Stable Walking Pattern Generation for a Biped Robot Using Reinforcement Learning

In this research, a stable biped walking pattern is generated by using reinforcement learning. The biped walking pattern for forward direction is chosen as a simple third order polynomial and sinusoidal function is used for sideway direction. To complete the forward walking pattern, four boundary conditions are needed. In order to avoid jerk motion, initial position and velocity and final position and velocity of the joint are selected as boundary conditions. Also desired motion or posture can be achieved by using the initial and final position. The final velocity of the walking pattern is related to the stability but it is hard to choose proper value. So the final velocity of the walking pattern is chosen as a learning parameter. In order to find the proper boundary condition value, a reinforcement learning algorithm is used. For the sideway movement, a sway amount is selected as learning parameter and a reinforcement learning agent finds proper value for sideway movement. To test the algorithm, a three-dimensional simulator that takes into consideration the whole model of the robot and the environment is developed. The algorithm is verified through a simulation.