GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917946951991296 |
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| author | Zhong, Tao Allen-Blanchette, Christine |
| author_facet | Zhong, Tao Allen-Blanchette, Christine |
| contents | We propose GAGrasp, a novel framework for dexterous grasp generation that leverages geometric algebra representations to enforce equivariance to SE(3) transformations. By encoding the SE(3) symmetry constraint directly into the architecture, our method improves data and parameter efficiency while enabling robust grasp generation across diverse object poses. Additionally, we incorporate a differentiable physics-informed refinement layer, which ensures that generated grasps are physically plausible and stable. Extensive experiments demonstrate the model's superior performance in generalization, stability, and adaptability compared to existing methods. Additional details at https://gagrasp.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04123 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping Zhong, Tao Allen-Blanchette, Christine Robotics Computer Vision and Pattern Recognition Machine Learning We propose GAGrasp, a novel framework for dexterous grasp generation that leverages geometric algebra representations to enforce equivariance to SE(3) transformations. By encoding the SE(3) symmetry constraint directly into the architecture, our method improves data and parameter efficiency while enabling robust grasp generation across diverse object poses. Additionally, we incorporate a differentiable physics-informed refinement layer, which ensures that generated grasps are physically plausible and stable. Extensive experiments demonstrate the model's superior performance in generalization, stability, and adaptability compared to existing methods. Additional details at https://gagrasp.github.io/ |
| title | GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping |
| topic | Robotics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.04123 |