Our team is using reinforcement learning to train a motion generation policy that controls a G1 humanoid robot. This policy allows a user to prompt the robot to walk or run mimicking a human reference by joystick command. This method creates a real-time, lightweight control policy that bypasses the need for torque level robotic control.