertip on the object point cloud, effectively capturing the specifics
of finger-object interactions. In addition, we develop a new dexterous grasping
optimization algorithm that solely relies on the point cloud as input,
eliminating the necessity for complete mesh information of the object. By
leveraging the contact maps of different fingertips, the proposed optimization
algorithm can generate precise and determinable strategies for human-like
object grasping. Experimental results confirm the efficiency of the proposed
scheme.
