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A constrained framework based on IBLF for robot learning with human supervision

Shi, Donghao; Li, Qinchuan; Yang, Chenguang; Lu, Zhenyu

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Authors

Donghao Shi

Qinchuan Li

Zhenyu Lu



Abstract

Dynamical movement primitives (DMPs) method is a useful tool for efficient robotic skills learning from human demonstrations. However, the DMPs method should know the specified constraints of tasks in advance. One flexible solution is to introduce the human superior experience as part of input. In this paper, we propose a framework for robot learning based on demonstration and supervision. Superior experience supplied by teleoperation is introduced to deal with unknown environment constrains and correct the demonstration for next execution. DMPs model with integral barrier Lyapunov function is used to deal with the constrains in robot learning. Additionally, a radial basis function neural network based controller is developed for teleoperation and the robot to track the generated motions. Then, we prove convergence of the generated path and controller. Finally, we deploy the novel framework with two touch robots to certify its effectiveness.

Citation

Shi, D., Li, Q., Yang, C., & Lu, Z. (2023). A constrained framework based on IBLF for robot learning with human supervision. Robotica, 41(8), 2451-2463. https://doi.org/10.1017/S0263574723000462

Journal Article Type Article
Acceptance Date Mar 28, 2023
Online Publication Date Apr 24, 2023
Publication Date Aug 31, 2023
Deposit Date May 3, 2023
Publicly Available Date Oct 25, 2023
Journal Robotica
Print ISSN 0263-5747
Electronic ISSN 1469-8668
Publisher Cambridge University Press (CUP)
Peer Reviewed Peer Reviewed
Volume 41
Issue 8
Pages 2451-2463
DOI https://doi.org/10.1017/S0263574723000462
Keywords Computer Science Applications; General Mathematics; Software; Control and Systems Engineering; Control and Optimization; Mechanical Engineering; Modeling and Simulation; dynamic movement primitives; robotic skill learning; integral barrier Lyapunov functi
Public URL https://uwe-repository.worktribe.com/output/10732041
Publisher URL https://www.cambridge.org/core/journals/robotica/article/constrained-framework-based-on-iblf-for-robot-learning-with-human-supervision/EC77418C0AA18A59CDC630C6BDF69FE7

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Licence
http://creativecommons.org/licenses/by-nc-nd/4.0/

Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright Statement
This is the authors accepted version of the article 'Shi, D., Li, Q., Yang, C., & Lu, Z. A constrained framework based on IBLF for robot learning with human supervision. Robotica'.

DOI: https://doi.org/10.1017/s0263574723000462

The final published version is available from here: https://www.cambridge.org/core/journals/robotica/article/constrained-framework-based-on-iblf-for-robot-learning-with-human-supervision/EC77418C0AA18A59CDC630C6BDF69FE7

© The Author(s), 2023. Published by Cambridge University Press





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