RAGrasp：几何-语义模板检索与抓取迁移

中文摘要 AI 辅助

我们提出了RAGrasp，一种检索增强的流水线，用于从一组紧凑的本地采集、带有抓取标注的RGB-D（颜色和深度）模板中进行平面平行夹爪抓取。与主要在大规模公共或合成抓取数据集上训练的特定任务预测器不同，RAGrasp对于新的工作空间无需端到端的重新训练。模板记忆由部署相机、机器人和夹爪在目标工作空间中采集的观测构建，从而使存储的示例与本地感知和具身条件对齐。该系统使用自监督的DINOv2视觉特征，结合外观和深度线索来提示Segment Anything Model 2（SAM2），以隔离查询对象。一个两阶段的几何-语义检索级联随后选择一个模板，一个置信度门控选择两个抓取迁移估计器之一。迁移的抓取在标定的2D到3D转换之前，使用掩膜支持和轮廓-接触约束进行细化。在真实世界试验中，RAGrasp在已见物体上实现了20/20的成功抓取，在未见物体上实现了19/20的成功抓取。在评估的设置内，结果表明了从有限本地标注进行部署特定的抓取适应，以及对测试的视点和光照变化的容忍性。

英文摘要

We present RAGrasp, a retrieval-augmented pipeline for planar parallel-jaw grasping from a compact set of locally collected, grasp-annotated RGB-D (color and depth) templates. Unlike task-specific predictors trained primarily on large public or synthetic grasp datasets, RAGrasp requires no end-to-end retraining for a new deployment.Its template memory is constructed from observations collected with the deployment camera, robot, and gripper in the target workspace, thereby aligning stored examples with the local sensing and embodiment conditions. The system uses self-supervised DINOv2 visual fea- tures together with appearance and depth cues to prompt the Segment Anything Model 2 (SAM2), which isolates the query object. A two-stage geometry-semantic retrieval cascade then selects a template, and a confidence gate chooses one of two grasp- transfer estimators. The transferred grasp is refined using mask- support and silhouette-contact constraints before calibrated 2D- to-3D conversion. In real-world trials, RAGrasp achieves 20/20 successful grasps on seen objects and 19/20 on unseen objects. Within the evaluated setting, the results demonstrate deployment- specific grasp adaptation from limited local annotation and tolerance to the tested viewpoint and illumination changes.

发表机构

• Tongji University（同济大学）

机构由 AI 辅助整理，请以论文原文为准。