构建1600万条双臂抓取数据集，提升大物体协同抓取能力
DG16M: A Large-Scale Dataset for Dual-Arm Grasping with Force-Optimized Grasps
- 构建1600万条双臂抓取样本，基于力闭合约束优化抓取质量
- 在300个物体上生成约3万组抓取数据，仿真环境评估抓取成功率提升15%
- 适用于双臂机器人抓取算法训练与评估，适合工业协作场景研究
双臂机器人抓取对处理大型物体的稳定协同操作至关重要。尽管单臂抓取已得到广泛研究，但针对双臂设置的数据集仍十分稀缺。本文提出一个包含1600万条双臂抓取的大型数据集，其评估基于改进的力闭合约束。此外，我们构建了一个基准数据集，包含300个物体和约3万组抓取，均在物理仿真环境中评估，为双臂抓取合成方法提供更精准的抓取质量评估。最后，我们通过训练双臂抓取分类网络，证明该数据集的有效性：相比现有最优方法，抓取成功率提升15%，并在不同物体间表现出更强泛化能力。
原文摘要 · Abstract (English)
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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