Aiming at the problems of recognition error and energy consumption of human-computer interaction bionic manipulator,a multi-objective genetic optimization algorithm based on population randomization was proposed.In this algorithm,the influences of terminal error,tracking error and energy consumption in the optimization of human-computer interaction manipulator attitude recognition were considered at the same time;the population randomization method based on parallel selection was adopted,and the adaptive optimal individuals in each generation were inherited to the next generation population to realize the adaptive and positioning functions.In order to verify the effectiveness of this algorithm,an interactive experiment was carried out on a 6 DOF manipulator.The results show that by using the proposed method,not only the energy consumption can be reduced,but also the terminal error can be reduced to less than 3 mm while ensuring the tracking effect of the tracking manipulator;compared with other algorithms,the proposed algorithm has the advantages of less energy consumption and small tracking error,and the accuracy of manipulator attitude adaptation can be effectively improved.