ControlTac: Force- and Position-Controlled Tactile Data Augmentation with a Single Reference Image
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915308309053440 |
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| author | Luo, Dongyu Yu, Kelin Shahidzadeh, Amir-Hossein Fermüller, Cornelia Aloimonos, Yiannis Gao, Ruohan |
| author_facet | Luo, Dongyu Yu, Kelin Shahidzadeh, Amir-Hossein Fermüller, Cornelia Aloimonos, Yiannis Gao, Ruohan |
| contents | Vision-based tactile sensing has been widely used in perception, reconstruction, and robotic manipulation. However, collecting large-scale tactile data remains costly due to the localized nature of sensor-object interactions and inconsistencies across sensor instances. Existing approaches to scaling tactile data, such as simulation and free-form tactile generation, often suffer from unrealistic output and poor transferability to downstream tasks. To address this, we propose ControlTac, a two-stage controllable framework that generates realistic tactile images conditioned on a single reference tactile image, contact force, and contact position. With those physical priors as control input, ControlTac generates physically plausible and varied tactile images that can be used for effective data augmentation. Through experiments on three downstream tasks, we demonstrate that ControlTac can effectively augment tactile datasets and lead to consistent gains. Our three real-world experiments further validate the practical utility of our approach. Project page: https://dongyuluo.github.io/controltac. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_20498 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ControlTac: Force- and Position-Controlled Tactile Data Augmentation with a Single Reference Image Luo, Dongyu Yu, Kelin Shahidzadeh, Amir-Hossein Fermüller, Cornelia Aloimonos, Yiannis Gao, Ruohan Computer Vision and Pattern Recognition Machine Learning Robotics Vision-based tactile sensing has been widely used in perception, reconstruction, and robotic manipulation. However, collecting large-scale tactile data remains costly due to the localized nature of sensor-object interactions and inconsistencies across sensor instances. Existing approaches to scaling tactile data, such as simulation and free-form tactile generation, often suffer from unrealistic output and poor transferability to downstream tasks. To address this, we propose ControlTac, a two-stage controllable framework that generates realistic tactile images conditioned on a single reference tactile image, contact force, and contact position. With those physical priors as control input, ControlTac generates physically plausible and varied tactile images that can be used for effective data augmentation. Through experiments on three downstream tasks, we demonstrate that ControlTac can effectively augment tactile datasets and lead to consistent gains. Our three real-world experiments further validate the practical utility of our approach. Project page: https://dongyuluo.github.io/controltac. |
| title | ControlTac: Force- and Position-Controlled Tactile Data Augmentation with a Single Reference Image |
| topic | Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2505.20498 |