Centralized curiosity model-based synchronization control for robotics teleoperator under input saturation and time delay

This paper investigates the state synchronization control problem of a teleoperation system for actuators based on deep reinforcement learning, taking into account input saturation, uncertainties, and stochastic delays in the actuators. A centralized curiosity model multi-agent reinforcement learning algorithm framework is proposed for the teleoperation system composed of a local robotic manipulator and a remote robotic manipulator. Specifically, a centralized world model and a centralized curiosity network are utilized to train the actor network. Subsequently, the robotic manipulator is controlled using traditional controller tuning methods. To learn the nonlinear communication delay perturbations in the system, a Long Short-Term Memory network is applied to learn new features. Based on these features, the state structure of the partially observable Markov model is reconstructed. In simulation experiments compared with traditional controllers and baseline reinforcement learning algorithms, the proposed method demonstrates competitive control performance.