Tenhumberg, Johannes und Mielke, Arman und Bäuml, Berthold (2024) Efficient Learning of Fast Inverse Kinematics with Collision Avoidance. 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/Humanoids57100.2023.10375143. ISBN 979-835030327-8. ISSN 2164-0572.
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Offizielle URL: https://ieeexplore.ieee.org/document/10375143
Kurzfassung
Fast inverse kinematics (IK) is a central component in robotic motion planning. For complex robots, IK methods are often based on root search and nonlinear optimization algorithms. These algorithms can be massively sped up using a neural network to predict a good initial guess, which can then be refined in a few numerical iterations. Besides previous work on learning-based IK, we present a learning approach for the fundamentally more complex problem of IK with collision avoidance. We do this in diverse and previously unseen environments. From a detailed analysis of the IK learning problem, we derive a network and unsupervised learning architecture that removes the need for a sample data generation step. Using the trained network's prediction as an initial guess for a two-stage Jacobian-based solver allows for fast and accurate computation of the collision-free IK. For the humanoid robot, Agile Justin (19 DoF), the collision-free IK is solved in less than 10 ms (on a single CPU core) and with an accuracy of 1×10-4m and 1×10-3 rad based on a high-resolution world model generated from the robot's integrated 3D sensor. Our method massively outperforms a random multi-start baseline in a benchmark with the 19 DoF humanoid and challenging 3D environments. It requires ten times less training time than a supervised training method while achieving comparable results.
elib-URL des Eintrags: | https://elib.dlr.de/202625/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||
Titel: | Efficient Learning of Fast Inverse Kinematics with Collision Avoidance | ||||||||||||||||
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/Humanoids57100.2023.10375143 | ||||||||||||||||
Seitenbereich: | Seiten 1-8 | ||||||||||||||||
Verlag: | IEEE | ||||||||||||||||
ISSN: | 2164-0572 | ||||||||||||||||
ISBN: | 979-835030327-8 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | inverse kinematics | ||||||||||||||||
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 - Autonomie & Geschicklichkeit [RO] | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) | ||||||||||||||||
Hinterlegt von: | Strobl, Dr. Klaus H. | ||||||||||||||||
Hinterlegt am: | 05 Feb 2024 08:48 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 21:02 |
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