  Generating natural human grasps necessitates consideration of not just object
geometry but also semantic information. Solely depending on object shape for
grasp generation confines the applications of prior methods in downstream
tasks. This paper presents a novel semantic-based grasp generation method,
termed SemGrasp, which generates a static human grasp pose by incorporating
semantic information into the grasp representation. We introduce a discrete
representation that aligns the grasp space with semantic space, enabling the
generation of grasp postures in accordance with language instructions. A
Multimodal Large Language Model (MLLM) is subsequently fine-tuned, integrating
object, grasp, and language within a unified semantic space. To facilitate the
training of SemGrasp, we have compiled a large-scale, grasp-text-aligned
dataset named CapGrasp, featuring about 260k detailed captions and 50k diverse
grasps. Experimental findings demonstrate that SemGrasp efficiently generates
natural human grasps in alignment with linguistic intentions. Our code, models,
and dataset are available publicly at: https://kailinli.github.io/SemGrasp.
