RAGrasp: A Retrieval-Augmented Framework with Diversity-Aware Modeling for Dexterous Grasp Generation

Dexterous grasping is a fundamental ability for robots toperform complex manipulation tasks. However, simultaneously maximiz-ing grasp success rates and maintaining grasp-type diversity remains asignificant challenge. In response, we introduce RAGrasp, a retrieval-augmented framework that comprises two core components: a Geometry-Aware hand pose Initialization strategy(GAI) and a Diversity-Awaregrasp-type Selection network(DAS). First, GAI constructs a distribution-aligned retrieval dataset and leverages object geometric similarity toretrieve high-quality initial samples, significantly improving grasp suc-cess rates. Second, DAS adopts a retrieval-augmented dual-head classi-fier that formulates grasp-type selection as a collaborative optimizationproblem of single-label and multi-label grasp-type classification, improv-ing diversity and allowing flexible diversity adjustment. Extensive experi-ments show that RAGrasp yields an 8.2% relative improvement in successrate on BODex, alongside average relative gains of 13.0% and 11.9% onDexonomy under type-unconditioned and type-conditioned settings.