Robots with telepresence capabilities are typically employed for tasks where human presence is not feasible due to geography, safety risks like fire or radiation exposure, or other factors like any epidemic disease. Time delay is a significant consideration in controlling a telepresence robot. This study proposes a deep learning‐based approach to compensate for the delay by predicting the behaviour of the teleoperator. The authors integrate a recurrent neural network (RNN) based on the Long Short‐Term Memory (LSTM) architecture with the reinforcement learning‐based Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed method predicts the teleoperator's angular and linear controlling commands by using data gathered by embedded sensors on the specially designed and built telepresence robot. Simulations and experiments assess the operation of the proposed technique in Gazebo simulation and MATLAB with robot operating system (ROS) integration, which shows 2.3% better response in the presence of static.

Robots with telepresence capabilities are typically employed for tasks where human presence is not feasible due to geography, safety risks like fire or radiation exposure, or other factors like any epidemic disease. Time delay is a significant consideration in controlling a telepresence robot. This study proposes a deep learning-based approach to compensate for the delay by predicting the behaviour of the teleoperator. We integrate a recurrent neural network (RNN) based on the Long Short-Term Memory (LSTM) architecture with the reinforcement learning-based Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed method predicts the teleoperator’s angular and linear controlling commands by using data gathered by embedded sensors on the specially designed and built telepresence robot. Simulations and experiments assess the operation of the proposed technique in Gazebo simulation and MATLAB with ROS integration, which shows 2.3% better response in the presence of static and dynamic obstacles.