Edge-Learning-Enabled Realistic Touch and Stable Communication for Remote Haptic Display

As the basis of Tactile Internet, remote haptic display has been made possible with the development of ultra-reliable low-latency communication in 5G. In this study, edge learning is employed to enable realistic haptic display and stable remote communication. We propose a double-loop control algorithm, which merges decoupling and PID neural network, for magnetic field generation of the electromagnetic haptic device. In addition, a supervised bidirectional LSTM network is constructed for online haptic prediction during remote interaction, thus complementing the missing haptic data on account of time delay and packet loss in network communications. Experiments have been conducted on the built remote haptic display system, where data streams from sensors are gathered, stored, and forwarded in real time. The results show that dynamic and accurate haptic display is achieved through our magnetic field control algorithm for the haptic device, and the error of haptic prediction by step is less than 0.01N. The conclusion is that the service sustainability of remote haptic display can be guaranteed by edge learning effectively.