CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning

Current dexterous grasp planners primarily optimize for phys-ical stability, focusing on whether an object can be grasped rather thanhow it should be grasped to support downstream functional tasks. How-ever, conditioning grasp synthesis on specific human grasp taxonomiestypically requires prohibitively expensive, object-annotated datasets. Toaddress these limitations, we propose CoToGrasp, a novel generativeframework that synthesizes diverse, stable grasps strictly conditioned onspecific contact topologies. To bypass the data collection bottleneck, Co-ToGrasp is trained entirely in an object-agnostic manner. We introducea feature-based canonical workspace that projects local object featuresinto a unified gripper-centric domain, effectively decoupling the semanticfunctional intent from the arbitrary object geometry. By learning the in-trinsic contact manifold of the gripper within this workspace, our modelachieves zero-shot generalization to unseen objects at inference. Exten-sive evaluations on the large-scale DexGraspNet dataset demonstratethat CoToGrasp achieves state-of-the-art performance, outperformingexisting taxonomy-guided planners. Finally, we demonstrate the physicalviability and kinematic feasibility of our synthesized contact topologieson a physical robot platform. Code is available on our project websitehttps://cea-list.github.io/cotograspweb/.