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
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. Project page: https://dg16m.github.io/DG-16M/
BibTeX
@inproceedings{iros2025_dg16malargescale,
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},
booktitle = {IROS 2025},
year = {2025}
}