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.  In the second stage, an Angle-Width Predictor (AWP) refines the grasp angle and width using local features.  Contrastive learning in the AWP module enables zero-shot generalization to unseen grippers by learning fundamental grasping characteristics. The modular framework integrates seamlessly with vision foundation models, providing pathways for future vision-language capabilities. 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.

Overview of the XGrasp framework: a proposed two-stage Gripper-Aware Grasp Detection approach.

Real-World