XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation

Abstract-- Most robotic grasping methods are typically designed for single gripper types, which limits their applicability in real-world scenarios requiring diverse end-effectors. We propose XGrasp, a real-time gripper-aware grasp detection framework that efficiently handles multiple gripper configurations. The proposed method addresses data scarcity by systematically augmenting existing datasets with multi-gripper annotations. XGrasp employs a hierarchical two-stage architecture. In the first stage, a Grasp Point Predictor (GPP) identifies optimal locations using global scene information and gripper specifications. Contrastive learning in the A WP module enables zero-shot generalization to unseen grippers by learning fundamental grasping characteristics. The experimental results demonstrate competitive grasp success rates across various gripper types, while achieving substantial improvements in inference speed compared to existing gripper-aware methods. I. INTRODUCTION Robot grasping represents a fundamental capability in autonomous manipulation systems, enabling robots to interact with objects in diverse environments.