构建超大规模抓取数据集，助力机器人通用抓取能力提升
GraspFactory: A Large Object-Centric Grasping Dataset
- 基于14,690种物体生成超1亿个6-自由度抓取姿态
- 在模拟与真实环境中验证模型对新物体的泛化性能
- 适合机器人抓取、工业自动化领域研究者使用
机器人抓取是工业自动化中的关键任务，随着机器人需处理的物体种类日益增多，模型在面对新物体时的泛化能力面临挑战。为训练具备强泛化能力的抓取模型，需依赖几何多样性丰富的数据集。本文提出GraspFactory，一个包含超过1.09亿个6-自由度抓取姿态的数据集，覆盖Franka Panda（14,690种物体）和Robotiq 2F-85（33,710种物体）两种机械臂。该数据集专为数据密集型模型训练设计，我们通过在部分数据上训练的模型，在仿真与真实场景中均展现出良好泛化能力。相关数据与工具已公开：https://graspfactory.github.io/
原文摘要 · Abstract (English)
Robotic grasping is a crucial task in industrial automation, where robots are increasingly expected to handle a wide range of objects. However, a significant challenge arises when robot grasping models trained on limited datasets encounter novel objects. In real-world environments such as warehouses or manufacturing plants, the diversity of objects can be vast, and grasping models need to generalize to this diversity. Training large, generalizable robot-grasping models requires geometrically diverse datasets. In this paper, we introduce GraspFactory, a dataset containing over 109 million 6-DoF grasps collectively for the Franka Panda (with 14,690 objects) and Robotiq 2F-85 grippers (with 33,710 objects). GraspFactory is designed for training data-intensive models, and we demonstrate the generalization capabilities of one such model trained on a subset of GraspFactory in both simulated and real-world settings. The dataset and tools are made available for download at https://graspfactory.github.io/.
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