  This paper explores a novel task ""Dexterous Grasp as You Say"" (DexGYS),
enabling robots to perform dexterous grasping based on human commands expressed
in natural language. However, the development of this field is hindered by the
lack of datasets with natural human guidance; thus, we propose a
language-guided dexterous grasp dataset, named DexGYSNet, offering high-quality
dexterous grasp annotations along with flexible and fine-grained human language
guidance. Our dataset construction is cost-efficient, with the carefully-design
hand-object interaction retargeting strategy, and the LLM-assisted language
guidance annotation system. Equipped with this dataset, we introduce the
DexGYSGrasp framework for generating dexterous grasps based on human language
instructions, with the capability of producing grasps that are intent-aligned,
high quality and diversity. To achieve this capability, our framework
decomposes the complex learning process into two manageable progressive
objectives and introduce two components to realize them. The first component
learns the grasp distribution focusing on intention alignment and generation
diversity. And the second component refines the grasp quality while maintaining
intention consistency. Extensive experiments are conducted on DexGYSNet and
real world environment for validation.
