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Efficient Learning of Fast Inverse Kinematics with Collision Avoidance

Tenhumberg, Johannes and Mielke, Arman and Bäuml, Berthold (2024) Efficient Learning of Fast Inverse Kinematics with Collision Avoidance. In: 22nd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2023, pp. 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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Official URL: https://ieeexplore.ieee.org/document/10375143


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.

Item URL in elib:https://elib.dlr.de/202625/
Document Type:Conference or Workshop Item (Speech)
Title:Efficient Learning of Fast Inverse Kinematics with Collision Avoidance
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Tenhumberg, JohannesUNSPECIFIEDhttps://orcid.org/0000-0002-5090-1259UNSPECIFIED
Bäuml, BertholdUNSPECIFIEDhttps://orcid.org/0000-0002-4545-4765UNSPECIFIED
Date:1 January 2024
Journal or Publication Title:22nd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2023
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-8
Keywords:inverse kinematics
Event Title:2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids)
Event Location:Austin, TX, USA
Event Type:international Conference
Event Start Date:12 December 2023
Event End Date:14 December 2023
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Autonomy & Dexterity [RO]
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Deposited By: Strobl, Dr. Klaus H.
Deposited On:05 Feb 2024 08:48
Last Modified:24 Apr 2024 21:02

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