Fed-HANet: Federated Visual Grasping Learning for Human Robot Handovers-Reference-Cited by-同舟云学术

Fed-HANet: Federated Visual Grasping Learning for Human Robot Handovers

Published:2023-06 Issue:6 Volume:8 Page:3772-3779
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Huang Ching-I¹^ORCID,Huang Yu-Yen¹^ORCID,Liu Jie-Xin¹,Ko Yu-Ting¹,Wang Hsueh-Cheng¹^ORCID,Chiang Kuang-Hsing²,Yu Lap-Fai³^ORCID

Affiliation:

1. National Yang Ming Chiao Tung University (NYCU), Hsinchu, Taiwan

2. Taipei Heart Institute, Taipei Medical University, Taipei, Taiwan

3. Department of Computer Science, George Mason University, Fairfax, VA, USA

Funder

Taiwan's National Science and Technology Council

Taiwan University Research Collaboration Project

National Yang Ming Chiao Tung University and Ministry of Education

Higher Education Sprout Project

National Science Foundation

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

https://ieeexplore.ieee.org/ielam/7083369/10102643/10109097-aam.pdf

Reference32 articles.

1. Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching;zeng;Int J Robot Res,2019

2. Learning ambidextrous robot grasping policies

3. 6-DOF GraspNet: Variational Grasp Generation for Object Manipulation

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

5. Deep object pose estimation for semantic robotic grasping of household objects;tremblay;Proc Conf Robot Learn,0

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