Learning User Interaction Forces using Vision for a Soft Finger Exosuit
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908478690295808 |
|---|---|
| author | Refai, Mohamed Irfan Alkayas, Abdulaziz Y. Mathew, Anup Teejo Renda, Federico Thuruthel, Thomas George |
| author_facet | Refai, Mohamed Irfan Alkayas, Abdulaziz Y. Mathew, Anup Teejo Renda, Federico Thuruthel, Thomas George |
| contents | Wearable assistive devices are increasingly becoming softer. Modelling their interface with human tissue is necessary to capture transmission of dynamic assistance. However, their nonlinear and compliant nature makes both physical modeling and embedded sensing challenging. In this paper, we develop a image-based, learning-based framework to estimate distributed contact forces for a finger-exosuit system. We used the SoRoSim toolbox to generate a diverse dataset of exosuit geometries and actuation scenarios for training. The method accurately estimated interaction forces across multiple contact locations from low-resolution grayscale images, was able to generalize to unseen shapes and actuation levels, and remained robust under visual noise and contrast variations. We integrated the model into a feedback controller, and found that the vision-based estimator functions as a surrogate force sensor for closed-loop control. This approach could be used as a non-intrusive alternative for real-time force estimation for exosuits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02870 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning User Interaction Forces using Vision for a Soft Finger Exosuit Refai, Mohamed Irfan Alkayas, Abdulaziz Y. Mathew, Anup Teejo Renda, Federico Thuruthel, Thomas George Robotics Wearable assistive devices are increasingly becoming softer. Modelling their interface with human tissue is necessary to capture transmission of dynamic assistance. However, their nonlinear and compliant nature makes both physical modeling and embedded sensing challenging. In this paper, we develop a image-based, learning-based framework to estimate distributed contact forces for a finger-exosuit system. We used the SoRoSim toolbox to generate a diverse dataset of exosuit geometries and actuation scenarios for training. The method accurately estimated interaction forces across multiple contact locations from low-resolution grayscale images, was able to generalize to unseen shapes and actuation levels, and remained robust under visual noise and contrast variations. We integrated the model into a feedback controller, and found that the vision-based estimator functions as a surrogate force sensor for closed-loop control. This approach could be used as a non-intrusive alternative for real-time force estimation for exosuits. |
| title | Learning User Interaction Forces using Vision for a Soft Finger Exosuit |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.02870 |