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.
@article{arxiv.2503.08358,
title = {DG16M: A Large-Scale Dataset for Dual-Arm Grasping with Force-Optimized Grasps},
author = {Md Faizal Karim and Mohammed Saad Hashmi and Shreya Bollimuntha and Mahesh Reddy Tapeti and Gaurav Singh and Nagamanikandan Govindan and K Madhava Krishna},
journal= {arXiv preprint arXiv:2503.08358},
year = {2025}
}