A strategy for push recovery in quadruped robot based on reinforcement learning

Abstract

In this paper, a strategy for push recovery in quadruped robot based on reinforcement learning(RL) is proposed. At first, this strategy makes use of the simplified model of quadruped robot to reduce the dimensions of the action and state space for the RL framework, then it enhance the efficiency of the arithmetic by using the prior knowledge provided by the simplified model. Through learning process, this strategy can provide a foot placement estimate to the quadruped robot to restore balance while being pushed. By compared with the traditional arithmetic on a united simulation platform, we prove that this arithmetic is available, and can converge at the result quickly.