Feasibility study of personalized speed adaptation method based on mental state for teleoperated robots

• Published in: Frontiers in neuroscience Vol. 16; p. 976437
• Main Authors: Zhang, Teng, Zhang, Xiaodong, Lu, Zhufeng, Zhang, Yi, Jiang, Zhiming, Zhang, Yingjie
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 02.09.2022; Frontiers Media S.A
• Subjects: Adaptation; Automation; biosignals; Decision making; EEG; EEG and EOG; Electroencephalography; Feasibility studies; Feedback; Human error; Interactive learning; mental state; Neuroscience; personalized; Physiology; Reinforcement; reinforcement learning; Robots; teleoperated robots; Workloads
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2022.976437

The teleoperated robotic system can support humans to complete tasks in high-risk, high-precision and difficult special environments. Because this kind of special working environment is easy to cause stress, high mental workload, fatigue and other mental states of the operator, which will reduce the quality of operation and even cause safety accidents, so the mental state of the people in this system has received extensive attention. However, the existence of individual differences and mental state diversity is often ignored, so that most of the existing adjustment strategy is out of a match between mental state and adaptive decision, which cannot effectively improve operational quality and safety. Therefore, a personalized speed adaptation (PSA) method based on policy gradient reinforcement learning was proposed in this paper. It can use electroencephalogram and electro-oculogram to accurately perceive the operator’s mental state, and adjust the speed of the robot individually according to the mental state of different operators, in order to perform teleoperation tasks efficiently and safely. The experimental results showed that the PSA method learns the mapping between the mental state and the robot’s speed regulation action by means of rewards and punishments, and can adjust the speed of the robot individually according to the mental state of different operators, thereby improving the operating quality of the system. And the feasibility and superiority of this method were proved. It is worth noting that the PSA method was validated on 6 real subjects rather than a simulation model. To the best of our knowledge, the PSA method is the first implementation of online reinforcement learning control of teleoperated robots involving human subjects.