Silvério, J and Huang, Y orcid.org/0000-0002-5395-5076 (2023) A Non-parametric Skill Representation with Soft Null Space Projectors for Fast Generalization. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). 2023 IEEE International Conference on Robotics and Automation (ICRA), 29 May - 02 Jun 2023, London, UK. IEEE , pp. 2988-2994. ISBN 979-8-3503-2366-5
Abstract
Over the last two decades, the robotics community witnessed the emergence of various motion representations that have been used extensively, particularly in behavorial cloning, to compactly encode and generalize skills. Among these, probabilistic approaches have earned a relevant place, owing to their encoding of variations, correlations and adaptability to new task conditions. Modulating such primitives, however, is often cumbersome due to the need for parameter re-optimization which frequently entails computationally costly operations. In this paper we derive a non-parametric movement primitive formulation that contains a null space projector. We show that such formulation allows for fast and efficient motion generation and adaptation with computational complexity O(n 2 ) without involving matrix inversions, whose complexity is O(n 3 ). This is achieved by using the null space to track secondary targets, with a precision determined by the training dataset. Using a 2D example associated with time input we show that our non-parametric solution compares favourably with a state-of-the-art parametric approach. For demonstrated skills with high-dimensional inputs we show that it permits on-the-fly adaptation as well.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 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) |
Funding Information: | Funder Grant number EU - European Union 101018395 |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Mar 2023 13:48 |
Last Modified: | 28 Sep 2023 15:29 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICRA48891.2023.10161065 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197022 |