A leader-follower game framing with two neural networks predicts next-step haptic signals, reporting 80-95% human-side and 70-90% robot-side accuracy, with a Taylor loss bound presented as a robustness guarantee.
Signal Prediction for Loss Mitigation in Tactile Internet: A Leader-Follower Game-Theoretic Approach
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abstract
Tactile Internet (TI) requires achieving ultra-low latency and highly reliable packet delivery for haptic signals. In the presence of packet loss and delay, the signal prediction method provides a viable solution for recovering the missing signals. To this end, we introduce the Leader-Follower (LeFo) approach based on a cooperative Stackelberg game, which enables both users and robots to learn and predict actions. With accurate prediction, the teleoperation system can safely relax its strict delay requirements. Our method achieves high prediction accuracy, ranging from 80.62% to 95.03% for remote robot signals at the Human ($H$) side and from 70.44% to 89.77% for human operation signals at the remote Robot ($R$) side. We also establish an upper bound for maximum signal loss using Taylor Expansion, ensuring robustness.
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Signal Prediction for Loss Mitigation in Tactile Internet: A Leader-Follower Game-Theoretic Approach
A leader-follower game framing with two neural networks predicts next-step haptic signals, reporting 80-95% human-side and 70-90% robot-side accuracy, with a Taylor loss bound presented as a robustness guarantee.