CoToGrasp：基於典型工作空間學習的接觸拓撲條件化靈巧抓取合成

摘要

當前的靈巧抓取規劃器主要優化物理穩定性，專注於物體能否被抓取，而非如何抓取以支持下游功能任務。然而，基於特定人類抓取分類法來條件化抓取合成，通常需要成本高昂且帶有物體標註的數據集。為了解決這些限制，我們提出 CoToGrasp，這是一個新穎的生成式框架，能夠嚴格基於特定接觸拓撲合成多樣且穩定的抓取。為了繞過數據收集瓶頸，CoToGrasp 完全以與物體無關的方式進行訓練。我們引入了一個基於特徵的規範工作空間，將局部物體特徵投影到統一的以夾爪為中心的域中，有效地將語義功能意圖與任意物體幾何形狀解耦。通過在該工作空間中學習夾爪的內在接觸流形，我們的模型在推理時實現了對未見過物體的零樣本泛化。在大規模 DexGraspNet 數據集上的廣泛評估表明，CoToGrasp 達到了最先進的性能，優於現有的分類法引導規劃器。最後，我們在實體機器人平台上展示了所合成接觸拓撲的物理可行性和運動學可行性。代碼可在我們的項目網站 https://cea-list.github.io/cotograspweb/ 上獲取。

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/ .