Push Recovery Control for Humanoid Robot using Reinforcement Learning

A humanoid robot similar to a human is structurally unstable, so the push recovery control is essential. The proposed push recovery controller consists of a IMU sensor part, a high-level push recovery controller and a low-level push recovery controller. The IMU sensor part measures the linear velocity and angular velocity and transmits it to the high-level push recovery controller. The high-level push recovery controller selects the strategy of the low-level push recovery controller based on the stability region. The stability region is improved using the DQN(Deep Q-Network) of the reinforcement learning method. The low-level push recovery controller consists of a ankle, hip and step strategies. Each strategy is analyzed using LIPM(Linear Inverted Pendulum Model). Based on the analysis, the actuators corresponding to each strategy are controlled.