Learning Based Robot Control with Sequential Gaussian Process

被引：0
|
作者：
Park, Sooho [1 ]
Mustafa, Shabbir Kurbanhusen [2 ]
Shimada, Kenji [1 ]
机构：
[1] Carnegie Mellon Univ, 5000 Forbes Ave, Pittsburgh, PA 15213 USA
[2] Singapore Inst Mfg Technol, \ Singapore 638075, Singapore
来源：
PROCEEDINGS OF THE 2013 IEEE WORKSHOP ON ROBOTIC INTELLIGENCE IN INFORMATIONALLY STRUCTURED SPACE (RIISS) | 2013年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In recent years, robots have started being utilized in applications with complex/unknown interaction environment, which makes system/interface modeling to be very challenging. In order to meet the demand from such applications, the experience based learning approach can be a suitable tool. In this paper, a general algorithm for learning based robot control is presented, and a novel online algorithm using sequential Gaussian process is introduced. As a case study, a simple inverted pendulum is tested to present the capabilities of the proposed algorithm.
引用
收藏
页码：120 / 127
页数：8
相关论文
共 50 条
  • [1] Learning-Based Robot Control with Localized Sparse Online Gaussian Process
    Park, Sooho
    Mustafa, Shabbir Kurbanhusen
    Shimada, Kenji
    2013 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2013, : 1202 - 1207
  • [2] Model-Based Robot Control with Gaussian Process Online Learning: An Experimental Demonstration
    Tesfazgi, Samuel
    Lederer, Armin
    Kunz, Johannes F.
    Ordonez-Conejo, Alejandro J.
    Hirche, Sandra
    IFAC PAPERSONLINE, 2023, 56 (02): : 501 - 506
  • [3] A Robot Self-learning Grasping Control Method Based on Gaussian Process and Bayesian Algorithm
    Tao, Yong
    Liu, Hui
    Deng, Xianling
    Chen, Youdong
    Xiong, Hegen
    Fang, Zengliang
    Xie, Xianwu
    Xu, Xi
    TRANSACTIONS ON INTELLIGENT WELDING MANUFACTURING, VOLUME I NO. 3 2017, 2018, I (03): : 79 - 98
  • [4] Gaussian Process-Enhanced Impedance Iterative Learning for Robot Interaction Control
    Pan, Yongping
    Li, Wei
    Shi, Tian
    2024 IEEE CONFERENCE ON ARTIFICIAL INTELLIGENCE, CAI 2024, 2024, : 616 - 619
  • [5] Gaussian Process Based Tracking Control for Robot Manipulators with Dynamical Uncertainties
    Lv, Guannan
    Ren, Yunxiao
    Zhang, Zhao
    Duan, Zhisheng
    PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 3072 - 3076
  • [6] Robot Learning by Demonstration with Local Gaussian Process Regression
    Schneider, Markus
    Ertel, Wolfgang
    IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, : 255 - 260
  • [7] Robot Model Learning with Gaussian Process Mixture Model
    Park, Sooho
    Huang, Yu
    Goh, Chun Fan
    Shimada, Kenji
    2018 IEEE 14TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2018, : 1263 - 1268
  • [8] Sequential process convolution Gaussian process models via particle learning
    Liang, Waley W. J.
    Lee, Herbert K. H.
    STATISTICS AND ITS INTERFACE, 2014, 7 (04) : 465 - 475
  • [9] Gaussian Process (GP)-based Learning Control of Selective Laser Melting Process
    Asadi, Farshid
    Olleak, Alaa
    Yi, Jingang
    Guo, Yuebin
    2021 AMERICAN CONTROL CONFERENCE (ACC), 2021, : 508 - 513
  • [10] Stable Model-based Control with Gaussian Process Regression for Robot Manipulators
    Beckers, Thomas
    Umlauft, Jonas
    Hirche, Sandra
    IFAC PAPERSONLINE, 2017, 50 (01): : 3877 - 3884
12345