@inproceedings{e70521874e85407b97ac9e267351596d,
title = "Visual Servoing Gain Tuning by Sarsa: An Application with a Manipulator",
abstract = "This paper investigates a Sarsa-based visual servoing control gain tuning method and the application on a manipulator. For a typical visual servo controller, fixed control gains will not provide the best performance. Therefore, state action reward state action (SARSA) algorithm, one of learning-based methods from reinforcement learning (RL), is introduced to select control gains in every control step. The norm of the visual error is used to define the state space. The positive gain of the controller is discretized as the actions. A reward function is defined to evaluate the performance of every action. Both a numerical test and a robot experiment are carried out to validate the presented algorithm.",
keywords = "SARSA, manipulator, reinforcement learning, visual servoing",
author = "Jie Liu and Yang Zhou and Jian Gao and Weisheng Yan",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 3rd International Conference on Robotics and Control Engineering, RobCE 2023 ; Conference date: 12-05-2023 Through 14-05-2023",
year = "2023",
month = may,
day = "12",
doi = "10.1145/3598151.3598169",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "103--107",
editor = "Aiguo Song and Maki Habib",
booktitle = "Proceedings - 2023 3rd International Conference on Robotics and Control Engineering, RobCE 2023",
}