Distance-directed Target Searching for a Deep Visual Servo SMA Driven Soft Robot Using Reinforcement Learning

2020 ◽  
Vol 17 (6) ◽  
pp. 1126-1138
Author(s):  
Wuji Liu ◽  
Zhongliang Jing ◽  
Han Pan ◽  
Lingfeng Qiao ◽  
Henry Leung ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Soft Robot ◽  
Visual Servo ◽  
Target Searching

scholarly journals Deep Reinforcement Learning for Target Searching in Cognitive Electronic Warfare

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37432-37447 ◽  
Author(s):  
Shixun You ◽  
Ming Diao ◽  
Lipeng Gao
Keyword(s):  
Reinforcement Learning ◽  
Electronic Warfare ◽  
Target Searching

Feasibility of reinforcement learning for UAV-based target searching in a simulated communication denied environment

2020 ◽  
Vol 50 (3) ◽  
pp. 375-395
Author(s):  
Songlin HOU ◽  
Yuyou WANG ◽  
Tianheng WU ◽  
Liang WANG ◽  
Xianping TAO ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Target Searching

Toward Effective Soft Robot Control via Reinforcement Learning

2017 ◽  
pp. 173-184 ◽  
Author(s):  
Haochong Zhang ◽  
Rongyun Cao ◽  
Shlomo Zilberstein ◽  
Feng Wu ◽  
Xiaoping Chen
Keyword(s):  
Reinforcement Learning ◽  
Robot Control ◽  
Soft Robot

Deep Reinforcement Learning Framework for Underwater Locomotion of Soft Robot

2021 ◽  
Author(s):  
Guanda Li ◽  
Jun Shintake ◽  
Mitsuhiro Hayashibe
Keyword(s):  
Reinforcement Learning ◽  
Soft Robot ◽  
Learning Framework

scholarly journals Position Control of Cable-Driven Robotic Soft Arm Based on Deep Reinforcement Learning

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 310
Author(s):  
Qiuxuan Wu ◽  
Yueqin Gu ◽  
Yancheng Li ◽  
Botao Zhang ◽  
Sergey A. Chepinskiy ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Position Control ◽  
Control Method ◽  
Strategy Training ◽  
Soft Robot ◽  
Robot Arm ◽  
Control Task ◽  
Modeling And Control ◽  
And Migration

The cable-driven soft arm is mostly made of soft material; it is difficult to control because of the material characteristics, so the traditional robot arm modeling and control methods cannot be directly applied to the soft robot arm. In this paper, we combine the data-driven modeling method with the reinforcement learning control method to realize the position control task of robotic soft arm, the method of control strategy based on deep Q learning. In order to solve slow convergence and unstable effect in the process of simulation and migration when deep reinforcement learning is applied to the actual robot control task, a control strategy learning method is designed, which is based on the experimental data, to establish a simulation environment for control strategy training, and then applied to the real environment. Finally, it is proved by experiment that the method can effectively complete the control of the soft robot arm, which has better robustness than the traditional method.

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