TY - GEN
T1 - Learning from demonstration using improved dynamic movement primitives
AU - Wang, Tiantian
AU - Yan, Liang
AU - Wang, Gang
AU - Gao, Xiaoshan
AU - Du, Nannan
AU - Chen, I. Ming
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/1
Y1 - 2021/8/1
N2 - It is important to endow the robot with the ability of learning the complex motion sequences and thus adopt such motions when facing to changeable environment. This paper proposes an improved Dynamic Movement Primitives (DMP) method. In order to solve the problem of invalidation of forcing term in conventional DMP, the improved DMP approach, i.e., the DMP together with Deep Neural Network (DNN), is proposed. Specially, DNN is introduced to fit the target nonlinear function with the demonstrated trajectory information, instead of using a specific formula to describe the forcing term in DMP. Thus, improved DMP method can avoid the drawback of conventional DMP. Simulation work is conducted and the results show that the invalidation performance of forcing term is improved compared with conventional DMP. In addition, the generalization property of improved DMP is also beneficial to work environmental adaptability.
AB - It is important to endow the robot with the ability of learning the complex motion sequences and thus adopt such motions when facing to changeable environment. This paper proposes an improved Dynamic Movement Primitives (DMP) method. In order to solve the problem of invalidation of forcing term in conventional DMP, the improved DMP approach, i.e., the DMP together with Deep Neural Network (DNN), is proposed. Specially, DNN is introduced to fit the target nonlinear function with the demonstrated trajectory information, instead of using a specific formula to describe the forcing term in DMP. Thus, improved DMP method can avoid the drawback of conventional DMP. Simulation work is conducted and the results show that the invalidation performance of forcing term is improved compared with conventional DMP. In addition, the generalization property of improved DMP is also beneficial to work environmental adaptability.
KW - DMP
KW - DNN
KW - Fitting of non-linear function
KW - Trajectory learning
UR - https://www.scopus.com/pages/publications/85115445275
U2 - 10.1109/ICIEA51954.2021.9516425
DO - 10.1109/ICIEA51954.2021.9516425
M3 - 会议稿件
AN - SCOPUS:85115445275
T3 - Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
SP - 2130
EP - 2135
BT - Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
Y2 - 1 August 2021 through 4 August 2021
ER -