Gait synthesis of a hybrid legged robot using reinforcement learning

Abstract

This article is concerned with the gait synthesis problem of a hybrid robot (in this case, a four-legged robot with free wheels on its feet) considering multiple criteria. It is assumed that the position of each leg actuator over time is described by a periodic function with parameters that are determined using the learning automata reinforcement learning algorithm. Analysis of the robot morphology is used to group similar legs and decrease the number of actuator functions that must be determined. MATLAB/Simulink/SimMechanics Toolbox are used to simulate the robot gait. The simulated robot response is evaluated by the reinforcement learning algorithm considering: 1) the robot frontal speed, 2) the "smoothness" of the robot movements, 3) the largest torque required by all leg actuators, and 4) the robot energy consumption. When the reinforcement learning algorithm converges to a good solution, it is applied to the real robot which was built using the Bioloid Comprehensive Kit, an educational robot kit manufactured by ROBOTIS. The responses of the simulated and real robot are then compared and are shown to be similar.