关键词: 双足机器人, 步态规划, 深度确定性策略梯度算法（DDPG）, 径向基函数（RBF）神经网络, SumTree, Gazebo

Abstract:

In order to solve the problem of high-dimensional nonlinear programming in walking control of a multi-degree-of-freedom biped robot, and to tap the autonomous motion potential of the biped robot in an uncertain environment, an improved biped robot step based on the Deep Deterministic Policy Gradient algorithm（DDPG） is proposed. The multi-joint DOF control problem of the two-joint robot is transformed into a non-linear function multi-objective optimization problem and solved by the DDPG algorithm. To solve the problem of slow convergence of the global approximation network solution process, Radial Basis Function（RBF） neural network is used for nonlinear function. The value is calculated and the gradient weighting algorithm is used to update the neural network weights, and SumTree is used to screen the quality samples. The simulation learning training of the biped robot is carried out through the joint simulation platform of ROS, Gazebo and Tensorflow. According to the data simulation, the time after the improved DDPG algorithm reaches the maximum cumulative reward is 45.7%, the success rate is also increased by 8.9%, and the joint posture angle after training is better.

Key words: biped robot, gait planning, Deep Deterministic Policy Gradient（DDPG）, Radial Basis Function（RBF） neural network, SumTree, Gazebo