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Dynamic System Identification of Underwater Vehicles Using Multi-output Gaussian Processes 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 681-693
作者:  Wilmer Ariza Ramirez;  Juš Kocijan;  Zhi Quan Leong;  Hung Duc Nguyen;  Shantha Gamini Jayasinghe
Adobe PDF(3231Kb)  |  收藏  |  浏览/下载：245/55  |  提交时间：2021/09/13
Dependent Gaussian processes  dynamic system identification  multi-output Gaussian processes  non-parametric identification  autonomous underwater vehicle (AUV)  
A Novel Heterogeneous Actor-critic Algorithm with Recent Emphasizing Replay Memory 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 619-631
作者:  Bao Xi;  Rui Wang;  Ying-Hao Cai;  TaoLu;  Shuo Wang
Adobe PDF(2505Kb)  |  收藏  |  浏览/下载：205/60  |  提交时间：2021/07/20
Reinforcement learning (RL)  actor-critic  experience replay  training efficiency  manipulation skill learning  
Delayed Teleoperation with Force Feedback of a Humanoid Robot 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 605-618
作者:  Viviana Moya;  Emanuel Slawiñski;  Vicente Mut
Adobe PDF(2260Kb)  |  收藏  |  浏览/下载：159/53  |  提交时间：2021/07/20
Humanoid robot  bilateral teleoperation  time delay  force feedback  walking  manipulation  
Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 457-467
作者:  Ying Li;  De Xu
Adobe PDF(1450Kb)  |  收藏  |  浏览/下载：221/53  |  提交时间：2021/05/24
Force Jacobian matrix  one-shot demonstration  dynamic exploration strategy  insertion skill learning  reinforcement  
Deep Audio-Visual Learning: A Survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 351-376
作者:  Hao Zhu;  Man-Di Luo;  Rui Wang;  Ai-Hua Zheng;  Ran He
Adobe PDF(1864Kb)  |  收藏  |  浏览/下载：232/52  |  提交时间：2021/05/24
Deep audio-visual learning  audio-visual separation and localization  correspondence learning  generative models  representation learning  
Suction-based Grasp Point Estimation in Cluttered Environment for Robotic Manipulator Using Deep Learning-based Affordance Map 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 2, 页码: 277-287
作者:  Tri Wahyu Utomo, Adha Imam Cahyadi, Igi Ardiyanto
Adobe PDF(913Kb)  |  收藏  |  浏览/下载：187/67  |  提交时间：2021/04/22
Grasping point estimation  household objects  red, green, blue and depth (RGBD) channel image  semantic segmentation  cluttered environment  
Why Deep Neural Nets Cannot Ever Match Biological Intelligence and What To Do About It? 期刊论文
International Journal of Automation and Computing, 2017, 卷号: 14, 期号: 5, 页码: 532-541
作者:  Danko Nikolic
浏览  |  Adobe PDF(349Kb)  |  收藏  |  浏览/下载：138/28  |  提交时间：2021/02/23
Artificial intelligence  neural networks  strong artificial intelligence  practopoiesis, machine learning.  
Event-based Control and Filtering of Networked Systems: A Survey 期刊论文
International Journal of Automation and Computing, 2017, 卷号: 14, 期号: 3, 页码: 239-253
作者:  Lei Zou;  Zi-Dong Wang;  Dong-Hua Zhou
浏览  |  Adobe PDF(3631Kb)  |  收藏  |  浏览/下载：197/44  |  提交时间：2021/02/23
Event-triggered transmission  networked systems  event-based control  event-based filtering  event-triggered distributed state estimation  distributed control with event-based protocol.  
Pinning Control and Controllability of Complex Dynamical Networks 期刊论文
International Journal of Automation and Computing, 2017, 卷号: 14, 期号: 1, 页码: 1-9
作者:  Guanrong Chen
浏览  |  Adobe PDF(2696Kb)  |  收藏  |  浏览/下载：159/58  |  提交时间：2021/02/23
Complex network  pinning control  controllability  linear time-invariant (LTI) system  temporally switching network  graph theory.  
Learning to Transform Service Instructions into Actions with Reinforcement Learning and Knowledge Base 期刊论文
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 5, 页码: 582-592
作者:  Meng-Yang Zhang;  Guo-Hui Tian;  Ci-Ci Li;  Jing Gong
浏览  |  Adobe PDF(975Kb)  |  收藏  |  浏览/下载：172/51  |  提交时间：2021/02/23
Natural language  robot  knowledge base  reinforcement learning  object state.