CASIA OpenIR  > 类脑智能研究中心  > 神经计算与脑机交互
闫哲1; 杜学丹1,2; 曹淼1,2; 蔡莹皓2; 鲁涛2; 王硕2
Source Publication高技术通讯
Abstract进行了机器人的智能抓取研究,提出了一种基于深度学习的机器人抓取位置检测方法。该方法以目标物体的多模态特征作为训练数据,采用无监督学习与监督学习相结合的方式对目标物体的最优抓取位置进行学习。在无监督学习阶段,使用压缩自动编码器(CAE)对神经网络进行无监督逐层预训练;在减粗学习阶段,使用反向传播算法对整个网络进行监督微调。仿真实验结果验证了该方法能够对目标物体的最优抓取位置做出精确的判断。在Universal Robot 5机器人上进行了抓取实验,实验结果表明该方法的抓取成功率较高,能够应用到机器人抓取相关的任务中。
Other AbstractThe research on intelligent grasping of robots is conducted, and a method for detection of the robotic grasping position of an pbject based on deep learning is proposed. The method learns the optimal grasping position of an object by using the mode of combining unsupervised learning and supervised learning, with the multimodal features of a target object as the training data. In the unsupervised learning process, it uses a contractive auto encoder (CAE) to pre-train the neural network layer by layer, and then the whole network is fine-tuned by using the back propagation algorithm in the supervised learning process. The simulation results verify that the proposed method can give the accurate optimal grasping position of any object. The experment on Universal Robot 5 shows that the grasping success rate is very high, indicatiing that the proposed method can be applied to robotic grasping.
Keyword深度学习 机器人抓取 位置检测 压缩自动编码器
Document Type期刊论文
Recommended Citation
GB/T 7714
闫哲,杜学丹,曹淼,等. 基于深度学习的机器人抓取位置检测方法[J]. 高技术通讯,2018,28(1):58-66.
APA 闫哲,杜学丹,曹淼,蔡莹皓,鲁涛,&王硕.(2018).基于深度学习的机器人抓取位置检测方法.高技术通讯,28(1),58-66.
MLA 闫哲,et al."基于深度学习的机器人抓取位置检测方法".高技术通讯 28.1(2018):58-66.
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