Vision-based Tactile Image Generation via Contact Condition-guided Diffusion Model
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| 发表在: | arXiv.org (Dec 2, 2024), p. n/a |
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| 主要作者: | |
| 其他作者: | , , , , , , |
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Cornell University Library, arXiv.org
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| 在线阅读: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3138992165 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3138992165 | ||
| 045 | 0 | |b d20241202 | |
| 100 | 1 | |a Lin, Xi | |
| 245 | 1 | |a Vision-based Tactile Image Generation via Contact Condition-guided Diffusion Model | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 2, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Vision-based tactile sensors, through high-resolution optical measurements, can effectively perceive the geometric shape of objects and the force information during the contact process, thus helping robots acquire higher-dimensional tactile data. Vision-based tactile sensor simulation supports the acquisition and understanding of tactile information without physical sensors by accurately capturing and analyzing contact behavior and physical properties. However, the complexity of contact dynamics and lighting modeling limits the accurate reproduction of real sensor responses in simulations, making it difficult to meet the needs of different sensor setups and affecting the reliability and effectiveness of strategy transfer to practical applications. In this letter, we propose a contact-condition guided diffusion model that maps RGB images of objects and contact force data to high-fidelity, detail-rich vision-based tactile sensor images. Evaluations show that the three-channel tactile images generated by this method achieve a 60.58% reduction in mean squared error and a 38.1% reduction in marker displacement error compared to existing approaches based on lighting model and mechanical model, validating the effectiveness of our approach. The method is successfully applied to various types of tactile vision sensors and can effectively generate corresponding tactile images under complex loads. Additionally, it demonstrates outstanding reconstruction of fine texture features of objects in a Montessori tactile board texture generation task. | |
| 653 | |a Image resolution | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Color imagery | ||
| 653 | |a Sensors | ||
| 653 | |a Effectiveness | ||
| 653 | |a Image acquisition | ||
| 653 | |a Optical data processing | ||
| 653 | |a Physical properties | ||
| 653 | |a Error reduction | ||
| 653 | |a Complexity | ||
| 653 | |a Tactile sensors (robotics) | ||
| 653 | |a Optical measurement | ||
| 653 | |a Image processing | ||
| 653 | |a Dimensional analysis | ||
| 653 | |a Lighting | ||
| 653 | |a Contact force | ||
| 653 | |a Texture | ||
| 653 | |a Optical properties | ||
| 700 | 1 | |a Xu, Weiliang | |
| 700 | 1 | |a Mao, Yixian | |
| 700 | 1 | |a Wang, Jing | |
| 700 | 1 | |a Lv, Meixuan | |
| 700 | 1 | |a Liu, Lu | |
| 700 | 1 | |a Luo, Xihui | |
| 700 | 1 | |a Li, Xinming | |
| 773 | 0 | |t arXiv.org |g (Dec 2, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3138992165/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.01639 |