Self-Recovery of Quadrupedal Robot Using Deep Reinforcement Learning

Quadruped robots are commonly used for navigating difficult terrains. However, they remain vulnerable to falls in unpredictable and unstructured environments. To ensure continuous operation, a self-recovery behavior is necessary. In this paper we present a self-recovery mechanism using Proximal Policy Optimization. This method eliminates the need for complex kinematic calculations. The proposed approach was trained and tested on the UnitreeA1 quadruped robot within the simulation environment to achieve effective self-recovery behavior. The results demonstrate the potential of this method as a reliable solution for self-recovery in quadruped robots.