Lehner, Peter und Roa Garzon, Máximo Alejandro und Albu-Schäffer, Alin Olimpiu (2022) Kinematic transfer learning of sampling distributions for manipulator motion planning. In: 39th IEEE International Conference on Robotics and Automation, ICRA 2022. IEEE. 2022 IEEE International Conference on Robotics and Automation, 2022-05-23 - 2022-05-27, Philadelphia, USA. doi: 10.1109/ICRA46639.2022.9811915. ISBN 978-172819681-7. ISSN 1050-4729.
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Offizielle URL: https://ieeexplore.ieee.org/document/9811915
Kurzfassung
Recent research has shown that guiding sampling-based planners with sampling distributions, learned from previous experiences via density estimation, can significantly decrease computation times for motion planning. We propose an algorithm that can estimate the density from the experiences of a robot with different kinematic structure, on the same task. The method allows to generalize collected data from one source manipulator to similarly designed target manipulators, significantly reducing the computation time for new queries for the target manipulator. We evaluate the algorithm in two experiments, including a constrained manipulation task with five different collaborative robots, and show that transferring information can significantly decrease planning time.
elib-URL des Eintrags: | https://elib.dlr.de/186868/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag, Poster) | ||||||||||||||||
Titel: | Kinematic transfer learning of sampling distributions for manipulator motion planning | ||||||||||||||||
Autoren: |
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Datum: | Mai 2022 | ||||||||||||||||
Erschienen in: | 39th IEEE International Conference on Robotics and Automation, ICRA 2022 | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||
DOI: | 10.1109/ICRA46639.2022.9811915 | ||||||||||||||||
Verlag: | IEEE | ||||||||||||||||
ISSN: | 1050-4729 | ||||||||||||||||
ISBN: | 978-172819681-7 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | robotics motion-planning transfer-learning | ||||||||||||||||
Veranstaltungstitel: | 2022 IEEE International Conference on Robotics and Automation | ||||||||||||||||
Veranstaltungsort: | Philadelphia, USA | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 23 Mai 2022 | ||||||||||||||||
Veranstaltungsende: | 27 Mai 2022 | ||||||||||||||||
Veranstalter : | IEEE Robotics and Automation Society | ||||||||||||||||
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 - Terrestrische Assistenz-Robotik | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) | ||||||||||||||||
Hinterlegt von: | Lehner, Peter | ||||||||||||||||
Hinterlegt am: | 13 Jul 2022 12:27 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:48 |
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