Huang, Y, Abu-Dakka, FJ, Silvério, J et al. (1 more author) (2019) Generalized Orientation Learning in Robot Task Space. In: 2019 International Conference on Robotics and Automation (ICRA). ICRA 2019, 20-24 May 2019, Montreal, Canada. IEEE , pp. 2531-2537.
Abstract
In the context of imitation learning, several approaches have been developed so as to transfer human skills to robots, with demonstrations often represented in Cartesian or joint space. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in many others. However, several crucial issues arising from learning orientations have not been adequately addressed yet. For instance, how can demonstrated orientations be adapted to pass through arbitrary desired points that comprise orientations and angular velocities? In this paper, we propose an approach that is capable of learning multiple orientation trajectories and adapting learned orientation skills to new situations (e.g., via-point and end-point), where both orientation and angular velocity are addressed. Specifically, we introduce a kernelized treatment to alleviate explicit basis functions when learning orientations. Several examples including comparison with the state-of-the-art dynamic movement primitives are provided to verify the effectiveness of our method.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Dec 2019 10:48 |
Last Modified: | 18 Dec 2019 14:56 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICRA.2019.8793540 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154621 |