XGrasp无需重新训练，即可通用识别多种夹爪的抓取姿态。
XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation
- 用夹爪形状和开合轨迹生成多夹爪数据，解决数据稀缺问题。
- 在多种夹爪上抓取成功率超现有方法，推理速度更快。
- 适合需快速适配新夹爪的工业机器人场景。
真实世界机器人系统常需适配多种末端执行器，但现有抓取检测方法多针对单一夹爪优化，每换一种夹爪都需重新训练或调优，既不具可扩展性也不实用。本文提出XGrasp，一种实时夹爪感知抓取检测框架，可在不额外训练的情况下泛化至新型夹爪配置。为缓解数据稀缺，我们通过引入多样夹爪的物理特性与开合轨迹，对现有单夹爪数据集进行多夹爪标注扩充。每个夹爪以双通道2D图像表示：静态形状（夹爪掩码）和动态开合轨迹（夹爪路径）。XGrasp采用分层两阶段架构，包含抓取点预测器（GPP）和角度-宽度预测器（AWP）。在AWP中，利用质量感知锚点的对比学习构建夹爪无关嵌入空间，实现对新夹爪的零样本泛化。实验表明，XGrasp在多种夹爪类型下均优于现有夹爪感知方法，在抓取成功率和推理速度上表现更优。
原文摘要 · Abstract (English)
Real-world robotic systems frequently require diverse end-effectors for different tasks, however most existing grasp detection methods are optimized for a single gripper type, demanding retraining or optimization for each novel gripper configuration. This gripper-specific retraining paradigm is neither scalable nor practical. We propose XGrasp, a real-time gripper-aware grasp detection framework that generalizes to novel gripper configurations without additional training or optimization. To resolve data scarcity, we augment existing single-gripper datasets with multi-gripper annotations by incorporating the physical characteristics and closing trajectories of diverse grippers. Each gripper is represented as a two-channel 2D image encoding its static shape (Gripper Mask) and dynamic closing trajectory (Gripper Path). XGrasp employs a hierarchical two-stage architecture consisting of a Grasp Point Predictor (GPP) and an Angle-Width Predictor (AWP). In the AWP, contrastive learning with a quality-aware anchor builds a gripper-agnostic embedding space, enabling generalization to novel grippers without additional training. Experimental results demonstrate that XGrasp outperforms existing gripper-aware methods in both grasp success rate and inference speed across diverse gripper types. Project page: https://sites.google.com/view/xgrasp
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