LEARNING APPROACH TO STUDY EFFECT OF FLEXIBLE SPINE ON RUNNING BEHAVIOR OF A QUADRUPED ROBOT

Abstract

Effect of flexibility in the spine of a quadruped robot on its energy efficiency and stability is studied. The approach is based on learning parameters of cyclic position command to the actuators. The learning system employs a reinforcement learning method to find the parameters that result in stable running while the velocity-energy ratio is minimized. The learning is done on a simulated system and the final results are tested on a situated robot. The results show that a wider range of parameters results in stable running for softer spine. In addition, robot with harder spine consumes more energy.