Enhancing Robustness of Locomotion Policy for Quadrupedal Robot with Deep Disturbance Observer

Abstract

This letter proposes a control framework to enhance the robustness of a locomotion policy against uncertainties by integrating it with a deep disturbance observer (DOB) network and a deep state estimator network. The deep DOB approximates the inverse model of a quadrupedal robot. The locomotion policy is trained to produce optimal actions, with the deep DOB estimating the overall uncertainties of the robot, and the deep state estimator estimates the body's linear velocities. All networks are trained under nominal conditions in Isaac Gym. Subsequently, all the trained networks are transferred to Gazebo and a real robot with ROS2 are used to validate their robustness under uncertain conditions without additional tuning. Furthermore, validation results show that the proposed control framework performs best in velocity tracking compared to the baseline method in terms of lowest estimation errors. This emphasizes the effectiveness of the proposed control framework in improving robustness of the locomotion policy. Videos on Isaac Gym and Gazebo simulation, and real robot experiment are available at Project page: bit.ly/3CF3OTQ.