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A constrained DMPs framework for robot skills learning and generalization from human demonstrations

Lu, Zhenyu; Wang, Ning; Yang, Chenguang

A constrained DMPs framework for robot skills learning and generalization from human demonstrations Thumbnail


Authors

Zhenyu Lu



Abstract

Dynamical movement primitives (DMPs) model is a useful tool for efficiently robotic learning manipulation skills from human demonstrations and then generalizing these skills to fulfill new tasks. It is improved and applied for the cases with multiple constraints such as having obstacles or relative distance limitation for multi-agent formation. However, the improved DMPs should change additional terms according to the specified constraints of different tasks. In this paper, we will propose a novel DMPs framework facing the constrained conditions for robotic skills generalization. First, we conclude the common characteristics of previous modified DMPs with constraints and propose a general DMPs framework with various classified constraints. Inspired by barrier Lyapunov functions (BLFs), an additional acceleration term of the general model is deduced to compensate tracking errors between the real and desired trajectories with constraints. Furthermore, we prove convergence of the generated path and makes a discussion about advantages of the proposed method compared with existing literature. Finally, we instantiate the novel framework through three experiments: obstacle avoidance in the static and dynamic environment and human-like cooperative manipulation, to certify its effectiveness.

Citation

Wang, N., Lu, Z., & Yang, C. (in press). A constrained DMPs framework for robot skills learning and generalization from human demonstrations. IEEE/ASME Transactions on Mechatronics, https://doi.org/10.1109/TMECH.2021.3057022

Journal Article Type Article
Acceptance Date Nov 1, 2020
Online Publication Date Feb 4, 2021
Deposit Date Apr 28, 2021
Publicly Available Date Apr 29, 2021
Journal IEEE/ASME Transactions on Mechatronics
Print ISSN 1083-4435
Electronic ISSN 1941-014X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/TMECH.2021.3057022
Keywords Dynamic movement primitives (DMPs); robot learning; skills generalization; barrier Lyapunov functions(BLFs)
Public URL https://uwe-repository.worktribe.com/output/7316932

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