Efficient and Robust Self-Recovery of Quadruped Robots Using Asymmetric Proximal Policy Optimization

Quadruped robots operating in unpredictable environments face a high risk of falls, which requires robust self-recovery strategies. In this work, we present a deep reinforcement learning approach utilizing Proximal Policy Optimization (PPO) for autonomous self-recovery behavior on the Unitree A1 quadruped robot. Using only proprioceptive sensor data, our approach achieves effective recovery behaviors. The proposed model, trained within the IsaacLab simulation environment, demonstrates effective generalization through sim-to-sim and sim-to-real transfers, successfully recovering from falls even under unseen joint control parameters and carrying unfamiliar payloads. Validation tests in both IsaacLab and Gazebo highlight the robustness and adaptability of our approach to real-world uncertainties. Throughout the operation, the proposed method showed responsive, reliable, and robust performance, highlighting its potential for practical deployment.