GraspFactory: A Large Object-Centric Grasping Dataset
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909805349699584 |
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| author | Srinivas, Srinidhi Kalgundi Shukla, Yash Arnold, Adam Chitta, Sachin |
| author_facet | Srinivas, Srinidhi Kalgundi Shukla, Yash Arnold, Adam Chitta, Sachin |
| contents | 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/. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_20550 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GraspFactory: A Large Object-Centric Grasping Dataset Srinivas, Srinidhi Kalgundi Shukla, Yash Arnold, Adam Chitta, Sachin Robotics Artificial Intelligence 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/. |
| title | GraspFactory: A Large Object-Centric Grasping Dataset |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.20550 |