Abstract
This study explores a method for the dynamic modeling of soft robots, focusing on enhancing the deep learning-based Lagrangian modeling approach through the attention mechanism, which enriches the training process by allocating focused attention and analytical weighting to critical state features, thereby increasing the model's sensitivity to changes in the robot's state. We compared our method through simulation, demonstrating that the model is effective in long-term prediction and noise rejection.
Original language | English |
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Title of host publication | Proceedings of the 3rd Conference on Fully Actuated System Theory and Applications, FASTA 2024 |
Publisher | IEEE |
Pages | 200-207 |
Number of pages | 8 |
ISBN (Electronic) | 979-8-3503-7369-1 |
DOIs | |
Publication status | Published - 2024 |
Event | 3rd Conference on Fully Actuated System Theory and Applications, FASTA 2024 - Shenzhen, China Duration: 10 May 2024 → 12 May 2024 |
Conference
Conference | 3rd Conference on Fully Actuated System Theory and Applications, FASTA 2024 |
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Country/Territory | China |
City | Shenzhen |
Period | 10/05/24 → 12/05/24 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Attention-enhanced deep learning
- Lagrangian neural networks
- Machine learning in robotics
- Predictive modeling
- Soft robots