Computer Science > Computer Vision and Pattern Recognition
arXiv:2404.03590 (cs)
[Submitted on 4 Apr 2024]
Title:SemGrasp: Semantic Grasp Generation via Language Aligned Discretization
View a PDF of the paper titled SemGrasp: Semantic Grasp Generation via Language Aligned Discretization, by Kailin Li and 4 other authors
View PDFHTML (experimental)Abstract: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:this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2404.03590 [cs.CV] |
| (orarXiv:2404.03590v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2404.03590 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled SemGrasp: Semantic Grasp Generation via Language Aligned Discretization, by Kailin Li and 4 other authors
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