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Lukas Zeh

Herr M.Sc.

Wissenschaftlicher Mitarbeiter "Antriebssysteme und -regelung"
Institut für Steuerungstechnik der Werkzeugmaschinen und Fertigungseinrichtungen

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+49 711 685 84511
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Seidenstr. 36
70174 Stuttgart
Deutschland
Raum: 5.088

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Veröffentlichungen:

Zeh, Lukas ; Meiwaldt, Johannes ; Zhou, Zexu ; Lechler, Armin ; Verl, Alexander: Manipulation of Deformable Linear Objects Using Model Predictive Path Integral Control with Bidirectional Long Short-Term Memory Learning. In: Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics : SCITEPRESS - Science and Technology Publications, 2025 — ISBN 978-989-758-770-2, S. 47–58
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BibTeX
@inproceedings{Zeh_2025, abstract = {The manipulation of Deformable Linear Objects (DLOs) such as cables poses a significant challenge for automation due to their infinite degrees of freedom and non-linear dynamics. In this paper we present a machine learning based optimal control approach for the manipulation of DLOs. This approach is divided into two main components: modeling and control. For modeling the dynamics of the DLO, we propose a learning based approach using a bidirectional Long Short-Term Memory (biLSTM) network. The biLSTM network is trained on synthetic data generated by the MuJoCo physics engine. For manipulating the DLO, a model predictive control strategy that employs Model Predictive Path Integral (MPPI) control is selected. The proposed approach is evaluated through simulation and experiments. The results demonstrate the effectiveness of the proposed method in achieving accurate and efficient manipulation of DLOs.}, author = {Zeh, Lukas and Meiwaldt, Johannes and Zhou, Zexu and Lechler, Armin and Verl, Alexander}, booktitle = {Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics}, doi = {10.5220/0013703800003982}, isbn = {978-989-758-770-2}, issn = {2184-280}, language = {English}, month = {10}, organization = {INSTICC}, pages = {47–58}, publisher = {SCITEPRESS - Science and Technology Publications}, title = {Manipulation of Deformable Linear Objects Using Model Predictive Path Integral Control with Bidirectional Long Short-Term Memory Learning}, url = {http://dx.doi.org/10.5220/0013703800003982}, year = 2025 }
Link
http://dx.doi.org/10.5220/0013703800003982
Ouyang, Shengzhe ; Zürn, Manuel ; Zeh, Lukas ; Lechler, Armin ; Verl, Alexander: Topology Prediction of Branched Deformable Linear Objects Using Deep Learning. In: IEEE Access Bd. 12 (2024), S. 194399–194411
- BibTeX
BibTeX
@article{10804119, abstract = {Automated wire harness handling can improve production efficiency, increase quality, and reduce assembly costs. However, due to deformation, there are an infinite number of possible wire harness configurations, making wire harness perception a challenge. Deep learning is a popular method for computer vision but lacks datasets, models, and experiments for wire harness perception. Therefore, this paper presents a novel deep learning model to predict the configuration of a wire harness using artificially generated datasets mixed with real annotated data. The model predicts keypoints which are interpolated as cubic splines to represent the wire harness configuration with reduced degrees of freedom. We benchmark our novel model against YOLOv8-Pose and experiment with different possibilities for predicting the wire harness. As a result, our proposed approach achieves mAP@50-95 of 89.8%, which could further be integrated into robotic systems to improve the automation and precision of robotic wire harness handling.}, author = {Ouyang, Shengzhe and Zürn, Manuel and Zeh, Lukas and Lechler, Armin and Verl, Alexander}, doi = {10.1109/ACCESS.2024.3518634}, issn = {2169-3536}, journal = {IEEE Access}, pages = {194399-194411}, title = {Topology Prediction of Branched Deformable Linear Objects Using Deep Learning}, volume = 12, year = 2024 }

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