AutoLfD: Closing the Loop for Learning from Demonstrations

Wu, S. orcid.org/0009-0001-9266-0799, Wang, Y. and Huang, Y. orcid.org/0000-0002-5395-5076 (2025) AutoLfD: Closing the Loop for Learning from Demonstrations. IEEE Transactions on Automation Science and Engineering. ISSN 1545-5955

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Item Type: Article
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This is an author produced version of an article published in IEEE Transactions on Automation Science and Engineering, made available under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: Learning from demonstrations, generalization metric, trajectory encoder network, dynamical movement primitives, kernelized movement primitives
Dates:
  • Published (online): 22 January 2025
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence
Depositing User: Symplectic Publications
Date Deposited: 31 Jan 2025 13:05
Last Modified: 31 Jan 2025 13:05
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Identification Number: 10.1109/tase.2025.3532820
Open Archives Initiative ID (OAI ID):

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