Few-Shot Instance Grasping of Novel Objects in Clutter-Reference-Cited by-同舟云学术

Few-Shot Instance Grasping of Novel Objects in Clutter

Published:2022-07 Issue:3 Volume:7 Page:6566-6573
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Guo Weikun¹^ORCID,Li Wei¹^ORCID,Hu Ziye¹^ORCID,Gan Zhongxue¹

Affiliation:

1. Academy for Engineering and Technology, Fudan University, Shanghai, China

Funder

Ji Hua Laboratory

Shanghai Municipal Science and Technology

Science and Technology Commission of Shanghai Municipality

Shanghai Engineering Research Center of AI and Robotics

Engineering Research Center of AI and Robotics

Ministry of Education, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9750005/09773996.pdf?arnumber=9773996

Reference27 articles.

1. Learning robust, real-time, reactive robotic grasping

2. Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

3. Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

4. Fully Convolutional Grasp Detection Network with Oriented Anchor Box

5. End-to-end learning of semantic grasping;jang,2017

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3. Robotic Object Perception Based on Multispectral Few-Shot Coupled Learning;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2023-10

4. Direct Object Reconstruction on RGB-D Images in Cluttered Environment;2023 European Conference on Mobile Robots (ECMR);2023-09-04

5. Unseen Object Few-Shot Semantic Segmentation for Robotic Grasping;IEEE Robotics and Automation Letters;2023-01