CPQNet: Contact Points Quality Network for Robotic Grasping-Reference-Cited by-同舟云学术

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CPQNet: Contact Points Quality Network for Robotic Grasping

Published:2022-10-23 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
language:
Short-container-title:

Author:

Li Zhihao¹,Zeng Pengfei²,Su Jionglong³,Guo Qingda⁴,Ding Ning¹,Zhang Jiaming¹

Affiliation:

1. Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong (Shenzhen),Shenzhen,China

2. Shenzhen Institute of Artificial Intelligence and Robotics for Society,Shenzhen,China

3. School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University,Suzhou,China

4. TOP-AIRS Hospital Intelligent Technique Joint Lab,Shenzhen,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9981026/9981028/09981372.pdf?arnumber=9981372

Reference33 articles.

1. Learning 6-DOF Grasping Interaction via Deep Geometry-Aware 3D Representations

2. The Columbia grasp database

3. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

4. Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours

5. Visual Tracking with Fully Convolutional Networks

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