PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robots

TL;DR

This paper introduces a multi-encoder privileged learning framework with a residual policy network to improve the robustness and reliability of quadruped robot locomotion under external disturbances, outperforming existing methods.

Contribution

It proposes a novel multi-encoder privileged learning approach combined with a residual policy network to enhance disturbance robustness in quadrupedal locomotion.

Findings

Enhanced robustness and stability in diverse terrains.

Superior performance over state-of-the-art algorithms.

Validated on a real quadruped robot with extensive experiments.

Abstract

Numerous locomotion controllers have been designed based on Reinforcement Learning (RL) to facilitate blind quadrupedal locomotion traversing challenging terrains. Nevertheless, locomotion control is still a challenging task for quadruped robots traversing diverse terrains amidst unforeseen disturbances. Recently, privileged learning has been employed to learn reliable and robust quadrupedal locomotion over various terrains based on a teacher-student architecture. However, its one-encoder structure is not adequate in addressing external force perturbations. The student policy would experience inevitable performance degradation due to the feature embedding discrepancy between the feature encoder of the teacher policy and the one of the student policy. Hence, this paper presents a privileged learning framework with multiple feature encoders and a residual policy network for robust and reliable quadruped locomotion subject to various external perturbations. The multi-encoder structure can decouple latent features from different privileged information, ultimately leading to enhanced performance of the learned policy in terms of robustness, stability, and reliability. The efficiency of the proposed feature encoding module is analyzed in depth using extensive simulation data. The introduction of the residual policy network helps mitigate the performance degradation experienced by the student policy that attempts to clone the behaviors of a teacher policy. The proposed framework is evaluated on a Unitree GO1 robot, showcasing its performance enhancement over the state-of-the-art privileged learning algorithm through extensive experiments conducted on diverse terrains. Ablation studies are conducted to illustrate the efficiency of the residual policy network.