  Generating dexterous grasping has been a long-standing and challenging
robotic task. Despite recent progress, existing methods primarily suffer from
two issues. First, most prior arts focus on a specific type of robot hand,
lacking the generalizable capability of handling unseen ones. Second, prior
arts oftentimes fail to rapidly generate diverse grasps with a high success
rate. To jointly tackle these challenges with a unified solution, we propose
GenDexGrasp, a novel hand-agnostic grasping algorithm for generalizable
grasping. GenDexGrasp is trained on our proposed large-scale multi-hand
grasping dataset MultiDex synthesized with force closure optimization. By
leveraging the contact map as a hand-agnostic intermediate representation,
GenDexGrasp efficiently generates diverse and plausible grasping poses with a
high success rate and can transfer among diverse multi-fingered robotic hands.
Compared with previous methods, GenDexGrasp achieves a three-way trade-off
among success rate, inference speed, and diversity. Code is available at
https://github.com/tengyu-liu/GenDexGrasp.
