Learning Autonomous and Safe Quadruped Traversal of Complex Terrains Using Multi-Layer Elevation Maps

Legged robots hold great promise for agile and flexible mobility across diverse and unstructured terrains, inspired by the remarkable adaptability of bipeds and quadrupeds in nature. However, achieving robust autonomous locomotion in cluttered and complex environments remains a significant challenge. In this work, we present a hierarchical control framework for quadrupedal robots that enables safe and autonomous traversal of cluttered terrains. Central to our approach is a novel multi-layer elevation map representation, which is generalized enough to capture a wide range of terrains. To further improve policy generalization and maneuverability, we incorporate terrain augmentation, knowledge distillation, and carefully designed reward functions. Extensive simulation experiments demonstrate that each component contributes to improved policy generalization, and that our terrain representation is more efficient and informative than existing alternatives. By training a terrain compressor in simulation, we successfully deploy our system on a low-cost quadrupedal robot in real-world environments, showcasing the practicality and robustness of our approach.