Brain-computer cooperation digital twinning reinforcement learning control method and system

Abstract

The invention discloses a brain-computer cooperation digital twinning reinforcement learning control method and system. A brain-computer cooperation control model is constructed, an operator gives a virtual robot direction instruction, meanwhile, an electroencephalogram signal generated when the operator gives the virtual robot direction instruction is collected, a corresponding speed instructionof a virtual robot is given according to the collected electroencephalogram signal to complete a specified action, reward value calculation is performed on the brain-computer cooperation control modelaccording to the completion quality, training of the brain-computer cooperation control model is completed, a double-loop information interaction mechanism between the brain and the computer is realized through a brain-computer cooperation digital twinning environment in a reinforced learning manner, and interaction of an information layer and an instruction layer between the brain and the computer is realized. According to the method and the system, the brain state of the operator is detected through the electroencephalogram signals, compensation control is conducted on the instruction of the robot according to the brain state of the operator, accurate control is achieved, and compared with other brain-computer cooperation methods, the method has the advantages that the robustness and generalization ability are improved, and mutual adaptation and mutual growth between the brain and the computers are achieved.

Description

technical field [0001] The invention belongs to the field of brain-computer interface and artificial intelligence comprehensive technology, and relates to a brain-computer cooperative digital twin reinforcement learning control method and system. Background technique [0002] With the development of robotics technology, there is an increasingly urgent demand for intelligent robots with human-like advanced perception and cognition capabilities and the ability to perform non-set tasks in highly complex environments. However, to achieve intelligent robots with human thinking and reasoning methods, autonomous discovery and feature extraction, online incremental learning, and comprehensive processing of various information capabilities only with artificial intelligence technology, the current technology is still not up to it. Utilizing the fusion of human-machine intelligence and giving full play to the advantages of different intelligences of humans and computers is an important...

AI Technical Summary

Problems solved by technology

[0003] After research, it is found that there are still two problems in the field of precision control: (1) the lack of two-way interaction of information between the operator and the robot, and the inability to realize the precise perception of the operator's intention; (2) due to the distraction of the human brain , mental fatigue and excessive mental load, etc., resulting in poor performance of the brain-computer hybrid intelligent system, and even danger

In view of the deterioration of the performance of the brain-computer system caused by the mental state of the human brain, currently only the one-way compensation of the control command is considered, and there is a lack of a dual-loop interaction mechanism between the brain and the computer.

To sum up, for the "human-in-the-loop" brain-computer collaborative control method, there is still no integrated brain-computer collaboration model established at this stage, and it is impossible to effectively realize the deep integration of the information layer and the command layer, and the brain-computer collaborative control. Accuracy, stability and safety need to be improved

Images