Abstract
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.
Abstract (translated)
对几个新型对象的精确机器人抓取是制造、自动化和物流领域一个巨大的挑战。当前没有模型的抓取方法大部分因为抓取数据集的稀疏数据和传感器数据及接触模型的错误而不利。本研究将数据生成和sim-to-real转移学习相结合在一个抓取框架中,从而减少sim-to-real差距并实现精确可靠的模型无关抓取。使用域随机化方法和基于深度学习的机器人抓取的新数据增强方法生成了大规模的具有密集抓握标签的机器人抓取数据集,以解决数据稀疏问题。我们提出了一个端到端机器人抓取网络,并使用抓取优化器进行抓取策略的训练。通过sim-to-real转移学习, presented results suggest that our grasping framework减少了抓取数据集、传感器数据和接触模型的不确定性。在物理机器人实验中,我们的抓取框架成功抓住了已知的单个物体和新型复杂形状的家庭常见物体,成功率为90.91%。在一个包含多个物体的机器人抓取多物体的复杂场景中,成功率为85.71%。提出的抓取框架在已知的和未知的物体机器人抓取中击败了两个先进的方法。
URL
https://arxiv.org/abs/2301.12249