GIMloco: Generic Internal Model-Based Locomotion for Quadruped Robots

Abstract

A central challenge in robust quadruped locomotion, which relies solely on proprioceptive information, is how to effectively encode the history of observations. While current methods, such as regression, struggle with high-dimensional multi-time-step histories, and Temporal Convolutional Networks (TCNs) incur computational overhead, we propose a more efficient and theoretically grounded alternative. Inspired by the Generic Internal Model (GIM) from control theory, we introduce GIMloco, which maps the history of proprioceptive observations into a compact and stable internal model space through a predesigned first-order integral system with stability and orthogonality guarantees. This encoded representation drives three downstream tasks: state estimation, latent variable learning, and control policy learning. Our experiments show that GIMloco outperforms strong baselines in velocity tracking, system overshoot, response speed. Furthermore, it can navigate more complex terrains while also demonstrating better training stability across random seeds. Crucially, our method reduces training time by two orders of magnitude compared to TCN-based approaches. Our work presents GIMloco as a robust and computationally efficient framework for locomotion based on proprioceptive information.