真实超市物体抓取数据集，含6自由度抓取姿态标注。
Supermarket-6DoF: A Real-World Grasping Dataset and Grasp Pose Representation Analysis
- 用机器人实测获取真实抓取结果，非仿真或解析评估。
- 首次在真实数据中同时标注抓取成功与抗扰稳定性。
- 点云表示夹爪几何比四元数编码更准，适合点云预测。
我们提出 Supermarket-6DoF，一个包含20种超市物品、共1500次抓取尝试的真实世界抓取数据集，所有物体均提供公开3D模型。不同于多数依赖解析指标或仿真的抓取标注，本数据集基于实际机器人执行获得真实标签。在少数真实抓取数据集中，虽规模较小，但其独特之处在于对每组抓取标注了完整的6-DoF姿态，并包含初始抓取成功率与外部扰动下的后抓取稳定性。我们通过分析三种从点云中预测抓取成功率的抓取姿态表示方法验证了该数据集的实用性。结果表明，将夹爪几何显式表示为点云的方法，在抓取成功率预测上优于传统的四元数编码方式。
原文摘要 · Abstract (English)
We present Supermarket-6DoF, a real-world dataset of 1500 grasp attempts across 20 supermarket objects with publicly available 3D models. Unlike most existing grasping datasets that rely on analytical metrics or simulation for grasp labeling, our dataset provides ground-truth outcomes from physical robot executions. Among the few real-world grasping datasets, wile more modest in size, Supermarket-6DoF uniquely features full 6-DoF grasp poses annotated with both initial grasp success and post-grasp stability under external perturbation. We demonstrate the dataset's utility by analyzing three grasp pose representations for grasp success prediction from point clouds. Our results show that representing the gripper geometry explicitly as a point cloud achieves higher prediction accuracy compared to conventional quaternion-based grasp pose encoding.
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