A Gait Control Method for Bipedal Robots Based on Reward Function Optimization

The intelligent control of walking of humanoid robots is one of the key research directions in the field of robotics research, but the traditional motion model's constraint on the center of mass makes it difficult for robots to maintain walking stability and simulate human gait at the same time for its excessive attention on stability. Compared with the traditional deep reinforcement learning algorithm, the DDPG algorithm has the ability to handle continuous action space and high-dimensional state space, and has a wide application prospect in many practical problems. We proposed a bipedal robot control method based on reward function optimization to enable the robot to achieve both stable high-speed movement and human gait imitation. This paper combines the physical characteristics of the bipedal robot to establish a control system based on the DDPG algorithm. At the same time, the reward function is designed to guide the robot to learn the correct walking strategy. Through the comparative test, the weight limit ratio of each reward function for the bipedal robot to stably increase the speed is given. The simulation results show that the method proposed in this paper has good practicality and effectiveness.