Precise robotic grasping of several novel objects is a huge challenge in manufacturing, automation, and logistics. Most of the current methods for model-free grasping are disadvantaged by the sparse data in grasping datasets and by errors in sensor data and contact models. This study combines data generation and sim-to-real transfer learning in a grasping framework that reduces the sim-to-real gap and enables precise and reliable model-free grasping. A large-scale robotic grasping dataset with dense grasp labels is generated using domain randomization methods and a novel data augmentation method for deep learning-based robotic grasping to solve data sparse problem. We present an end-to-end robotic grasping network with a grasp optimizer. The grasp policies are trained with sim-to-real transfer learning. The presented results suggest that our grasping framework reduces the uncertainties in grasping datasets, sensor data, and contact models. In physical robotic experiments, our grasping framework grasped single known objects and novel complex-shaped household objects with a success rate of 90.91%. In a complex scenario with multi-objects robotic grasping, the success rate was 85.71%. The proposed grasping framework outperformed two state-of-the-art methods in both known and unknown object robotic grasping.

翻译：精确抓取多种新物体是制造、自动化和物流领域的重大挑战。当前大多数无模型抓取方法受限于抓取数据集的稀疏性，以及传感器数据和接触模型的误差。本研究将数据生成与Sim-to-Real迁移学习相结合，构建了一套抓取框架，有效缩小了仿真与现实之间的差距，实现了精确可靠的无模型抓取。通过域随机化方法和一种新颖的数据增强方法（针对基于深度学习的机器人抓取），生成了具有密集抓取标签的大规模机器人抓取数据集，以解决数据稀疏问题。我们提出了一种带有抓取优化器的端到端机器人抓取网络，并利用Sim-to-Real迁移学习训练抓取策略。实验结果表明，该抓取框架减小了抓取数据集、传感器数据和接触模型中的不确定性。在实物机器人实验中，该框架抓取单个已知物体和形状复杂的新型家居物体的成功率达到90.91%；在多物体复杂场景抓取中，成功率为85.71%。所提出的抓取框架在已知和未知物体的机器人抓取中均优于两种现有最先进方法。