Haptic-Based User Authentication for Tele-robotic System
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912435594592256 |
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| author | Yu, Rongyu Chen, Kan Deng, Zeyu Wang, Chen Kizilkaya, Burak Li, Liying Emma |
| author_facet | Yu, Rongyu Chen, Kan Deng, Zeyu Wang, Chen Kizilkaya, Burak Li, Liying Emma |
| contents | Tele-operated robots rely on real-time user behavior mapping for remote tasks, but ensuring secure authentication remains a challenge. Traditional methods, such as passwords and static biometrics, are vulnerable to spoofing and replay attacks, particularly in high-stakes, continuous interactions. This paper presents a novel anti-spoofing and anti-replay authentication approach that leverages distinctive user behavioral features extracted from haptic feedback during human-robot interactions. To evaluate our authentication approach, we collected a time-series force feedback dataset from 15 participants performing seven distinct tasks. We then developed a transformer-based deep learning model to extract temporal features from the haptic signals. By analyzing user-specific force dynamics, our method achieves over 90 percent accuracy in both user identification and task classification, demonstrating its potential for enhancing access control and identity assurance in tele-robotic systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14116 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Haptic-Based User Authentication for Tele-robotic System Yu, Rongyu Chen, Kan Deng, Zeyu Wang, Chen Kizilkaya, Burak Li, Liying Emma Robotics Tele-operated robots rely on real-time user behavior mapping for remote tasks, but ensuring secure authentication remains a challenge. Traditional methods, such as passwords and static biometrics, are vulnerable to spoofing and replay attacks, particularly in high-stakes, continuous interactions. This paper presents a novel anti-spoofing and anti-replay authentication approach that leverages distinctive user behavioral features extracted from haptic feedback during human-robot interactions. To evaluate our authentication approach, we collected a time-series force feedback dataset from 15 participants performing seven distinct tasks. We then developed a transformer-based deep learning model to extract temporal features from the haptic signals. By analyzing user-specific force dynamics, our method achieves over 90 percent accuracy in both user identification and task classification, demonstrating its potential for enhancing access control and identity assurance in tele-robotic systems. |
| title | Haptic-Based User Authentication for Tele-robotic System |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.14116 |