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Imitation learning of a wheeled mobile manipulator based on dynamical movement primitives

Zeguo Yang (School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China)
Mantian Li (School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China)
Fusheng Zha (School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China)
Xin Wang (Shenzhen Academy of Aerospace of Technology, Harbin, China)
Pengfei Wang (School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China)
Wei Guo (School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 17 June 2021

Issue publication date: 19 August 2021

234

Abstract

Purpose

This paper aims to introduce an imitation learning framework for a wheeled mobile manipulator based on dynamical movement primitives (DMPs). A novel mobile manipulator with the capability to learn from demonstration is introduced. Then, this study explains the whole process for a wheeled mobile manipulator to learn a demonstrated task and generalize to new situations. Two visual tracking controllers are designed for recording human demonstrations and monitoring robot operations. The study clarifies how human demonstrations can be learned and generalized to new situations by a wheel mobile manipulator.

Design/methodology/approach

The kinematic model of a mobile manipulator is analyzed. An RGB-D camera is applied to record the demonstration trajectories and observe robot operations. To avoid human demonstration behaviors going out of sight of the camera, a visual tracking controller is designed based on the kinematic model of the mobile manipulator. The demonstration trajectories are then represented by DMPs and learned by the mobile manipulator with corresponding models. Another tracking controller is designed based on the kinematic model of the mobile manipulator to monitor and modify the robot operations.

Findings

To verify the effectiveness of the imitation learning framework, several daily tasks are demonstrated and learned by the mobile manipulator. The results indicate that the presented approach shows good performance for a wheeled mobile manipulator to learn tasks through human demonstrations. The only thing a robot-user needs to do is to provide demonstrations, which highly facilitates the application of mobile manipulators.

Originality/value

The research fulfills the need for a wheeled mobile manipulator to learn tasks via demonstrations instead of manual planning. Similar approaches can be applied to mobile manipulators with different architecture.

Keywords

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No.61773139), National Natural Science Foundation of China (Grant No.51521003), National Natural Science Foundation of China (Grant No.U2013602), Shenzhen Science and Technology Research and Development Foundation (Grant No.JCYJ20190813171009236), Shenzhen Science and Technology Program (Grant No.KQTD2016112515134654).

Citation

Yang, Z., Li, M., Zha, F., Wang, X., Wang, P. and Guo, W. (2021), "Imitation learning of a wheeled mobile manipulator based on dynamical movement primitives", Industrial Robot, Vol. 48 No. 4, pp. 556-568. https://doi.org/10.1108/IR-11-2020-0255

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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