论文标题
连续的视觉伺服插入快速稳健的孔插入
Fast robust peg-in-hole insertion with continuous visual servoing
论文作者
论文摘要
本文展示了一种视觉宣传方法，该方法对与系统校准和掌握相关的不确定性具有鲁棒性，同时与经典方法和基于深度学习的最新尝试相比，大大减少了孔洞时间。所提出的视觉宣传方法基于多型摄像头设置中深神经网络的PEG和孔点估计，该模型在纯粹的合成数据上进行了培训。经验结果表明，学识渊博的模型将其推广到现实世界，使成功率更高，周期时间较高，而周期时间则比现有方法更低。
This paper demonstrates a visual servoing method which is robust towards uncertainties related to system calibration and grasping, while significantly reducing the peg-in-hole time compared to classical methods and recent attempts based on deep learning. The proposed visual servoing method is based on peg and hole point estimates from a deep neural network in a multi-cam setup, where the model is trained on purely synthetic data. Empirical results show that the learnt model generalizes to the real world, allowing for higher success rates and lower cycle times than existing approaches.
