DG16M: A Large-Scale Dataset for Dual-Arm Grasping with Force-Optimized Grasps

Md Faizal Karim; Mohammed Saad Hashmi; Shreya Bollimuntha; Mahesh Reddy Tapeti; Gaurav Singh; Nagamanikandan Govindan; K Madhava Krishna

arXiv · 2503.08358

DG16M: A Large-Scale Dataset for Dual-Arm Grasping with Force-Optimized Grasps

Md Faizal Karim·Mohammed Saad Hashmi·Shreya Bollimuntha·Mahesh Reddy Tapeti·Gaurav Singh·Nagamanikandan Govindan·K Madhava Krishna

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Abstract

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.

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Md Faizal Karim, Mohammed Saad Hashmi, Shreya Bollimuntha, Mahesh Reddy Tapeti, Gaurav Singh, Nagamanikandan Govindan, K Madhava Krishna. 2025-03-11. DG16M: A Large-Scale Dataset for Dual-Arm Grasping with Force-Optimized Grasps. https://arxiv.org/abs/2503.08358

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