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.
@article{arxiv.2506.14116,
title = {Haptic-Based User Authentication for Tele-robotic System},
author = {Rongyu Yu and Kan Chen and Zeyu Deng and Chen Wang and Burak Kizilkaya and Liying Emma Li},
journal= {arXiv preprint arXiv:2506.14116},
year = {2025}
}