DG16M: A Large-Scale Dataset for Dual-Arm Grasping with Force-Optimized Grasps
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866909707685330944 |
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| author | Karim, Md Faizal Hashmi, Mohammed Saad Bollimuntha, Shreya Tapeti, Mahesh Reddy Singh, Gaurav Govindan, Nagamanikandan Krishna, K Madhava |
| author_facet | Karim, Md Faizal Hashmi, Mohammed Saad Bollimuntha, Shreya Tapeti, Mahesh Reddy Singh, Gaurav Govindan, Nagamanikandan Krishna, K Madhava |
| contents | Dual-arm robotic grasping is crucial for handling large objects that require stable and coordinated manipulation. While single-arm grasping has been extensively studied, datasets tailored for dual-arm settings remain scarce. We introduce a large-scale dataset of 16 million dual-arm grasps, evaluated under improved force-closure constraints. Additionally, we develop a benchmark dataset containing 300 objects with approximately 30,000 grasps, evaluated in a physics simulation environment, providing a better grasp quality assessment for dual-arm grasp synthesis methods. Finally, we demonstrate the effectiveness of our dataset by training a Dual-Arm Grasp Classifier network that outperforms the state-of-the-art methods by 15\%, achieving higher grasp success rates and improved generalization across objects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08358 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DG16M: A Large-Scale Dataset for Dual-Arm Grasping with Force-Optimized Grasps Karim, Md Faizal Hashmi, Mohammed Saad Bollimuntha, Shreya Tapeti, Mahesh Reddy Singh, Gaurav Govindan, Nagamanikandan Krishna, K Madhava Robotics Dual-arm robotic grasping is crucial for handling large objects that require stable and coordinated manipulation. While single-arm grasping has been extensively studied, datasets tailored for dual-arm settings remain scarce. We introduce a large-scale dataset of 16 million dual-arm grasps, evaluated under improved force-closure constraints. Additionally, we develop a benchmark dataset containing 300 objects with approximately 30,000 grasps, evaluated in a physics simulation environment, providing a better grasp quality assessment for dual-arm grasp synthesis methods. Finally, we demonstrate the effectiveness of our dataset by training a Dual-Arm Grasp Classifier network that outperforms the state-of-the-art methods by 15\%, achieving higher grasp success rates and improved generalization across objects. |
| title | DG16M: A Large-Scale Dataset for Dual-Arm Grasping with Force-Optimized Grasps |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.08358 |