Fuzzy Q-Learning Based Weight-Lifting Autobalancing Control Strategy for Adult-Sized Humanoid Robots

This paper proposes a control method that improves the ability of adult-sized humanoid robots to adapt to weightlifting situations. In order to achieve the goal of having humanoid robots automatically balance their motion for weight-lifting situations, feedback control is added to the motion control system. The feedback sensors include a three-axis accelerometer and a three-axis gyroscopic, which would be processed by Kalman filter, as well as eight force sensors providing the zero moment point (ZMP) information on the robot. These feedback signals are used as the input of a Fuzzy Q-learning controller, which adjusts the motions to keep the stabilization of the robot. The Fuzzy Q-learning controller consists of two stages: one is the stage of fitting the output weights of each pose in motion patterns, and the second is training the rule-table of the controller. The experiment shows that the controller allows the adult-sized robot to walk stably in weight-lifting situation. Thus, the developed controller indeed keeps the balance of the robot in different situations, which gives the robot the ability to adapt to various environments in the manner of human beings.