Human-in-the-loop Learning and Control for Robot Teleoperation

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Robots have been used in various industrial processes to reduce labor costs and improve work efficiency. However, most robots are only designed to work on repetitive and fixed tasks, leaving a gap with the human desired manufacturing effect. Situations become serious when robots handle complicated tasks in uncontrollable environments due to weak intelligence, inadequate sensing, controlling abilities, and unfit mechanical design. Considering imperfect performance of robotic automation, researchers chose teleoperation technology to add human intelligence to robot manipulation, enabling robots to adapt to complex situations and avoid endangering human lives. Human-in-the-loop Learning and Control for Robot Teleoperation presents recent research progress on teleoperation and robots, including human-robot interaction, learning, and control for teleoperation with many extensions on intelligent learning techniques. This book integrates cutting-edge research on learning and control algorithms of robot teleoperation, neural motor learning control, wave variable enhancement, EMG-based teleoperation control, and other key aspects related to robot technology, presenting in detail the implementation of these control and learning algorithms with adequate application examples and illustrative interpretations.