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
Abstract
Over the past few years, there have been numerous works towards advancing the generalization capability of robots, among which learning from demonstrations (LfD) has drawn much attention by virtue of its user-friendly and data-efficient nature. While many LfD solutions have been reported, a key question has not been properly addressed: how can we evaluate the generalization performance of LfD? For instance, when a robot draws a letter that needs to pass through new desired points, how does it ensure the new trajectory maintains a similar shape to the demonstration? This question becomes more relevant when a new task is significantly far from the demonstrated region. To tackle this issue, a user often resorts to manual tuning of the hyperparameters of an LfD approach until a satisfactory trajectory is attained. In this paper, we aim to provide closed-loop evaluative feedback for LfD and optimize LfD in an automatic fashion. Specifically, we consider dynamical movement primitives (DMP) and kernelized movement primitives (KMP) as examples and develop a generic optimization framework capable of measuring the generalization performance of DMP and KMP and auto-optimizing their hyperparameters. Evaluations including peg-in-hole, block-stacking and pushing tasks on a real robot evidence the applicability of our framework.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | 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: |
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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): | oai:eprints.whiterose.ac.uk:222706 |