CoToGrasp：基于典型工作空间学习的接触拓扑条件化灵巧抓取合成

摘要

当前的灵巧抓取规划器主要针对物理稳定性进行优化，关注物体能否被抓取，而非如何抓取以支持下游功能性任务。然而，以特定人类抓取分类法为条件来合成抓取，通常需要极其昂贵且带有物体标注的数据集。为解决这些局限，我们提出了CoToGrasp，一种新颖的生成式框架，能够严格以特定接触拓扑为条件，合成多样且稳定的抓取。为绕过数据收集瓶颈，CoToGrasp完全以物体无关的方式进行训练。我们引入了一种基于特征的规范工作空间，将局部物体特征投影到统一的以夹持器为中心的空间中，从而有效地将语义功能意图与任意物体几何形状解耦。通过在该工作空间中学习夹持器的内在接触流形，我们的模型在推理时实现了对未见物体的零样本泛化。在大规模DexGraspNet数据集上的广泛评估表明，CoToGrasp取得了最先进的性能，优于现有的分类引导规划器。最后，我们在物理机器人平台上验证了所合成接触拓扑的物理可行性与运动学可行性。代码可在项目网站 https://cea-list.github.io/cotograspweb/ 获取。

English

Current dexterous grasp planners primarily optimize for physical stability, focusing on whether an object can be grasped rather than how it should be grasped to support downstream functional tasks. However, conditioning grasp synthesis on specific human grasp taxonomies typically requires prohibitively expensive, object-annotated datasets. To address these limitations, we propose CoToGrasp, a novel generative framework that synthesizes diverse, stable grasps strictly conditioned on specific contact topologies. To bypass the data collection bottleneck, CoToGrasp is trained entirely in an object-agnostic manner. We introduce a feature-based canonical workspace that projects local object features into a unified gripper-centric domain, effectively decoupling the semantic functional intent from the arbitrary object geometry. By learning the intrinsic contact manifold of the gripper within this workspace, our model achieves zero-shot generalization to unseen objects at inference. Extensive evaluations on the large-scale DexGraspNet dataset demonstrate that CoToGrasp achieves state-of-the-art performance, outperforming existing taxonomy-guided planners. Finally, we demonstrate the physical viability and kinematic feasibility of our synthesized contact topologies on a physical robot platform. Code is available on our project website https://cea-list.github.io/cotograspweb/ .