Text2Grasp: Grasp synthesis by text prompts of object grasping parts
<|reference_start|>Text2Grasp: Grasp synthesis by text prompts of object grasping parts: The hand plays a pivotal role in human ability to grasp and manipulate objects and controllable grasp synthesis is the key for successfully performing downstream tasks. Existing methods that use human intention or task-level language as control signals for grasping inherently face ambiguity. To address this challenge, we propose a grasp synthesis method guided by text prompts of object grasping parts, Text2Grasp, which provides more precise control. Specifically, we present a two-stage method that includes a text-guided diffusion model TextGraspDiff to first generate a coarse grasp pose, then apply a hand-object contact optimization process to ensure both plausibility and diversity. Furthermore, by leveraging Large Language Model, our method facilitates grasp synthesis guided by task-level and personalized text descriptions without additional manual annotations. Extensive experiments demonstrate that our method achieves not only accurate part-level grasp control but also comparable performance in grasp quality.<|reference_end|>
@article{chang2024text2grasp:,
    title={Text2Grasp: Grasp synthesis by text prompts of object grasping parts},
    author={Xiaoyun Chang and Yi Sun},
    journal={arXiv preprint arXiv:2404.15189},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15189},
    primaryClass={cs.AI}
}