XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation
This addresses the need for versatile robotic grasping in real-world scenarios, though it appears incremental as it builds on existing gripper-aware methods with enhancements.
The authors tackled the problem of robotic grasping being limited to single gripper types by proposing XGrasp, a real-time gripper-aware framework that achieves competitive grasp success rates across various grippers and improves inference speed compared to existing methods.
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. Project page: https://sites.google.com/view/xgrasp