用几何代数提升灵巧抓取生成的泛化与稳定性
GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping
- 将SE(3)对称性直接编码到网络架构中，增强鲁棒性
- 生成抓取在多种物体姿态下均稳定，且物理上合理
- 适合需要高精度灵巧操作的机器人场景
我们提出GAGrasp，一种基于几何代数表示的灵巧抓取生成新框架，通过将SE(3)变换等变性约束直接嵌入网络结构，提升数据与参数效率，并实现对多样化物体姿态的鲁棒抓取生成。此外，引入可微分的物理启发式优化层，确保生成抓取在物理上可行且稳定。大量实验表明，相比现有方法，该模型在泛化能力、稳定性与适应性方面表现更优。更多信息见 https://gagrasp.github.io/
原文摘要 · Abstract (English)
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/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。