Yan, Yashuai und Mascaro, Esteve Valls und Lee, Dongheui (2024) ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space. In: 22nd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2023, Seiten 1-8. IEEE. 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), 2023-12-12 - 2023-12-14, Austin, TX, USA. doi: 10.1109/Humanoids57100.2023.10375150. ISBN 979-835030327-8. ISSN 2164-0572.
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Offizielle URL: https://ieeexplore.ieee.org/document/10375150
Kurzfassung
This paper introduces a novel deep-learning approach for human-to-robot motion retargeting, enabling robots to mimic human poses accurately. Contrary to prior deep-learning-based works, our method does not require paired human-to-robot data, which facilitates its translation to new robots. First, we construct a shared latent space between humans and robots via adaptive contrastive learning that takes advantage of a proposed cross-domain similarity metric between the human and robot poses. Additionally, we propose a consistency term to build a common latent space that captures the similarity of the poses with precision while allowing direct robot motion control from the latent space. For instance, we can generate in-between motion through simple linear interpolation between two projected human poses. We conduct a comprehensive evaluation of robot control from diverse modalities (i.e., texts, RGB videos, and key poses), which facilitates robot control for non-expert users. Our model outperforms existing works regarding human-to-robot retargeting in terms of efficiency and precision. Finally, we implemented our method in a real robot with self-collision avoidance through a whole-body controller to showcase the effectiveness of our approach.
elib-URL des Eintrags: | https://elib.dlr.de/202150/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||
Titel: | ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space | ||||||||||||||||
Autoren: |
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Datum: | 1 Januar 2024 | ||||||||||||||||
Erschienen in: | 22nd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2023 | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Nein | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
DOI: | 10.1109/Humanoids57100.2023.10375150 | ||||||||||||||||
Seitenbereich: | Seiten 1-8 | ||||||||||||||||
Verlag: | IEEE | ||||||||||||||||
ISSN: | 2164-0572 | ||||||||||||||||
ISBN: | 979-835030327-8 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | motion retargeting | ||||||||||||||||
Veranstaltungstitel: | 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids) | ||||||||||||||||
Veranstaltungsort: | Austin, TX, USA | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 12 Dezember 2023 | ||||||||||||||||
Veranstaltungsende: | 14 Dezember 2023 | ||||||||||||||||
Veranstalter : | IEEE-RAS | ||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||
HGF - Programmthema: | Robotik | ||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||
DLR - Forschungsgebiet: | R RO - Robotik | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Basistechnologien [RO] | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) | ||||||||||||||||
Hinterlegt von: | Strobl, Dr. Klaus H. | ||||||||||||||||
Hinterlegt am: | 23 Jan 2024 15:22 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 21:02 |
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