  The versatility and adaptability of human grasping catalyze advancing
dexterous robotic manipulation. While significant strides have been made in
dexterous grasp generation, current research endeavors pivot towards optimizing
object manipulation while ensuring functional integrity, emphasizing the
synthesis of functional grasps following desired affordance instructions. This
paper addresses the challenge of synthesizing functional grasps tailored to
diverse dexterous robotic hands by proposing DexGrasp-Diffusion, an end-to-end
modularized diffusion-based pipeline. DexGrasp-Diffusion integrates
MultiHandDiffuser, a novel unified data-driven diffusion model for
multi-dexterous hands grasp estimation, with DexDiscriminator, which employs a
Physics Discriminator and a Functional Discriminator with open-vocabulary
setting to filter physically plausible functional grasps based on object
affordances. The experimental evaluation conducted on the MultiDex dataset
provides substantiating evidence supporting the superior performance of
MultiHandDiffuser over the baseline model in terms of success rate, grasp
diversity, and collision depth. Moreover, we demonstrate the capacity of
DexGrasp-Diffusion to reliably generate functional grasps for household objects
aligned with specific affordance instructions.
