用变分形状推断提升抓取扩散模型的鲁棒性与多样性
Variational Shape Inference for Grasp Diffusion on SE(3)
- 通过隐式神经表示训练变分自编码器，从稀疏点云中推断形状特征
- 在SE(3)流形上生成多模态抓取，比现有方法高6.3%成功率
- 测试时可插件式优化，零样本迁移到真实家居物体抓取
抓取合成是机器人操作中的基础任务，通常存在多个可行解。多模态抓取合成旨在根据物体几何结构生成多样且稳定的抓取方案，因此对几何特征的鲁棒学习至关重要。为此，我们提出一种基于变分形状推断的多模态抓取分布学习框架，以增强对形状噪声和测量稀疏性的鲁棒性。该方法首先使用隐式神经表示训练一个变分自编码器进行形状推断，随后利用这些学习到的几何特征引导在SE(3)流形上的抓取扩散模型。此外，我们引入一种测试时抓取优化技术，可作为插件集成以进一步提升抓取性能。实验结果表明，我们的方法在ACRONYM数据集上比当前最优方法高出6.3%的成功率，且在点云密度下降时表现更稳健。此外，所训练模型实现零样本迁移至真实世界家居物体操作，在存在测量噪声和点云校准误差的情况下，成功抓取率比基线高出34%。
原文摘要 · Abstract (English)
Grasp synthesis is a fundamental task in robotic manipulation which usually has multiple feasible solutions. Multimodal grasp synthesis seeks to generate diverse sets of stable grasps conditioned on object geometry, making the robust learning of geometric features crucial for success. To address this challenge, we propose a framework for learning multimodal grasp distributions that leverages variational shape inference to enhance robustness against shape noise and measurement sparsity. Our approach first trains a variational autoencoder for shape inference using implicit neural representations, and then uses these learned geometric features to guide a diffusion model for grasp synthesis on the SE(3) manifold. Additionally, we introduce a test-time grasp optimization technique that can be integrated as a plugin to further enhance grasping performance. Experimental results demonstrate that our shape inference for grasp synthesis formulation outperforms state-of-the-art multimodal grasp synthesis methods on the ACRONYM dataset by 6.3%, while demonstrating robustness to deterioration in point cloud density compared to other approaches. Furthermore, our trained model achieves zero-shot transfer to real-world manipulation of household objects, generating 34% more successful grasps than baselines despite measurement noise and point cloud calibration errors.
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