A robotic learning and generalization framework for curved surface based on modified DMP

作     者：Xue, Xianfa Dong, Jiale Lu, Zhenyu Wang, Ning 

作者机构：South China Univ Technol Sch Automation Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou Peoples R China Univ West England Bristol Robot Lab Coldharbour Ln Frenchay BS16 1QY England 

出 版 物：《ROBOTICS AND AUTONOMOUS SYSTEMS》 (机器人学和自控系统)

年 卷 期：2023年第160卷

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China (NSFC) Industrial Key Technologies R & D Program of Foshan, China 61803039 2020001006308 

主　　题：Continuous drag demonstration Modified DMP Curve drawing experiments Learning and generalization framework 

摘      要：How to reproduce and generalize the skills acquired by demonstrating is a hot topic for researchers. (1) A compliant continuous drag demonstration system based on discrete admittance model was designed to continuously and smoothly drag or demonstrate. (2) The modified DMP including the scaling factor and the force coupling term was used to improve the poor generalization ability of the classical DMP. (3) Curve drawing experiments were carried out to show the effectiveness of our proposed learning and generalization framework.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).