Knowledge Commons of Institute of Automation,CAS
Object Recognition, Localization and Grasp Detection Using a Unified Deep Convolutional Neural Network with Multi-task Loss | |
Qun, Jia1,2![]() ![]() ![]() ![]() ![]() ![]() | |
2018-12 | |
会议名称 | IEEE International Conference on Robotics and Biomimetics |
会议录名称 | IEEE International Conference on Robotics and Biomimetics |
页码 | 1545-1550 |
会议日期 | 2018年12月12~15号 |
会议地点 | 吉隆坡 |
摘要 | Recognize an object and detect a good grasp in unstructured scenes is still a challenge. In this paper, the problem of detecting robotic grasps is expressed by a two-point representation in an unstructured scene with an RGB-D camera. A deep Convolutional Neural Network is designed to predict good grasps in real-time on GTX1080, with using region proposal techniques. A contribution of this work is our proposed network framework can perform classification, location and grasp detection simultaneously so that in a single step, it not only recognizes the category and bounding-box of the object, but also finds a good grasp line. Besides, in training process, we minimize a multi-task loss objective function of object classification, location and grasp detection in order to train the network endto-end. Our experimental evaluation on a real robotic manipulator demonstrates that the robotic manipulator can fulfill the grasping task effectively. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23604 |
专题 | 复杂系统认知与决策实验室_先进机器人 复杂系统管理与控制国家重点实验室 |
通讯作者 | Zhiqiang, Cao |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Qun, Jia,Zhiqiang, Cao,Xionglei, Zhao,et al. Object Recognition, Localization and Grasp Detection Using a Unified Deep Convolutional Neural Network with Multi-task Loss[C],2018:1545-1550. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
robio18-252.pdf(2230KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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