Text2Grasp: Grasp synthesis by text prompts of object grasping parts

• URL: http://arxiv.org/abs/2404.15189v1
• Date: Tue, 9 Apr 2024 10:57:27 GMT
• Title: Text2Grasp: Grasp synthesis by text prompts of object grasping parts
• Authors: Xiaoyun Chang, Yi Sun,
• Abstract summary: The hand plays a pivotal role in human ability to grasp and manipulate objects. Existing methods that use human intention or task-level language as control signals for grasping inherently face ambiguity. We propose a grasp synthesis method guided by text prompts of object grasping parts, Text2Grasp, which provides more precise control.
• Score: 4.031699584957737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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