Robot Arm Control Method of Moving Below Object Based on Deep Reinforcement Learning

2019 ◽  
pp. 127-136
Author(s):  
HeYu Li ◽  
LiQin Guo ◽  
GuoQiang Shi ◽  
YingYing Xiao ◽  
Bi Zeng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Control Method ◽  
Robot Arm ◽  
Object Based ◽  
Robot Arm Control

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.

scholarly journals Robot arm control method using forearm EMG signals

2020 ◽  
Vol 309 ◽  
pp. 04007
Author(s):  
Minjie Chen ◽  
Honghai Liu
Keyword(s):  
Continuous Improvement ◽  
Control Method ◽  
Recognition Rate ◽  
Robotic Arm ◽  
Support Vector ◽  
Robot Arm ◽  
Svm Algorithm ◽  
Input Signals ◽  
Robot Arm Control ◽  
The Cost

With the continuous improvement of control technology and the continuous improvement of people’s living standards, the needs of disabled people for high-quality prosthetics have become increasingly strong. A control method of robotic arm based on surface electromyography signal (sEMG) of forearm is proposed. Firstly, the 16-channel EMG data of the forearm is obtained via the multi-channel EMG acquisition instrument and the electrode cuff as input signals, the features are extracted, then the gestures are classified and identified by the support-vector machine (SVM) algorithm, and the signals are finally transmitted to the robotic arm, so that people can teleoperate the robotic arm via sEMG signals in real time. Reduce the number of channels to lower the cost while ensuring a high and usable recognition rate. Experiments were performed by collecting EMG signals from the forearm surface of eight healthy volunteers. The experimental results show that the system’s overall gesture recognition accuracy rate can reach up to 90%, and the system responds fast, laying a good foundation for manipulating artificial limbs in the future.

scholarly journals A Collaborative Control Method of Dual-Arm Robots Based on Deep Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1816
Author(s):  
Luyu Liu ◽  
Qianyuan Liu ◽  
Yong Song ◽  
Bao Pang ◽  
Xianfeng Yuan ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Control Method ◽  
Gradient Algorithm ◽  
Robot Arm ◽  
Collaborative Control ◽  
Policy Gradient ◽  
Multi Agent ◽  
Dual Arm Robot ◽  
Dual Arm

Collaborative control of a dual-arm robot refers to collision avoidance and working together to accomplish a task. To prevent the collision of two arms, the control strategy of a robot arm needs to avoid competition and to cooperate with the other one during motion planning. In this paper, a dual-arm deep deterministic policy gradient (DADDPG) algorithm is proposed based on deep reinforcement learning of multi-agent cooperation. Firstly, the construction method of a replay buffer in a hindsight experience replay algorithm is introduced. The modeling and training method of the multi-agent deep deterministic policy gradient algorithm is explained. Secondly, a control strategy is assigned to each robotic arm. The arms share their observations and actions. The dual-arm robot is trained based on a mechanism of “rewarding cooperation and punishing competition”. Finally, the effectiveness of the algorithm is verified in the Reach, Push, and Pick up simulation environment built in this study. The experiment results show that the robot trained by the DADDPG algorithm can achieve cooperative tasks. The algorithm can make the robots explore the action space autonomously and reduce the level of competition with each other. The collaborative robots have better adaptability to coordination tasks.

Continuous reinforcement learning based ramp jump control for single-track two-wheeled robots

2021 ◽  
pp. 014233122110378
Author(s):  
Qingyuan Zheng ◽  
Duo Wang ◽  
Zhang Chen ◽  
Yiyong Sun ◽  
Bin Liang
Keyword(s):  
Reinforcement Learning ◽  
Energy Savings ◽  
Control Method ◽  
Learning Control ◽  
Action Space ◽  
Gradient Algorithm ◽  
Single Track ◽  
Wheeled Robots ◽  
Reward Function ◽  
Wheeled Robot

Single-track two-wheeled robots have become an important research topic in recent years, owing to their simple structure, energy savings and ability to run on narrow roads. However, the ramp jump remains a challenging task. In this study, we propose to realize a single-track two-wheeled robot ramp jump. We present a control method that employs continuous action reinforcement learning techniques for single-track two-wheeled robot control. We design a novel reward function for reinforcement learning, optimize the dimensions of the action space, and enable training under the deep deterministic policy gradient algorithm. Finally, we validate the control method through simulation experiments and successfully realize the single-track two-wheeled robot ramp jump task. Simulation results validate that the control method is effective and has several advantages over high-dimension action space control, reinforcement learning control of sparse reward function and discrete action reinforcement learning control.

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