CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning
CoToGrasp is a generative framework that synthesizes diverse, stable grasps conditioned strictly on desired contact topologies, decoupling functional intent from object geometry. It works by training an object-agnostic model in a canonical workspace and using human grasp taxonomies to guide the synthesis process. This allows for state-of-the-art performance on unseen objects without needing specific object annotations.